diff --git a/dashboard/backend/api/auth.py b/dashboard/backend/api/auth.py
index c6ed9c65..eb6d4c6a 100644
--- a/dashboard/backend/api/auth.py
+++ b/dashboard/backend/api/auth.py
@@ -436,17 +436,60 @@ def _store_unavailable(exc: BaseException, *, route: str) -> HTTPException:
     return HTTPException(status_code=503, detail=_STORE_UNAVAILABLE_DETAIL)
 
 
+def _local_autologin_user(request: Request) -> Optional[dict]:
+    """Local-dev convenience (never active in CI or production).
+
+    When ``ATL_LOCAL_AUTOLOGIN_EMAIL`` is set AND the request originates from
+    loopback, resolve that seeded admin account without a session cookie —
+    so a locally-run console opens pre-authenticated for verification. The
+    env var exists only in the local launchd plist; the loopback check keeps
+    a leaked flag from ever authenticating a network peer, and the role check
+    means a misconfigured email silently disables the feature instead of
+    elevating a stranger.
+    """
+    email = (os.getenv("ATL_LOCAL_AUTOLOGIN_EMAIL") or "").strip()
+    if not email:
+        return None
+    client = request.client.host if request.client else ""
+    if client not in {"127.0.0.1", "::1"}:
+        return None
+    try:
+        user = users_module.user_store.get_user_by_email(email)
+    except _USER_STORE_OUTAGE:
+        return None
+    if not user or user.get("role") != "admin":
+        return None
+    enriched = dict(user)
+    if user.get("id") is not None:
+        try:
+            enriched["entitlements"] = users_module.user_store.get_entitlements(user["id"])
+        except Exception:
+            pass
+    return enriched
+
+
 def get_current_user(
     request: Request,
     authorization: Optional[str] = Header(default=None),
 ) -> dict:
     token = _session_token(request, authorization)
+    user = None
+    if token:
+        try:
+            user = users_module.user_store.get_user_for_token(token)
+        except _USER_STORE_OUTAGE as exc:
+            raise _store_unavailable(exc, route="get_current_user") from None
+    if not user:
+        # The local-console fallback covers BOTH shapes of "no live session":
+        # no cookie at all, and a cookie whose session died (wiped dev DB,
+        # expired row) — the second shape is what a stale browser cookie
+        # produces after the database is swapped, and it must land on the
+        # seeded admin instead of a 401 nothing on the page explains.
+        autologin = _local_autologin_user(request)
+        if autologin is not None:
+            return autologin
     if not token:
         raise HTTPException(status_code=401, detail="Not authenticated")
-    try:
-        user = users_module.user_store.get_user_for_token(token)
-    except _USER_STORE_OUTAGE as exc:
-        raise _store_unavailable(exc, route="get_current_user") from None
     if not user:
         raise HTTPException(status_code=401, detail="Invalid or expired session")
     return user
diff --git a/dashboard/backend/api/router.py b/dashboard/backend/api/router.py
index 92134cf8..f9b45d03 100644
--- a/dashboard/backend/api/router.py
+++ b/dashboard/backend/api/router.py
@@ -20,6 +20,7 @@
 from dashboard.backend.api.routers.leaderboard import router as leaderboard_router
 from dashboard.backend.api.routers.news import router as news_router
 from dashboard.backend.api.routers.portfolio import router as portfolio_router
+from dashboard.backend.api.routers.research import router as research_router
 from dashboard.backend.api.routers.runs import router as runs_router
 from dashboard.backend.api.routers.strategies import router as strategies_router
 from dashboard.backend.api.routers.robinhood_live import router as robinhood_router
@@ -31,6 +32,7 @@
 api_router.include_router(admin_users_router)
 api_router.include_router(algo_router)
 api_router.include_router(agents_router)
+api_router.include_router(research_router)
 api_router.include_router(analytics_router)
 api_router.include_router(admin_analytics_router)
 api_router.include_router(discord_router)
diff --git a/dashboard/backend/api/routers/research.py b/dashboard/backend/api/routers/research.py
new file mode 100644
index 00000000..96fb43df
--- /dev/null
+++ b/dashboard/backend/api/routers/research.py
@@ -0,0 +1,355 @@
+"""Research-agent routes (design N2/PR2).
+
+The /admin-style analogue of the marketplace for report-output agents: the
+catalog lists them (shelf ``research``), "add" is the research clone, and the
+workbench's runs proxy to the agent's external service via the N2/PR2 contract
+(manifest / runs / status / result — see research-agent-integration/contract.md).
+
+Auth model: everything requires a signed-in user (runs and reports are
+per-user; a due-diligence report is not platform-public content). Service-side
+calls carry ``X-Service-Token`` from RESEARCH_SERVICE_TOKEN.
+
+Completion side effects happen on the poll that *discovers* completion (the
+frontend polls every ~15s while a run is open; there is no background sweeper
+in v1). If the user closes the browser, the email fires the next time anyone
+fetches that run's status — acceptable for v1, noted in the route docstring.
+"""
+
+from __future__ import annotations
+
+import base64
+import os
+from typing import Any, Dict, Optional
+
+import httpx
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.responses import Response
+
+from dashboard.backend.api.auth import get_current_user
+from dashboard.backend.domain.agents import marketplace as marketplace_mod
+from dashboard.backend.domain.agents import research_store
+
+router = APIRouter(prefix="/v1/research", tags=["research"])
+
+POLL_TIMEOUT_SECONDS = 10.0
+MANIFEST_CACHE_SECONDS = 300.0
+_manifest_cache: Dict[str, Any] = {}
+
+ARTIFACT_CONTENT_TYPES = {
+    "docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    "pdf": "application/pdf",
+    "markdown": "text/markdown; charset=utf-8",
+    "markdown_report": "text/markdown; charset=utf-8",
+    "evidence_json": "application/json",
+    "evidence": "application/json",
+}
+
+
+def _service_token() -> str:
+    return os.getenv("RESEARCH_SERVICE_TOKEN", "dev-token")
+
+
+def _template_or_404(template_id: str) -> Dict[str, Any]:
+    template = marketplace_mod.get_marketplace_template(template_id)
+    if not template or not marketplace_mod.shelf_is_research(template):
+        raise HTTPException(status_code=404, detail="Research template not found")
+    return template
+
+
+def _service_base(template: Dict[str, Any]) -> str:
+    return marketplace_mod.research_service_config(template)["base_url"]
+
+
+def _agent_id(template: Dict[str, Any]) -> str:
+    return (template.get("research") or {}).get("agent_id", "")
+
+
+def _service_headers() -> Dict[str, str]:
+    return {"X-Service-Token": _service_token()}
+
+
+def _service_error(exc: httpx.HTTPError, action: str) -> HTTPException:
+    status = getattr(getattr(exc, "response", None), "status_code", None)
+    if status == 401:
+        return HTTPException(status_code=502, detail="Research service rejected its token")
+    if status == 404:
+        return HTTPException(status_code=404, detail="Research service does not know this run")
+    if status == 422:
+        try:
+            detail = exc.response.json()
+        except Exception:
+            detail = {"detail": "validation failed"}
+        return HTTPException(status_code=422, detail=detail)
+    return HTTPException(status_code=503, detail=f"Research service unavailable while {action}")
+
+
+def _cached_manifest(template: Dict[str, Any]) -> Dict[str, Any]:
+    import time
+
+    cache_key = _agent_id(template)
+    cached = _manifest_cache.get(cache_key)
+    now = time.time()
+    if cached and now - cached["at"] < MANIFEST_CACHE_SECONDS:
+        return cached["data"]
+    response = httpx.get(
+        f"{_service_base(template)}/manifest",
+        params={"agent_id": _agent_id(template)},
+        headers=_service_headers(),
+        timeout=POLL_TIMEOUT_SECONDS,
+    )
+    response.raise_for_status()
+    data = response.json()
+    _manifest_cache[cache_key] = {"at": now, "data": data}
+    return data
+
+
+def _public_agent_card(template: Dict[str, Any]) -> Dict[str, Any]:
+    """Catalog card + added-state for the Community / My Agents shelves."""
+    public = marketplace_mod._public_template(template)
+    return public
+
+
+@router.get("/agents")
+def list_research_agents(current_user: dict = Depends(get_current_user)):
+    added = set(research_store.list_added_template_ids(current_user["id"]))
+    items = [
+        _public_agent_card(raw)
+        for raw in marketplace_mod._load_catalog().values()
+        if marketplace_mod.shelf_is_research(raw)
+    ]
+    for item in items:
+        item["added"] = item["template_id"] in added
+    return {"agents": items}
+
+
+@router.post("/agents/{template_id}/add")
+def add_research_agent(template_id: str, current_user: dict = Depends(get_current_user)):
+    _template_or_404(template_id)
+    created = research_store.add_research_agent(current_user["id"], template_id)
+    return {"added": True, "created": created}
+
+
+@router.delete("/agents/{template_id}/add")
+def remove_research_agent(template_id: str, current_user: dict = Depends(get_current_user)):
+    removed = research_store.remove_research_agent(current_user["id"], template_id)
+    return {"removed": removed}
+
+
+@router.get("/agents/{template_id}/manifest")
+def get_manifest(template_id: str, current_user: dict = Depends(get_current_user)):
+    template = _template_or_404(template_id)
+    try:
+        return _cached_manifest(template)
+    except httpx.HTTPError as exc:
+        raise _service_error(exc, "loading the agent manifest") from None
+
+
+@router.post("/agents/{template_id}/runs")
+def create_research_run(
+    template_id: str,
+    body: dict,
+    current_user: dict = Depends(get_current_user),
+):
+    template = _template_or_404(template_id)
+    settings = body.get("settings")
+    if not isinstance(settings, dict):
+        raise HTTPException(status_code=422, detail={"detail": "settings object required"})
+    try:
+        manifest = _cached_manifest(template)
+    except httpx.HTTPError as exc:
+        raise _service_error(exc, "loading the agent manifest") from None
+
+    # Server-side required-field check so a stale frontend cannot silently
+    # submit an incomplete mandate (the service would 422 anyway; this gives
+    # the same field_errors shape without depending on its copy).
+    field_errors = {
+        field["id"]: "required"
+        for field in manifest.get("settings_schema", {}).get("fields", [])
+        if field.get("required") and not str(settings.get(field["id"]) or "").strip()
+    }
+    if field_errors:
+        raise HTTPException(
+            status_code=422,
+            detail={"detail": "validation failed", "field_errors": field_errors},
+        )
+
+    email_me = bool(body.get("email_me"))
+    payload = {"agent_id": _agent_id(template), "settings": settings}
+    try:
+        response = httpx.post(
+            f"{_service_base(template)}/runs",
+            json=payload,
+            headers=_service_headers(),
+            timeout=POLL_TIMEOUT_SECONDS,
+        )
+        response.raise_for_status()
+        service_run = response.json()
+    except httpx.HTTPError as exc:
+        raise _service_error(exc, "submitting the research run") from None
+
+    import uuid
+
+    service_run_id = str(service_run.get("run_id") or "").strip()
+    run_id = f"rr_{service_run_id}" if service_run_id else f"rr_{uuid.uuid4().hex[:12]}"
+    research_store.create_run(
+        run_id=run_id,
+        user_id=current_user["id"],
+        template_id=template_id,
+        service_run_id=service_run_id,
+        status=str(service_run.get("status") or "running"),
+        settings=settings,
+        email_me=email_me,
+    )
+    return {"run_id": run_id, "status": service_run.get("status") or "running"}
+
+
+def _service_run_id(run: Dict[str, Any]) -> str:
+    return str(run.get("service_run_id") or "")
+
+
+def _maybe_complete_run(run: Dict[str, Any], template: Dict[str, Any],
+                        current_user: dict) -> Dict[str, Any]:
+    """Poll the service once for a non-terminal run; on first discovery of
+    completion, store artifacts and send the notification email."""
+    if run["status"] in ("completed", "failed"):
+        return run
+    try:
+        response = httpx.get(
+            f"{_service_base(template)}/runs/{_service_run_id(run)}",
+            headers=_service_headers(),
+            timeout=POLL_TIMEOUT_SECONDS,
+        )
+        response.raise_for_status()
+        service_status = response.json()
+    except httpx.HTTPError:
+        # A single poll failure is tolerated — the next frontend poll retries.
+        return run
+
+    status = str(service_status.get("status") or "running")
+    if status == "failed":
+        research_store.update_run_status(run["run_id"], "failed",
+                                         error=service_status.get("error") or "Research failed",
+                                         completed=True)
+        run["status"] = "failed"
+        return run
+    if status != "completed":
+        return run
+
+    try:
+        result = httpx.get(
+            f"{_service_base(template)}/runs/{_service_run_id(run)}/result",
+            headers=_service_headers(),
+            timeout=POLL_TIMEOUT_SECONDS,
+        )
+        result.raise_for_status()
+        payload = result.json()
+    except httpx.HTTPError:
+        research_store.update_run_status(run["run_id"], "failed",
+                                         error="Result could not be retrieved",
+                                         completed=True)
+        run["status"] = "failed"
+        return run
+
+    research_store.store_artifacts(
+        run["run_id"],
+        payload.get("artifacts") or {},
+        payload.get("evidence"),
+        payload.get("report_markdown") or "",
+    )
+    research_store.update_run_status(run["run_id"], "completed", completed=True)
+    run["status"] = "completed"
+
+    # Notification email (link, not attachment — Brevo sender is plain-text v1).
+    if run.get("email_me") and not run.get("emailed"):
+        from dashboard.backend.infrastructure.email.sender import email_configured, send_email
+
+        if email_configured():
+            base = os.getenv("PUBLIC_BASE_URL", "https://agentic-trading-lab.vercel.app")
+            link = f"{base}/app?view=research"
+            sent = send_email(
+                current_user["email"],
+                f"[ATL] Your research report is ready — {template['name']}",
+                "Your research report has completed.\n\n"
+                f"Open it here: {link}\n"
+                "(The report page offers Markdown / DOCX / PDF downloads.)\n",
+            )
+            if sent:
+                research_store.mark_emailed(run["run_id"])
+    return run
+
+
+@router.get("/runs")
+def list_my_runs(current_user: dict = Depends(get_current_user)):
+    runs = research_store.list_runs_for_user(current_user["id"])
+    for run in runs:
+        try:
+            run["settings"] = __import__("json").loads(run.pop("settings_json") or "{}")
+        except Exception:
+            run["settings"] = {}
+    return {"runs": runs}
+
+
+@router.get("/runs/{run_id}")
+def get_run_status(run_id: str, current_user: dict = Depends(get_current_user)):
+    run = research_store.get_run(run_id, current_user["id"])
+    if not run:
+        raise HTTPException(status_code=404, detail="Run not found")
+    template = _template_or_404(run["template_id"])
+    run = _maybe_complete_run(run, template, current_user)
+    return {
+        "run_id": run["run_id"],
+        "template_id": run["template_id"],
+        "status": run["status"],
+        "error": run.get("error"),
+        "created_at": run["created_at"],
+        "completed_at": run["completed_at"],
+    }
+
+
+def _run_and_template_or_404(run_id: str, user: dict):
+    run = research_store.get_run(run_id, user["id"])
+    if not run:
+        raise HTTPException(status_code=404, detail="Run not found")
+    if run["status"] != "completed":
+        raise HTTPException(status_code=409, detail="Run not completed")
+    template = _template_or_404(run["template_id"])
+    return run, template
+
+
+@router.get("/runs/{run_id}/report")
+def get_run_report(run_id: str, current_user: dict = Depends(get_current_user)):
+    run, _template = _run_and_template_or_404(run_id, current_user)
+    artifact = research_store.get_artifact(run_id, "markdown")
+    if not artifact:
+        raise HTTPException(status_code=404, detail="Report not found")
+    return {
+        "run_id": run_id,
+        "template_id": run["template_id"],
+        "report_markdown": artifact["content_base64"],
+        "filename": artifact["filename"],
+    }
+
+
+@router.get("/runs/{run_id}/artifacts/{kind}")
+def download_artifact(run_id: str, kind: str, current_user: dict = Depends(get_current_user)):
+    run, _template = _run_and_template_or_404(run_id, current_user)
+    artifact = research_store.get_artifact(run_id, kind)
+    if not artifact:
+        raise HTTPException(status_code=404, detail="Artifact not found")
+    content = artifact["content_base64"] or ""
+    if artifact["kind"] in ("markdown_report", "evidence_json"):
+        # These two are stored as plain text, not base64.
+        return Response(
+            content=content,
+            media_type=ARTIFACT_CONTENT_TYPES[artifact["kind"]],
+            headers={"Content-Disposition": f'attachment; filename="{artifact["filename"]}"'},
+        )
+    try:
+        raw = base64.b64decode(content)
+    except Exception:
+        raise HTTPException(status_code=500, detail="Artifact payload is corrupt")
+    return Response(
+        content=raw,
+        media_type=ARTIFACT_CONTENT_TYPES.get(artifact["kind"], "application/octet-stream"),
+        headers={"Content-Disposition": f'attachment; filename="{artifact["filename"]}"'},
+    )
diff --git a/dashboard/backend/app.py b/dashboard/backend/app.py
index 3b2501b0..fd631ad7 100644
--- a/dashboard/backend/app.py
+++ b/dashboard/backend/app.py
@@ -374,6 +374,11 @@ def bar_cache_background():
 
 frontend_path = FRONTEND_DIR
 
+# HTML/JS/CSS always revalidate: the local console bumps ?v= per change, and
+# browsers heuristic-cache FileResponses that carry no Cache-Control header,
+# which is how Chrome kept serving a pre-autologin app.js (design N2/PR2).
+NO_CACHE = {"Cache-Control": "no-cache"}
+
 @app.get("/", include_in_schema=False)
 async def serve_root():
     """Serve marketing landing page."""
@@ -398,7 +403,7 @@ async def serve_app(request: Request):
         if user_query:
             target += f"?user={quote(str(user_query), safe='')}"
         return RedirectResponse(url=target, status_code=307)
-    return FileResponse(frontend_path / "app.html")
+    return FileResponse(frontend_path / "app.html", headers=NO_CACHE)
 
 
 @app.get("/app/", include_in_schema=False)
@@ -512,12 +517,12 @@ async def redirect_admin_analytics(request: Request):
 @app.get("/styles.css", include_in_schema=False)
 async def serve_styles():
     """Serve styles.css."""
-    return FileResponse(frontend_path / "styles.css", media_type="text/css")
+    return FileResponse(frontend_path / "styles.css", media_type="text/css", headers=NO_CACHE)
 
 @app.get("/app.js", include_in_schema=False)
 async def serve_app_js():
     """Serve app.js."""
-    return FileResponse(frontend_path / "app.js", media_type="text/javascript")
+    return FileResponse(frontend_path / "app.js", media_type="text/javascript", headers=NO_CACHE)
 
 @app.get("/home-page.js", include_in_schema=False)
 async def serve_home_page_js():
diff --git a/dashboard/backend/domain/agents/marketplace.py b/dashboard/backend/domain/agents/marketplace.py
index f4097d30..5fd00bb7 100644
--- a/dashboard/backend/domain/agents/marketplace.py
+++ b/dashboard/backend/domain/agents/marketplace.py
@@ -19,15 +19,17 @@
 
 # Community supermarket rows. Declared order is display order: LLMs first,
 # then Agents. Unknown / omitted values fall through ``_normalize_shelf``.
-MARKETPLACE_SHELVES = ("llms", "open")
+MARKETPLACE_SHELVES = ("llms", "open", "research")
 
 
 def _normalize_shelf(raw: Dict[str, Any]) -> str:
-    """Return ``llms`` or ``open``.
+    """Return ``llms``, ``open`` or ``research``.
 
     Explicit ``shelf`` on the catalog row wins. Otherwise a non-pipeline
     runtime (today: AI Hedge Fund) is an open agent, so a future hosted
     project does not have to remember the field to land on the right row.
+    Research agents (design N2/PR2) are always explicit — they are external
+    services, not runtimes this process hosts.
     """
     explicit = str(raw.get("shelf") or "").strip().lower()
     if explicit in MARKETPLACE_SHELVES:
@@ -36,6 +38,29 @@ def _normalize_shelf(raw: Dict[str, Any]) -> str:
     return "open" if runtime_type != "pipeline" else "llms"
 
 
+def shelf_is_research(raw: Dict[str, Any]) -> bool:
+    """True when a catalog row is a research-agent service template."""
+    return _normalize_shelf(raw) == "research"
+
+
+def research_service_config(raw: Dict[str, Any]) -> Dict[str, str]:
+    """Resolve the service base URL for a research template row.
+
+    The URL comes from the environment named by ``service_base_url_env``
+    (deployment-owned) with the catalog's ``service_base_url_default`` as the
+    local-dev fallback — same pattern as every other credential in the app.
+    """
+    import os
+
+    research = raw.get("research") or {}
+    env_name = str(research.get("service_base_url_env") or "").strip()
+    base = os.getenv(env_name, "") if env_name else ""
+    return {
+        "base_url": (base or str(research.get("service_base_url_default") or "")).rstrip("/"),
+        "agent_id": str(research.get("agent_id") or ""),
+    }
+
+
 def _public_template(raw: Dict[str, Any]) -> Dict[str, Any]:
     # This "category" and an agent's "category" used to be two different
     # vocabularies under one key name: templates carried display strings
@@ -73,6 +98,19 @@ def _public_template(raw: Dict[str, Any]) -> Dict[str, Any]:
     }
     if repo_url.startswith(("https://github.com/", "http://github.com/")):
         public["repo_url"] = repo_url
+    # Research rows (N2/PR2) project their service pointer and delivery facts
+    # instead of runtime/step fields, which mean nothing for an external
+    # Deep Research service.
+    research = raw.get("research")
+    if shelf_is_research(raw):
+        public["mode"] = "research"
+        public["model_name"] = raw.get("model_name") or "deep-research"
+        public["research"] = {
+            "agent_id": research.get("agent_id"),
+            "estimated_runtime_seconds": int(research.get("estimated_runtime_seconds") or 300),
+            "max_runtime_seconds": int(research.get("max_runtime_seconds") or 1800),
+            "output_formats": list(research.get("output_formats") or []),
+        }
     return public
 
 
diff --git a/dashboard/backend/domain/agents/research_store.py b/dashboard/backend/domain/agents/research_store.py
new file mode 100644
index 00000000..f221ca80
--- /dev/null
+++ b/dashboard/backend/domain/agents/research_store.py
@@ -0,0 +1,210 @@
+"""Storage for the research-agent module (design N2/PR2).
+
+Three concerns, one small sqlite module (same DATABASE_PATH as the rest of the
+dashboard — the deploy runs sqlite stores, consistent with the agent store):
+
+- ``research_agent_adds``   : which user cloned which research template
+                              (the research analogue of the marketplace clone)
+- ``research_runs``         : one row per submitted research run
+- ``research_artifacts``    : the completed run's deliverables, stored as
+                              base64 text (Render's filesystem is ephemeral;
+                              the database is the only durable home)
+
+Write-shape notes:
+- Every helper opens its own connection (short-lived, like users_store).
+- ``_init_schema`` runs on module import — cheap CREATE IF NOT EXISTS, and the
+  research module is the only caller.
+"""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+from typing import Any, Dict, List, Optional
+
+from dashboard.backend.database import DB_PATH
+
+
+def _connect() -> sqlite3.Connection:
+    conn = sqlite3.connect(str(DB_PATH))
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def _init_schema() -> None:
+    with _connect() as conn:
+        conn.executescript(
+            """
+            CREATE TABLE IF NOT EXISTS research_agent_adds (
+                user_id INTEGER NOT NULL,
+                template_id TEXT NOT NULL,
+                added_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                PRIMARY KEY (user_id, template_id)
+            );
+
+            CREATE TABLE IF NOT EXISTS research_runs (
+                run_id TEXT PRIMARY KEY,
+                user_id INTEGER NOT NULL,
+                template_id TEXT NOT NULL,
+                service_run_id TEXT,
+                status TEXT NOT NULL DEFAULT 'queued',
+                settings_json TEXT NOT NULL,
+                email_me INTEGER NOT NULL DEFAULT 0,
+                emailed INTEGER NOT NULL DEFAULT 0,
+                error TEXT,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                completed_at TIMESTAMP
+            );
+
+            CREATE TABLE IF NOT EXISTS research_artifacts (
+                run_id TEXT NOT NULL,
+                kind TEXT NOT NULL,
+                filename TEXT,
+                content_base64 TEXT,
+                PRIMARY KEY (run_id, kind)
+            );
+
+            CREATE INDEX IF NOT EXISTS idx_research_runs_user
+                ON research_runs(user_id, created_at DESC);
+            """
+        )
+
+
+_init_schema()
+
+
+# --- adds (the research "clone") -------------------------------------------
+
+def add_research_agent(user_id: int, template_id: str) -> bool:
+    """Idempotent add. Returns True when a new row was created."""
+    with _connect() as conn:
+        cursor = conn.execute(
+            "INSERT OR IGNORE INTO research_agent_adds (user_id, template_id) VALUES (?, ?)",
+            (user_id, template_id),
+        )
+        return cursor.rowcount > 0
+
+
+def remove_research_agent(user_id: int, template_id: str) -> bool:
+    with _connect() as conn:
+        cursor = conn.execute(
+            "DELETE FROM research_agent_adds WHERE user_id = ? AND template_id = ?",
+            (user_id, template_id),
+        )
+        return cursor.rowcount > 0
+
+
+def list_added_template_ids(user_id: int) -> List[str]:
+    with _connect() as conn:
+        rows = conn.execute(
+            "SELECT template_id FROM research_agent_adds WHERE user_id = ? ORDER BY added_at DESC",
+            (user_id,),
+        ).fetchall()
+    return [row["template_id"] for row in rows]
+
+
+# --- runs -------------------------------------------------------------------
+
+def create_run(
+    *,
+    run_id: str,
+    user_id: int,
+    template_id: str,
+    service_run_id: str,
+    status: str,
+    settings: Dict[str, Any],
+    email_me: bool,
+) -> None:
+    with _connect() as conn:
+        conn.execute(
+            "INSERT INTO research_runs (run_id, user_id, template_id, service_run_id,"
+            " status, settings_json, email_me) VALUES (?, ?, ?, ?, ?, ?, ?)",
+            (run_id, user_id, template_id, service_run_id, status,
+             json.dumps(settings, ensure_ascii=False), int(email_me)),
+        )
+
+
+def get_run(run_id: str, user_id: int) -> Optional[Dict[str, Any]]:
+    with _connect() as conn:
+        row = conn.execute(
+            "SELECT * FROM research_runs WHERE run_id = ? AND user_id = ?",
+            (run_id, user_id),
+        ).fetchone()
+    return dict(row) if row else None
+
+
+def list_runs_for_user(user_id: int, limit: int = 50) -> List[Dict[str, Any]]:
+    with _connect() as conn:
+        rows = conn.execute(
+            "SELECT run_id, template_id, status, settings_json, error,"
+            " created_at, completed_at FROM research_runs"
+            " WHERE user_id = ? ORDER BY created_at DESC LIMIT ?",
+            (user_id, limit),
+        ).fetchall()
+    return [dict(row) for row in rows]
+
+
+def update_run_status(run_id: str, status: str, error: Optional[str] = None,
+                      completed: bool = False) -> None:
+    with _connect() as conn:
+        if completed:
+            conn.execute(
+                "UPDATE research_runs SET status = ?, error = ?,"
+                " completed_at = CURRENT_TIMESTAMP WHERE run_id = ?",
+                (status, error, run_id),
+            )
+        else:
+            conn.execute(
+                "UPDATE research_runs SET status = ?, error = ? WHERE run_id = ?",
+                (status, error, run_id),
+            )
+
+
+def mark_emailed(run_id: str) -> None:
+    with _connect() as conn:
+        conn.execute("UPDATE research_runs SET emailed = 1 WHERE run_id = ?", (run_id,))
+
+
+# --- artifacts --------------------------------------------------------------
+
+def store_artifacts(run_id: str, artifacts: Dict[str, Dict[str, str]],
+                    evidence: Dict[str, Any], report_markdown: str) -> None:
+    """Persist everything a completed run delivered (replace-on-complete)."""
+    rows = [
+        (run_id, "markdown", f"{run_id}.md", None),
+    ]
+    with _connect() as conn:
+        conn.execute("DELETE FROM research_artifacts WHERE run_id = ?", (run_id,))
+        conn.execute(
+            "INSERT INTO research_artifacts (run_id, kind, filename, content_base64)"
+            " VALUES (?, 'markdown_report', ?, ?)",
+            (run_id, f"{run_id}.md", report_markdown),
+        )
+        for kind, item in (artifacts or {}).items():
+            safe_kind = str(kind).replace("/", "_")
+            conn.execute(
+                "INSERT INTO research_artifacts (run_id, kind, filename, content_base64)"
+                " VALUES (?, ?, ?, ?)",
+                (run_id, safe_kind,
+                 item.get("filename") or f"{run_id}.{safe_kind}",
+                 item.get("content_base64")),
+            )
+        if evidence is not None:
+            conn.execute(
+                "INSERT INTO research_artifacts (run_id, kind, filename, content_base64)"
+                " VALUES (?, 'evidence_json', ?, ?)",
+                (run_id, f"{run_id}_evidence.json",
+                 json.dumps(evidence, ensure_ascii=False)),
+            )
+
+
+def get_artifact(run_id: str, kind: str) -> Optional[Dict[str, Any]]:
+    kind_map = {"markdown": "markdown_report", "evidence_json": "evidence_json"}
+    lookup = kind_map.get(kind, kind)
+    with _connect() as conn:
+        row = conn.execute(
+            "SELECT kind, filename, content_base64 FROM research_artifacts"
+            " WHERE run_id = ? AND kind = ?",
+            (run_id, lookup),
+        ).fetchone()
+    return dict(row) if row else None
diff --git a/dashboard/backend/tests/test_admin_analytics_frontend.py b/dashboard/backend/tests/test_admin_analytics_frontend.py
index 991107fe..3054ac0b 100644
--- a/dashboard/backend/tests/test_admin_analytics_frontend.py
+++ b/dashboard/backend/tests/test_admin_analytics_frontend.py
@@ -161,7 +161,7 @@ def test_profile_menu_admin_entry_opens_the_admin_page():
 def test_app_lifecycle_and_cache_versions_are_wired():
     # Lockstep owner for the console's bumped tags and the /admin page's pins:
     # every bump edits this test in the same change (Global Constraints).
-    assert 'styles.css?v=144' in APP_HTML
+    assert 'styles.css?v=145' in APP_HTML
     assert 'app.js?v=138' in APP_HTML
     assert 'js/admin-tabs.js?v=12' in APP_HTML
     for tag in (
diff --git a/dashboard/backend/tests/test_agent_taxonomy.py b/dashboard/backend/tests/test_agent_taxonomy.py
index 21f7d2c7..4dd63624 100644
--- a/dashboard/backend/tests/test_agent_taxonomy.py
+++ b/dashboard/backend/tests/test_agent_taxonomy.py
@@ -124,6 +124,12 @@ def test_no_template_is_left_on_the_retired_prompting_llms_slug():
     import dashboard.backend.domain.agents.marketplace as marketplace_mod
 
     marketplace_mod.reload_marketplace_catalog()
-    slugs = {t.get("category") for t in marketplace_mod.list_marketplace_templates()}
+    # Research rows (N2/PR2) carry no category: they are not market-trading
+    # templates, so the taxonomy guard scopes to the trading shelves only.
+    slugs = {
+        t.get("category")
+        for t in marketplace_mod.list_marketplace_templates()
+        if t.get("shelf") != "research"
+    }
     assert "prompting_llms" not in slugs
     assert slugs <= AGENT_CATEGORIES
diff --git a/dashboard/backend/tests/test_agents_api.py b/dashboard/backend/tests/test_agents_api.py
index 75157822..ab61987c 100644
--- a/dashboard/backend/tests/test_agents_api.py
+++ b/dashboard/backend/tests/test_agents_api.py
@@ -850,7 +850,8 @@ def test_marketplace_listing_is_ordered_by_shelf_not_by_slug():
     assert llms, "the LLM shelf is empty"
     assert opens, "the Agents shelf is empty"
     assert templates[0]["shelf"] == "llms"
-    assert templates[-1]["shelf"] == "open"
+    # The research shelf (N2/PR2) is declared after the two trading shelves.
+    assert templates[-1]["shelf"] == "research"
     assert [t["name"] for t in opens][0] == "AI Hedge Fund"
     assert {t["template_id"] for t in opens} >= {
         "ai-hedge-fund",
diff --git a/dashboard/backend/tests/test_app_composition.py b/dashboard/backend/tests/test_app_composition.py
index 3b979caf..cb10de44 100644
--- a/dashboard/backend/tests/test_app_composition.py
+++ b/dashboard/backend/tests/test_app_composition.py
@@ -282,6 +282,15 @@
     ("GET", "/admin"),
     ("GET", "/admin.css"),
     ("GET", "/admin-console.css"),
+    ("GET", "/api/v1/research/agents"),
+    ("GET", "/api/v1/research/agents/{template_id}/manifest"),
+    ("POST", "/api/v1/research/agents/{template_id}/add"),
+    ("DELETE", "/api/v1/research/agents/{template_id}/add"),
+    ("POST", "/api/v1/research/agents/{template_id}/runs"),
+    ("GET", "/api/v1/research/runs"),
+    ("GET", "/api/v1/research/runs/{run_id}"),
+    ("GET", "/api/v1/research/runs/{run_id}/report"),
+    ("GET", "/api/v1/research/runs/{run_id}/artifacts/{kind}"),
     ("GET", "/admin-analytics"),
     ("GET", "/styles.css"),
     ("GET", "/ticker"),
diff --git a/dashboard/backend/tests/test_backtest_comparison_frontend.py b/dashboard/backend/tests/test_backtest_comparison_frontend.py
index 61200f41..fe0e9925 100644
--- a/dashboard/backend/tests/test_backtest_comparison_frontend.py
+++ b/dashboard/backend/tests/test_backtest_comparison_frontend.py
@@ -192,7 +192,7 @@ def test_exact_raw_ties_mark_every_tied_series_best():
 def test_comparison_script_and_semantic_table_ship_before_app():
     helper = '<script src="js/backtest-comparison.js?v=1" defer></script>'
     app = '<script src="app.js?v=138" defer></script>'
-    assert 'href="styles.css?v=144"' in APP_HTML
+    assert 'href="styles.css?v=145"' in APP_HTML
     assert APP_HTML.index(helper) < APP_HTML.index(app)
     for element_id in (
         "performanceLegend",
diff --git a/dashboard/backend/tests/test_frontend_fast_boot.py b/dashboard/backend/tests/test_frontend_fast_boot.py
index 6d358848..80bc3f1b 100644
--- a/dashboard/backend/tests/test_frontend_fast_boot.py
+++ b/dashboard/backend/tests/test_frontend_fast_boot.py
@@ -193,7 +193,7 @@ def test_cache_busters_bumped():
     # round of follow-ups (#347/#348).
     assert "app.js?v=138" in APP_HTML
     assert "js/agent-editor.js?v=31" in APP_HTML
-    assert "styles.css?v=144" in APP_HTML
+    assert "styles.css?v=145" in APP_HTML
     assert "js/leaderboard.js?v=33" in APP_HTML
     assert "home-page.js?v=50" in APP_HTML
     assert "js/credit-format.js?v=1" in APP_HTML
diff --git a/dashboard/backend/tests/test_frontend_model_facets.py b/dashboard/backend/tests/test_frontend_model_facets.py
index cd035f5b..d84da950 100644
--- a/dashboard/backend/tests/test_frontend_model_facets.py
+++ b/dashboard/backend/tests/test_frontend_model_facets.py
@@ -54,6 +54,8 @@ def test_every_catalog_model_matches_a_vendor_prefix():
     "AI-powered" with no chip and no badge, which is otherwise invisible."""
     prefixes = [row[1] for row in _vendor_rows()]
     for template in _CATALOG:
+        if template.get("shelf") == "research":
+            continue  # external Deep Research service (N2/PR2): no ATL-owned model
         model = template["model_name"].lower()
         assert any(model.startswith(p) for p in prefixes), (
             f"{template['template_id']} runs {model!r}, which matches no MODEL_VENDORS prefix"
diff --git a/dashboard/backend/tests/test_marketplace_catalog_models.py b/dashboard/backend/tests/test_marketplace_catalog_models.py
index 9baec802..47861f70 100644
--- a/dashboard/backend/tests/test_marketplace_catalog_models.py
+++ b/dashboard/backend/tests/test_marketplace_catalog_models.py
@@ -47,6 +47,8 @@
 def test_every_template_runs_a_supported_or_hosted_model(template):
     if template.get("runtime_type"):
         return  # hosted runtime: its model is not user-selectable
+    if template.get("shelf") == "research":
+        return  # external Deep Research service (N2/PR2): no ATL-owned model
     assert template["model_name"] in (_SUPPORTED_SLUGS | _LEADERBOARD_ONLY_SLUGS), (
         f"{template['template_id']} runs {template['model_name']!r}, "
         "which is not in SUPPORTED_MODELS or the leaderboard"
@@ -151,7 +153,11 @@ def test_catalog_rows_declare_a_supermarket_shelf():
 
 def test_catalog_covers_every_pickable_vendor():
     """The facet is decorative if most of its chips are empty."""
-    vendors = {t["model_name"].split("/", 1)[0] for t in _CATALOG}
+    vendors = {
+        t["model_name"].split("/", 1)[0]
+        for t in _CATALOG
+        if t.get("shelf") != "research"
+    }
     assert {"anthropic", "openai", "google", "deepseek", "qwen"} <= vendors
 
 
@@ -160,4 +166,6 @@ def test_catalog_includes_both_markets():
     Agents shelf, so the China A-Share chip ships again without a hardcoded
     chip list.
     """
-    assert {t.get("category") for t in _CATALOG} == {"us_stocks", "cn_ashares"}
+    assert {
+        t.get("category") for t in _CATALOG if t.get("shelf") != "research"
+    } == {"us_stocks", "cn_ashares"}
diff --git a/dashboard/config/marketplace.json b/dashboard/config/marketplace.json
index be95d9cb..b53e8c58 100644
--- a/dashboard/config/marketplace.json
+++ b/dashboard/config/marketplace.json
@@ -1,417 +1,468 @@
 {
-  "templates": [
+ "templates": [
+  {
+   "template_id": "claude-haiku-4-5",
+   "shelf": "llms",
+   "name": "Claude Haiku 4.5",
+   "model_name": "anthropic/claude-haiku-4-5",
+   "description": "The Competition Leaderboard's Claude Haiku 4.5 agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
+   "category": "us_stocks",
+   "tags": [
+    "competition model",
+    "DJIA"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "claude-haiku-4-5",
-      "shelf": "llms",
-      "name": "Claude Haiku 4.5",
-      "model_name": "anthropic/claude-haiku-4-5",
-      "description": "The Competition Leaderboard's Claude Haiku 4.5 agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
-      "category": "us_stocks",
-      "tags": [
-        "competition model",
-        "DJIA"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_claude_haiku",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_claude_haiku",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "claude-sonnet-4-6",
+   "shelf": "llms",
+   "name": "Claude Sonnet 4.6",
+   "model_name": "anthropic/claude-sonnet-4-6",
+   "description": "The Competition Leaderboard's Claude Sonnet 4.6 agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
+   "category": "us_stocks",
+   "tags": [
+    "competition model",
+    "DJIA"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "claude-sonnet-4-6",
-      "shelf": "llms",
-      "name": "Claude Sonnet 4.6",
-      "model_name": "anthropic/claude-sonnet-4-6",
-      "description": "The Competition Leaderboard's Claude Sonnet 4.6 agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
-      "category": "us_stocks",
-      "tags": [
-        "competition model",
-        "DJIA"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_claude_sonnet",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_claude_sonnet",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "deepseek-v4-pro",
+   "shelf": "llms",
+   "name": "DeepSeek V4 Pro",
+   "model_name": "deepseek/deepseek-v4-pro",
+   "description": "The Competition Leaderboard's DeepSeek V4 Pro agent — the only model that beat the passive baselines in the April 2026 contest window. Same long-only DJIA hourly backtest; add it and edit the instruction to try this model yourself.",
+   "category": "us_stocks",
+   "tags": [
+    "competition model",
+    "DJIA"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "deepseek-v4-pro",
-      "shelf": "llms",
-      "name": "DeepSeek V4 Pro",
-      "model_name": "deepseek/deepseek-v4-pro",
-      "description": "The Competition Leaderboard's DeepSeek V4 Pro agent — the only model that beat the passive baselines in the April 2026 contest window. Same long-only DJIA hourly backtest; add it and edit the instruction to try this model yourself.",
-      "category": "us_stocks",
-      "tags": [
-        "competition model",
-        "DJIA"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_deepseek_v4",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_deepseek_v4",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "gpt-5-5",
+   "shelf": "llms",
+   "name": "GPT-5.5",
+   "model_name": "openai/gpt-5.5",
+   "description": "The Competition Leaderboard's GPT-5.5 agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
+   "category": "us_stocks",
+   "tags": [
+    "competition model",
+    "DJIA"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "gpt-5-5",
-      "shelf": "llms",
-      "name": "GPT-5.5",
-      "model_name": "openai/gpt-5.5",
-      "description": "The Competition Leaderboard's GPT-5.5 agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
-      "category": "us_stocks",
-      "tags": [
-        "competition model",
-        "DJIA"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_gpt_5_5",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_gpt_5_5",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "gemini-3-1-pro-preview",
+   "shelf": "llms",
+   "name": "Gemini 3.1 Pro Preview",
+   "model_name": "google/gemini-3.1-pro-preview",
+   "description": "The Competition Leaderboard's Gemini 3.1 Pro Preview agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
+   "category": "us_stocks",
+   "tags": [
+    "competition model",
+    "DJIA"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "gemini-3-1-pro-preview",
-      "shelf": "llms",
-      "name": "Gemini 3.1 Pro Preview",
-      "model_name": "google/gemini-3.1-pro-preview",
-      "description": "The Competition Leaderboard's Gemini 3.1 Pro Preview agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
-      "category": "us_stocks",
-      "tags": [
-        "competition model",
-        "DJIA"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_gemini_pro",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_gemini_pro",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "nemotron-3-nano-30b",
+   "shelf": "llms",
+   "name": "Nemotron 3 Nano 30B",
+   "model_name": "nvidia/nemotron-3-nano-30b-a3b",
+   "description": "The Competition Leaderboard's NVIDIA Nemotron 3 Nano 30B agent. An open-weight model on the same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
+   "category": "us_stocks",
+   "tags": [
+    "competition model",
+    "DJIA"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "nemotron-3-nano-30b",
-      "shelf": "llms",
-      "name": "Nemotron 3 Nano 30B",
-      "model_name": "nvidia/nemotron-3-nano-30b-a3b",
-      "description": "The Competition Leaderboard's NVIDIA Nemotron 3 Nano 30B agent. An open-weight model on the same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
-      "category": "us_stocks",
-      "tags": [
-        "competition model",
-        "DJIA"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_nemotron_nano",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_nemotron_nano",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "qwen3-7-plus",
+   "shelf": "llms",
+   "name": "Qwen3.7 Plus",
+   "model_name": "qwen/qwen3.7-plus",
+   "description": "The Competition Leaderboard's Qwen3.7 Plus agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
+   "category": "us_stocks",
+   "tags": [
+    "competition model",
+    "DJIA"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "qwen3-7-plus",
-      "shelf": "llms",
-      "name": "Qwen3.7 Plus",
-      "model_name": "qwen/qwen3.7-plus",
-      "description": "The Competition Leaderboard's Qwen3.7 Plus agent. Same long-only DJIA hourly backtest as the rest of the board — add it and edit the instruction to try this model yourself.",
-      "category": "us_stocks",
-      "tags": [
-        "competition model",
-        "DJIA"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_qwen3_7",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_qwen3_7",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "ai-hedge-fund",
+   "shelf": "open",
+   "card_subtitle": "Open-source multi-agent system",
+   "name": "AI Hedge Fund",
+   "model_name": "nvidia/nemotron-3-nano-30b-a3b",
+   "description": "A team of AI investors that analyzes the market, develops trading ideas, and tests them through backtesting.",
+   "category": "us_stocks",
+   "tags": [
+    "analyst team",
+    "fundamentals",
+    "official template"
+   ],
+   "author": "virattt / Agentic Trading Lab",
+   "repo_url": "https://github.com/virattt/ai-hedge-fund",
+   "runtime_type": "ai_hedge_fund",
+   "runtime_config": {
+    "analysts": [
+     "fundamentals_analyst",
+     "technical_analyst",
+     "sentiment_analyst",
+     "valuation_analyst"
+    ]
+   }
+  },
+  {
+   "template_id": "balanced-starter",
+   "shelf": "open",
+   "name": "Balanced Starter",
+   "model_name": "anthropic/claude-haiku-4-5",
+   "description": "A simple starter agent that diversifies across strong stocks, buys dips, and takes profits after run-ups.",
+   "category": "us_stocks",
+   "tags": [
+    "starter",
+    "diversified"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "ai-hedge-fund",
-      "shelf": "open",
-      "card_subtitle": "Open-source multi-agent system",
-      "name": "AI Hedge Fund",
-      "model_name": "nvidia/nemotron-3-nano-30b-a3b",
-      "description": "A team of AI investors that analyzes the market, develops trading ideas, and tests them through backtesting.",
-      "category": "us_stocks",
-      "tags": [
-        "analyst team",
-        "fundamentals",
-        "official template"
-      ],
-      "author": "virattt / Agentic Trading Lab",
-      "repo_url": "https://github.com/virattt/ai-hedge-fund",
-      "runtime_type": "ai_hedge_fund",
-      "runtime_config": {
-        "analysts": [
-          "fundamentals_analyst",
-          "technical_analyst",
-          "sentiment_analyst",
-          "valuation_analyst"
-        ]
-      }
-    },
+     "id": "sub_balanced_starter",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "momentum-scout",
+   "shelf": "open",
+   "name": "Momentum Scout",
+   "model_name": "anthropic/claude-haiku-4-5",
+   "description": "Focus on recent price strength and volume. Favor leaders with positive momentum and trim laggards quickly.",
+   "category": "us_stocks",
+   "tags": [
+    "momentum",
+    "trend"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "balanced-starter",
-      "shelf": "open",
-      "name": "Balanced Starter",
-      "model_name": "anthropic/claude-haiku-4-5",
-      "description": "A simple starter agent that diversifies across strong stocks, buys dips, and takes profits after run-ups.",
-      "category": "us_stocks",
-      "tags": [
-        "starter",
-        "diversified"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_balanced_starter",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money across a few of the strongest available stocks. Buy on meaningful dips, take profits after strong run-ups, and never put everything into one stock.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_momentum_instruction",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Prioritize stocks showing the strongest recent momentum and healthy volume. Add to winners on pullbacks, cut positions that lose momentum, and keep cash when the tape is unclear.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "pipeline-analyst",
+   "shelf": "open",
+   "name": "Three-Step Analyst",
+   "model_name": "anthropic/claude-sonnet-4-6",
+   "description": "A three-step strategy: gather market facts, convert them into signals, then produce executable orders.",
+   "category": "us_stocks",
+   "tags": [
+    "multi-step strategy",
+    "official template"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "momentum-scout",
-      "shelf": "open",
-      "name": "Momentum Scout",
-      "model_name": "anthropic/claude-haiku-4-5",
-      "description": "Focus on recent price strength and volume. Favor leaders with positive momentum and trim laggards quickly.",
-      "category": "us_stocks",
-      "tags": [
-        "momentum",
-        "trend"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_momentum_instruction",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Prioritize stocks showing the strongest recent momentum and healthy volume. Add to winners on pullbacks, cut positions that lose momentum, and keep cash when the tape is unclear.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
+     "id": "sub_gather",
+     "presetKey": "info_gather",
+     "label": "Information Gathering",
+     "prompt": "You are the information-gathering sub-agent. Collect key facts relevant to trading decisions from market data, news, and macro events. Filter noise and keep high-confidence facts and indicator changes.",
+     "outputFormat": "JSON: { \"timestamp\": \"ISO8601\", \"symbols\": [\"...\"], \"facts\": [{ \"source\": \"...\", \"summary\": \"...\", \"impact\": \"bullish|bearish|neutral\" }], \"confidence\": 0.0-1.0 }"
     },
     {
-      "template_id": "pipeline-analyst",
-      "shelf": "open",
-      "name": "Three-Step Analyst",
-      "model_name": "anthropic/claude-sonnet-4-6",
-      "description": "A three-step strategy: gather market facts, convert them into signals, then produce executable orders.",
-      "category": "us_stocks",
-      "tags": [
-        "multi-step strategy",
-        "official template"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_gather",
-          "presetKey": "info_gather",
-          "label": "Information Gathering",
-          "prompt": "You are the information-gathering sub-agent. Collect key facts relevant to trading decisions from market data, news, and macro events. Filter noise and keep high-confidence facts and indicator changes.",
-          "outputFormat": "JSON: { \"timestamp\": \"ISO8601\", \"symbols\": [\"...\"], \"facts\": [{ \"source\": \"...\", \"summary\": \"...\", \"impact\": \"bullish|bearish|neutral\" }], \"confidence\": 0.0-1.0 }"
-        },
-        {
-          "id": "sub_signal",
-          "presetKey": "info_to_signal",
-          "label": "Information to Signal",
-          "prompt": "You are the signal-generation sub-agent. Based on upstream information-gathering output, convert facts and indicators into executable trading signals (direction, strength, time horizon).",
-          "outputFormat": "JSON: { \"signals\": [{ \"symbol\": \"...\", \"direction\": \"long|short|flat\", \"strength\": 0.0-1.0, \"horizon\": \"1h|4h|1d\", \"rationale\": \"...\" }] }"
-        },
-        {
-          "id": "sub_exec",
-          "presetKey": "signal_to_execution",
-          "label": "Signal to Execution",
-          "prompt": "You are the trade-execution sub-agent. Turn signals into concrete order instructions, respecting position limits, liquidity, and slippage. Output a submit-ready buy/sell plan.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
+     "id": "sub_signal",
+     "presetKey": "info_to_signal",
+     "label": "Information to Signal",
+     "prompt": "You are the signal-generation sub-agent. Based on upstream information-gathering output, convert facts and indicators into executable trading signals (direction, strength, time horizon).",
+     "outputFormat": "JSON: { \"signals\": [{ \"symbol\": \"...\", \"direction\": \"long|short|flat\", \"strength\": 0.0-1.0, \"horizon\": \"1h|4h|1d\", \"rationale\": \"...\" }] }"
     },
     {
-      "template_id": "blue-chip-steady",
-      "shelf": "open",
-      "name": "Blue-Chip Steady",
-      "model_name": "anthropic/claude-haiku-4-5",
-      "description": "Buy and hold a handful of the strongest Dow companies, selling only when a position deteriorates badly. Mirrors the buy-and-hold benchmark on our leaderboard.",
-      "category": "us_stocks",
-      "tags": [
-        "buy and hold",
-        "blue chips"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_blue_chip_steady",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Buy and hold a handful of the strongest Dow companies. Sell only if a company's position deteriorates badly. Do not chase short-term moves.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_exec",
+     "presetKey": "signal_to_execution",
+     "label": "Signal to Execution",
+     "prompt": "You are the trade-execution sub-agent. Turn signals into concrete order instructions, respecting position limits, liquidity, and slippage. Output a submit-ready buy/sell plan.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "blue-chip-steady",
+   "shelf": "open",
+   "name": "Blue-Chip Steady",
+   "model_name": "anthropic/claude-haiku-4-5",
+   "description": "Buy and hold a handful of the strongest Dow companies, selling only when a position deteriorates badly. Mirrors the buy-and-hold benchmark on our leaderboard.",
+   "category": "us_stocks",
+   "tags": [
+    "buy and hold",
+    "blue chips"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "even-split-dow",
-      "shelf": "open",
-      "name": "Even-Split Dow",
-      "model_name": "anthropic/claude-haiku-4-5",
-      "description": "Spread the money evenly across all available Dow stocks and keep the split even. Mirrors the equal-weight benchmark on our leaderboard.",
-      "category": "us_stocks",
-      "tags": [
-        "equal weight",
-        "diversified"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_even_split_dow",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Spread the money evenly across all available Dow stocks and keep the split even, rebalancing when any position drifts far from its equal share.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_blue_chip_steady",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Buy and hold a handful of the strongest Dow companies. Sell only if a company's position deteriorates badly. Do not chase short-term moves.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "even-split-dow",
+   "shelf": "open",
+   "name": "Even-Split Dow",
+   "model_name": "anthropic/claude-haiku-4-5",
+   "description": "Spread the money evenly across all available Dow stocks and keep the split even. Mirrors the equal-weight benchmark on our leaderboard.",
+   "category": "us_stocks",
+   "tags": [
+    "equal weight",
+    "diversified"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "ashare-steady-t1",
-      "shelf": "open",
-      "name": "A-Share Steady (T+1)",
-      "model_name": "anthropic/claude-haiku-4-5",
-      "description": "A patient strategy for Chinese A-shares, built for that market's rule that shares bought today cannot be sold until the next trading day.",
-      "category": "cn_ashares",
-      "tags": [
-        "a-shares",
-        "patient"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_ashare_steady",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Trade the available Chinese A-share stocks patiently. Because shares bought today cannot be sold until the next trading day, avoid quick in-and-out trades; build positions you are willing to hold overnight.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_even_split_dow",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Spread the money evenly across all available Dow stocks and keep the split even, rebalancing when any position drifts far from its equal share.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "ashare-steady-t1",
+   "shelf": "open",
+   "name": "A-Share Steady (T+1)",
+   "model_name": "anthropic/claude-haiku-4-5",
+   "description": "A patient strategy for Chinese A-shares, built for that market's rule that shares bought today cannot be sold until the next trading day.",
+   "category": "cn_ashares",
+   "tags": [
+    "a-shares",
+    "patient"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "contrarian-dip-buyer",
-      "shelf": "open",
-      "name": "Contrarian Dip Buyer",
-      "model_name": "openai/gpt-5.5",
-      "description": "Buys stocks that have sold off hard and trims them back once they have recovered. The opposite instinct to a momentum strategy.",
-      "category": "us_stocks",
-      "tags": [
-        "contrarian",
-        "mean reversion"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_contrarian_dip",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Look for stocks that have fallen well below where they were trading recently and buy those, in small pieces rather than all at once. Sell back into strength once a position has recovered. Do not chase stocks that are already running.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_ashare_steady",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Trade the available Chinese A-share stocks patiently. Because shares bought today cannot be sold until the next trading day, avoid quick in-and-out trades; build positions you are willing to hold overnight.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "contrarian-dip-buyer",
+   "shelf": "open",
+   "name": "Contrarian Dip Buyer",
+   "model_name": "openai/gpt-5.5",
+   "description": "Buys stocks that have sold off hard and trims them back once they have recovered. The opposite instinct to a momentum strategy.",
+   "category": "us_stocks",
+   "tags": [
+    "contrarian",
+    "mean reversion"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "sector-rotator",
-      "shelf": "open",
-      "name": "Sector Rotator",
-      "model_name": "google/gemini-3.1-pro-preview",
-      "description": "Concentrates into whichever part of the market is leading, and moves on when leadership changes.",
-      "category": "us_stocks",
-      "tags": [
-        "rotation",
-        "trend"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_sector_rotator",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Group the available stocks by the kind of business they are in. Put most of the money into the group that has been performing best, and hold two or three names from it rather than one. When a different group takes the lead, sell out of the old one before building the new position.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_contrarian_dip",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Look for stocks that have fallen well below where they were trading recently and buy those, in small pieces rather than all at once. Sell back into strength once a position has recovered. Do not chase stocks that are already running.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "sector-rotator",
+   "shelf": "open",
+   "name": "Sector Rotator",
+   "model_name": "google/gemini-3.1-pro-preview",
+   "description": "Concentrates into whichever part of the market is leading, and moves on when leadership changes.",
+   "category": "us_stocks",
+   "tags": [
+    "rotation",
+    "trend"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "volatility-guard",
-      "shelf": "open",
-      "name": "Volatility Guard",
-      "model_name": "deepseek/deepseek-v4-pro",
-      "description": "Holds a steady portfolio in calm markets and cuts exposure when prices start swinging. Runs on the only model that has beaten the passive baselines on our leaderboard.",
-      "category": "us_stocks",
-      "tags": [
-        "risk management",
-        "defensive"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_volatility_guard",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Judge how violently prices have been moving lately compared with earlier in the period. While things are calm, stay invested across several stocks. When the swings get noticeably larger, sell part of every position and hold the cash rather than switching stocks. Rebuild the positions gradually once the market settles down. Protecting the money matters more here than catching every rally.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
-    },
+     "id": "sub_sector_rotator",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Group the available stocks by the kind of business they are in. Put most of the money into the group that has been performing best, and hold two or three names from it rather than one. When a different group takes the lead, sell out of the old one before building the new position.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "volatility-guard",
+   "shelf": "open",
+   "name": "Volatility Guard",
+   "model_name": "deepseek/deepseek-v4-pro",
+   "description": "Holds a steady portfolio in calm markets and cuts exposure when prices start swinging. Runs on the only model that has beaten the passive baselines on our leaderboard.",
+   "category": "us_stocks",
+   "tags": [
+    "risk management",
+    "defensive"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
+    {
+     "id": "sub_volatility_guard",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Judge how violently prices have been moving lately compared with earlier in the period. While things are calm, stay invested across several stocks. When the swings get noticeably larger, sell part of every position and hold the cash rather than switching stocks. Rebuild the positions gradually once the market settles down. Protecting the money matters more here than catching every rally.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
+    }
+   ]
+  },
+  {
+   "template_id": "ashare-momentum-t1",
+   "shelf": "open",
+   "name": "A-Share Momentum (T+1)",
+   "model_name": "qwen/qwen3.7-plus",
+   "description": "Rides the strongest Chinese A-shares while respecting that market's rule that shares bought today cannot be sold until the next trading day.",
+   "category": "cn_ashares",
+   "tags": [
+    "a-shares",
+    "momentum"
+   ],
+   "author": "Agentic Trading Lab",
+   "pipeline": [
     {
-      "template_id": "ashare-momentum-t1",
-      "shelf": "open",
-      "name": "A-Share Momentum (T+1)",
-      "model_name": "qwen/qwen3.7-plus",
-      "description": "Rides the strongest Chinese A-shares while respecting that market's rule that shares bought today cannot be sold until the next trading day.",
-      "category": "cn_ashares",
-      "tags": [
-        "a-shares",
-        "momentum"
-      ],
-      "author": "Agentic Trading Lab",
-      "pipeline": [
-        {
-          "id": "sub_ashare_momentum",
-          "presetKey": "simple_instruction",
-          "label": "Trading instruction",
-          "prompt": "Buy the Chinese A-shares that have been climbing most steadily and hold them while they keep leading. Shares bought today cannot be sold until the next trading day, so only buy what you are happy to still own tomorrow, and plan any exit at least a day ahead. Sell a position once it stops leading.",
-          "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
-        }
-      ]
+     "id": "sub_ashare_momentum",
+     "presetKey": "simple_instruction",
+     "label": "Trading instruction",
+     "prompt": "Buy the Chinese A-shares that have been climbing most steadily and hold them while they keep leading. Shares bought today cannot be sold until the next trading day, so only buy what you are happy to still own tomorrow, and plan any exit at least a day ahead. Sell a position once it stops leading.",
+     "outputFormat": "JSON: { \"orders\": [{ \"symbol\": \"...\", \"side\": \"buy|sell|hold\", \"qty\": number, \"order_type\": \"market|limit\", \"limit_price\": number|null, \"reason\": \"...\" }] }"
     }
-  ]
+   ]
+  },
+  {
+   "template_id": "shell-company-screening",
+   "shelf": "research",
+   "name": "Shell Company Screening Agent",
+   "description": "Deep Research agent that identifies and evaluates listed shell-company candidates for reverse mergers and other M&A transactions. Fill in a client mandate, get a structured, evidence-backed screening report.",
+   "tags": [
+    "M&A",
+    "deep research",
+    "screening"
+   ],
+   "author": "Lin-Feihan",
+   "research": {
+    "agent_id": "shell-company-screening",
+    "service_base_url_env": "RESEARCH_SHELL_SERVICE_URL",
+    "service_base_url_default": "http://127.0.0.1:9100",
+    "estimated_runtime_seconds": 300,
+    "max_runtime_seconds": 1800,
+    "output_formats": [
+     "markdown",
+     "docx",
+     "pdf",
+     "evidence_json"
+    ]
+   },
+   "repo_url": "https://github.com/Lin-Feihan/shell-company-screening-agent"
+  },
+  {
+   "template_id": "due-diligence-agent",
+   "shelf": "research",
+   "name": "Due Diligence Agent",
+   "description": "Deep Research agent for public-source corporate due diligence: planning, multi-dimensional investigation, risk assessment, valuation and deal-impact analysis, delivered as a standalone DD report.",
+   "tags": [
+    "M&A",
+    "deep research",
+    "due diligence"
+   ],
+   "author": "Lin-Feihan",
+   "research": {
+    "agent_id": "due-diligence-agent",
+    "service_base_url_env": "RESEARCH_DD_SERVICE_URL",
+    "service_base_url_default": "http://127.0.0.1:9100",
+    "estimated_runtime_seconds": 300,
+    "max_runtime_seconds": 1800,
+    "output_formats": [
+     "markdown",
+     "pdf",
+     "evidence_json"
+    ]
+   },
+   "repo_url": "https://github.com/Lin-Feihan/due-diligence-agent"
+  }
+ ]
 }
diff --git a/dashboard/frontend/app.html b/dashboard/frontend/app.html
index 2d17b152..245a4b67 100644
--- a/dashboard/frontend/app.html
+++ b/dashboard/frontend/app.html
@@ -13,7 +13,7 @@
          because every API call is a CORS request. -->
     <link rel="preconnect" href="https://agentictrading.onrender.com" crossorigin>
     <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=JetBrains+Mono:wght@400;500;700&display=swap" rel="stylesheet">
-    <link rel="stylesheet" href="styles.css?v=144">
+    <link rel="stylesheet" href="styles.css?v=145">
     <!-- defer: ~200KB that was render-blocking. Deferred scripts execute in
          document order (this one first, the body-end ones after), all before
          DOMContentLoaded, so Chart is defined before any chart renders. -->
@@ -189,6 +189,7 @@
       <symbol id="icon-bot" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round"><path d="M12 8V4H8"/><rect width="16" height="12" x="4" y="8" rx="2"/><path d="M2 14h2"/><path d="M20 14h2"/><path d="M15 13v2"/><path d="M9 13v2"/></symbol>
       <symbol id="icon-calendar" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round"><path d="M8 2v4"/><path d="M16 2v4"/><rect width="18" height="18" x="3" y="4" rx="2"/><path d="M3 10h18"/></symbol>
       <symbol id="icon-users" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round"><path d="M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2"/><circle cx="9" cy="7" r="4"/><path d="M22 21v-2a4 4 0 0 0-3-3.87"/><path d="M16 3.13a4 4 0 0 1 0 7.75"/></symbol>
+      <symbol id="icon-file-text" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8Z"/><path d="M14 2v6h6"/><path d="M16 13H8"/><path d="M16 17H8"/><path d="M10 9H8"/></symbol>
       <symbol id="icon-activity" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round"><path d="M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l-2.35 8.36A2 2 0 0 1 4.49 12H2"/></symbol>
       <symbol id="icon-flask" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round"><path d="M10 2v7.527a2 2 0 0 1-.211.896L4.72 20.55a1 1 0 0 0 .9 1.45h12.76a1 1 0 0 0 .9-1.45l-5.069-10.127A2 2 0 0 1 14 9.527V2"/><path d="M8.5 2h7"/><path d="M7 16h10"/></symbol>
       <symbol id="icon-shield-check" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round"><path d="M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z"/><path d="m9 12 2 2 4-4"/></symbol>
@@ -1031,6 +1032,22 @@ <h3 class="agents-category-title">Open Agents</h3>
                         <div id="agentsGridFooterOpen" class="agents-grid-footer" hidden></div>
                         <p id="agentsEmptyOpen" class="control-helper" hidden>No open agents yet. Add a ready-made strategy like AI Hedge Fund from Community.</p>
                     </section>
+
+                    <section class="agents-category" data-category="research">
+                        <div class="agents-category-head">
+                            <div class="agents-category-heading">
+                                <span class="agents-category-icon" aria-hidden="true">
+                                    <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8Z"/><path d="M14 2v6h6"/><path d="M16 13H8"/><path d="M16 17H8"/><path d="M10 9H8"/></svg>
+                                </span>
+                                <h3 class="agents-category-title">Research Agents</h3>
+                                <span id="agentsCountResearch" class="agents-category-count" hidden></span>
+                            </div>
+                            <p class="agents-category-sub">Deep Research agents that produce analyst reports. Add them from Community, fill in a mandate, and get a downloadable report.</p>
+                        </div>
+                        <div id="agentsGridResearch" class="agents-grid"></div>
+                        <p id="agentsEmptyResearch" class="control-helper" hidden>No research agents yet. Add one from Community.</p>
+                    </section>
+
                     <!-- Locked shelves. Crypto is a display-only price ticker
                          (quotes.py, never wired to the engine) and futures exist
                          nowhere in the repo, so these rows are inert on purpose:
@@ -1766,6 +1783,43 @@ <h3 class="sidebar-title compact">Entry Details</h3>
 
     <!-- Community -- Agent Supermarket is its first section, not the whole page:
          the nav button says "Community", so the page has to say it too. -->
+    
+    <!-- Research workbench (design N2/PR2): the "point open" surface for a
+         research agent cloned from Community. A JS-toggled view, not a nav
+         page — it always opens from a My Agents card, so Back returns there. -->
+    <div id="researchWorkbenchView" class="page-view research-workbench-view" style="display: none;">
+        <div class="research-workbench">
+            <button id="researchBackBtn" class="research-back-btn" type="button">← Back to My Agents</button>
+            <div class="research-agent-head">
+                <h2 id="researchAgentName">—</h2>
+                <p id="researchAgentDesc" class="research-agent-desc"></p>
+                <p id="researchAgentMeta" class="research-agent-meta"></p>
+            </div>
+            <div class="research-columns">
+                <section class="research-form-card">
+                    <h3>New research run</h3>
+                    <form id="researchRunForm">
+                        <div id="researchFormFields"></div>
+                        <label class="research-email-row"><input type="checkbox" id="researchEmailMe" checked> Email me when the report is ready</label>
+                        <p id="researchFormError" class="research-error" hidden></p>
+                        <button id="researchSubmitBtn" class="auth-btn auth-btn-primary" type="submit">Start research</button>
+                    </form>
+                </section>
+                <section class="research-side">
+                    <h3>Runs</h3>
+                    <div id="researchRunsList" class="research-runs-list"><p class="control-helper">Loading…</p></div>
+                </section>
+            </div>
+            <section id="researchResultArea" class="research-result" hidden>
+                <div class="research-result-head">
+                    <h3>Report</h3>
+                    <div id="researchDownloadBtns" class="research-download-btns"></div>
+                </div>
+                <article id="researchReportBody" class="research-report-body"></article>
+            </section>
+        </div>
+    </div>
+
     <div id="communityView" class="page-view community-view" style="display: none;">
         <div class="page-header">
             <div>
diff --git a/dashboard/frontend/app.js b/dashboard/frontend/app.js
index 30a8a1cb..0922ac1c 100644
--- a/dashboard/frontend/app.js
+++ b/dashboard/frontend/app.js
@@ -1655,6 +1655,9 @@ function applyAgentFilters(resetPagination = true) {
     agentGridPage = Object.fromEntries(AGENT_SHELVES.map((shelf) => [shelf.key, 0]));
   }
   renderAgentCategories(getFilteredAgents());
+  // Research Agents is a sibling shelf whose rows come from the research API,
+  // not from /agents — refreshed on the same trigger.
+  if (typeof renderResearchShelf === 'function') renderResearchShelf();
 }
 
 function setAgentViewMode(mode) {
@@ -2859,6 +2862,7 @@ let marketplaceContestMeta = {
 const MARKETPLACE_SHELVES = [
   { key: 'llms', title: 'LLMs', sub: 'LLMs tested on the ATL leaderboard' },
   { key: 'open', title: 'Agents', sub: 'Ready-made trading agents' },
+  { key: 'research', title: 'Research Agents', sub: 'Deep Research agents that produce analyst reports' },
 ];
 /** 'all' or one of MARKET_LABELS' keys. Set by the chip row and by the Prompted
  * Models shelf's empty-state Community button (via navigateToPage's options). */
@@ -2989,7 +2993,7 @@ function marketplaceEmptyHtml({ searching, categoryFilter }) {
 
 function templateMarketplaceShelf(template) {
   const explicit = String(template?.shelf || '').toLowerCase();
-  if (explicit === 'llms' || explicit === 'open') return explicit;
+  if (explicit === 'llms' || explicit === 'open' || explicit === 'research') return explicit;
   // Mirrors the backend's _normalize_shelf fallback: any hosted runtime is an
   // open agent, with no per-runtime special case to remember.
   return template?.mode === 'runtime' ? 'open' : 'llms';
@@ -3273,7 +3277,50 @@ function marketplaceRepoLabel(template) {
 }
 
 /** Compact leaderboard-first card. Shared by both supermarket shelves. */
+function buildResearchMarketplaceCardHtml(template) {
+  // Mirrors the trading card's wrapper classes (agent-card / agent-card-cta /
+  // agent-card-actions) — the blue CTA and card chrome hang off those, and a
+  // bare button with marketplace-clone-btn alone renders unstyled.
+  const research = template.research || {};
+  const runtimeMin = Math.max(1, Math.round((research.estimated_runtime_seconds || 300) / 60));
+  const formats = (research.output_formats || []).join(' / ') || 'Markdown';
+  const description = String(template.description || '').trim();
+  const repoUrl = String(template.repo_url || '').trim();
+  const repoLabel = marketplaceRepoLabel(template);
+  const repoExtra = repoUrl
+    ? `<a class="marketplace-repo-btn" href="${escapeHtml(repoUrl)}" target="_blank" rel="noopener noreferrer" aria-label="Open ${escapeHtml(repoLabel)} on GitHub">
+            <svg class="ui-icon marketplace-repo-icon" aria-hidden="true"><use href="#icon-github"></use></svg>
+            <span>${escapeHtml(repoLabel)}</span>
+          </a>`
+    : '';
+  return `
+    <div class="section-card agent-card marketplace-card marketplace-card--research">
+      <div class="agent-card-top">
+        <div class="agent-card-identity">
+          ${agentRobotIcon()}
+          <div class="agent-card-identity-text">
+            <h3 class="agent-name">${escapeHtml(template.name)}</h3>
+            <p class="agent-card-submeta">${escapeHtml(template.author || 'Community')} · Deep Research</p>
+          </div>
+        </div>
+        <span class="marketplace-mode-chip">Research</span>
+      </div>
+      ${description ? `<p class="marketplace-card-description">${escapeHtml(description)}</p>` : ''}
+      ${repoExtra}
+      <div class="research-card-facts">
+        <span><svg class="ui-icon research-fact-icon" aria-hidden="true"><use href="#icon-clock"></use></svg> ~${runtimeMin} min per run</span>
+        <span><svg class="ui-icon research-fact-icon" aria-hidden="true"><use href="#icon-file-text"></use></svg> ${escapeHtml(formats)}</span>
+      </div>
+      <div class="agent-card-actions agent-card-actions--status">
+        <button class="agent-card-cta marketplace-clone-btn" type="button" data-template-id="${escapeHtml(template.template_id)}"${researchAddedIds.has(template.template_id) ? ' disabled' : ''}>${researchAddedIds.has(template.template_id) ? 'Added ✓' : 'Add to My Agents'}</button>
+      </div>
+    </div>`;
+}
+
 function buildMarketplaceCardHtml(template) {
+  if (templateMarketplaceShelf(template) === 'research') {
+    return buildResearchMarketplaceCardHtml(template);
+  }
   const stats = marketplacePerformanceFor(template);
   const isOpen = templateMarketplaceShelf(template) === 'open';
   const cloneLabel = 'Add to My Agents';
@@ -3422,6 +3469,23 @@ function renderMarketplaceGrid() {
       btn.disabled = true;
       const prevLabel = btn.textContent;
       btn.textContent = 'Adding…';
+      if (templateMarketplaceShelf(template) === 'research') {
+        try {
+          const data = await API.post(`${API_BASE}/api/v1/research/agents/${encodeURIComponent(templateId)}/add`, {});
+          researchAddedIds.add(templateId);
+          btn.textContent = 'Added ✓';
+          if (typeof showAppToast === 'function') {
+            showAppToast(data.created
+              ? 'Added to My Agents — open it there to start a research run.'
+              : 'Already in My Agents — open it there to start a research run.');
+          }
+        } catch (error) {
+          alert(error.message || `Couldn't add this template. Please try again.`);
+        } finally {
+          marketplaceCloneInFlight = false;
+        }
+        return;
+      }
       try {
         await cloneMarketplaceTemplate(template);
       } catch (error) {
@@ -3491,6 +3555,15 @@ async function loadMarketplaceLeaderboard() {
  */
 async function loadMarketplace() {
   loadMarketplaceLeaderboard();
+  // Research cards need their added-state for the button, so fetch the
+  // research shelf's add-state alongside the static catalog.
+  (async () => {
+    try {
+      const data = await API.get(`${RESEARCH_API}/agents`);
+      researchAddedIds = new Set((data.agents || []).filter((a) => a.added).map((a) => a.template_id));
+      renderMarketplaceGrid();
+    } catch (_error) { /* guest: buttons stay as plain Add */ }
+  })();
   if (marketplaceTemplates.length) {
     renderMarketplaceGrid();
     return;
@@ -6558,7 +6631,14 @@ document.addEventListener('DOMContentLoaded', async () => {
     // The HttpOnly session cookie is invisible to JS, so the boot signal is
     // the cached auth-user (written on every cookie sign-in) or a pre-cookie
     // legacy localStorage token (upgraded to a cookie by the /me bridge).
-    if (localStorage.getItem(AUTH_TOKEN_KEY) || getStoredAuthUser()) {
+    // Local console (design N2/PR2 local verification): the launchd service
+    // runs with ATL_LOCAL_AUTOLOGIN_EMAIL, so /me answers as the local admin
+    // for every loopback request. Probe unconditionally on loopback — the
+    // "cached auth-user only" shortcut would keep a fresh browser a guest
+    // forever, because there is nothing cached to trigger the first probe.
+    // The hostname gate keeps prod boot (cold-start /me skip) untouched.
+    const localConsole = ['127.0.0.1', 'localhost'].includes(window.location.hostname);
+    if (localStorage.getItem(AUTH_TOKEN_KEY) || getStoredAuthUser() || localConsole) {
         try {
             await refreshAuthUser();
         } catch (error) {
@@ -11352,6 +11432,7 @@ function navigateToPage(page, options = {}) {
     hide(myAlgoView);
     hide(leaderboardView);
     hide(document.getElementById('playgroundAgentsPanel'));
+    hide(document.getElementById('researchWorkbenchView'));
     hide(document.getElementById('competitionParticipantsPanel'));
     hide(document.getElementById('competitionAboutPanel'));
 
@@ -12890,3 +12971,333 @@ async function executeMyTradingAlgo() {
 }
 
 console.log('Frontend loaded - connecting to API at ' + API_BASE);
+
+// ============================================================================
+// Research agents (design N2/PR2) — Community shelf actions, the My Agents
+// "Research Agents" shelf, and the workbench (dynamic form → run → report).
+// ============================================================================
+
+const RESEARCH_API = `${API_BASE}/api/v1/research`;
+let researchAddedIds = new Set();
+let researchWorkbenchTemplateId = null;
+let researchWorkbenchReturnView = null;
+let researchPollTimer = null;
+let researchSubmitInFlight = false;
+
+function stopResearchPolling() {
+  if (researchPollTimer) {
+    clearInterval(researchPollTimer);
+    researchPollTimer = null;
+  }
+}
+
+async function fetchResearchAgents() {
+  const data = await API.get(`${RESEARCH_API}/agents`);
+  return Array.isArray(data?.agents) ? data.agents : [];
+}
+
+function researchStatusLabel(status) {
+  return { queued: 'Queued', running: 'Running…', completed: 'Completed', failed: 'Failed' }[status] || status;
+}
+
+/** The My Agents "Research Agents" shelf: cards for cloned research agents. */
+async function renderResearchShelf() {
+  const grid = document.getElementById('agentsGridResearch');
+  const emptyEl = document.getElementById('agentsEmptyResearch');
+  const countEl = document.getElementById('agentsCountResearch');
+  if (!grid) return;
+  let agents = [];
+  try {
+    agents = await fetchResearchAgents();
+  } catch (error) {
+    // A 401 here just means the visitor is not signed in — the shelf hides
+    // rather than advertising an error for a surface guests cannot use.
+    grid.innerHTML = '';
+    if (emptyEl) emptyEl.hidden = true;
+    const section = grid.closest('.agents-category');
+    if (section) section.style.display = error?.status === 401 ? 'none' : '';
+    return;
+  }
+  const added = agents.filter((agent) => agent.added);
+  if (countEl) {
+    countEl.textContent = String(added.length);
+    countEl.hidden = added.length === 0;
+  }
+  if (!added.length) {
+    grid.innerHTML = '';
+    if (emptyEl) {
+      emptyEl.hidden = false;
+      emptyEl.innerHTML = 'No research agents yet. Add one from <button type="button" class="link-btn" data-goto-community>Community</button>.';
+    }
+    grid.querySelectorAll('[data-goto-community]').forEach((btn) => {
+      btn.addEventListener('click', () => navigateToPage('community'));
+    });
+    return;
+  }
+  if (emptyEl) emptyEl.hidden = true;
+  grid.innerHTML = added.map((agent) => {
+    const runtimeMin = Math.max(1, Math.round(((agent.research || {}).estimated_runtime_seconds || 300) / 60));
+    return `
+      <article class="section-card agent-card research-agent-card" data-template-id="${escapeHtml(agent.template_id)}">
+        <div class="research-agent-card-head">
+          <h4>${escapeHtml(agent.name)}</h4>
+          <span class="marketplace-mode-chip">Research</span>
+        </div>
+        <p class="research-agent-card-desc">${escapeHtml(String(agent.description || '').slice(0, 140))}</p>
+        <p class="research-agent-card-meta">
+          <svg class="ui-icon research-fact-icon" aria-hidden="true"><use href="#icon-clock"></use></svg> ~${runtimeMin} min ·
+          <svg class="ui-icon research-fact-icon" aria-hidden="true"><use href="#icon-file-text"></use></svg> ${escapeHtml(((agent.research || {}).output_formats || []).join(' / '))}
+        </p>
+        <button type="button" class="auth-btn auth-btn-primary research-open-btn">Open workbench</button>
+      </article>`;
+  }).join('');
+  grid.querySelectorAll('.research-agent-card').forEach((card) => {
+    card.querySelector('.research-open-btn')?.addEventListener('click', () => {
+      openResearchWorkbench(card.dataset.templateId);
+    });
+  });
+}
+
+/** The Community research card's Add action (separate from the trading clone:
+ * no runtime copy, no cash allocation — just the user→template link). */
+async function addResearchAgentFromCommunity(templateId) {
+  await API.post(`${RESEARCH_API}/agents/${encodeURIComponent(templateId)}/add`, {});
+  await renderResearchShelf();
+}
+
+/** Minimal, safe Markdown renderer for research reports: escape first, then a
+ * bounded subset (headings, bold/italic/code, links, lists, blockquote, hr,
+ * pipe tables). Enough for the Deep Research report shape; never raw HTML. */
+function renderSimpleMarkdown(markdown) {
+  const lines = String(markdown || '').replace(/\r\n/g, '\n').split('\n');
+  const esc = (s) => escapeHtml(s);
+  const inline = (t) => esc(t)
+    .replace(/\*\*([^*]+)\*\*/g, '<strong>$1</strong>')
+    .replace(/(^|[^*])\*([^*\n]+)\*/g, '$1<em>$2</em>')
+    .replace(/`([^`]+)`/g, '<code>$1</code>')
+    .replace(/\[([^\]]+)\]\((https?:[^)\s]+)\)/g, '<a href="$2" target="_blank" rel="noopener noreferrer">$1</a>');
+  let html = '';
+  let tableRows = [];
+  const flushTable = () => {
+    if (!tableRows.length) return;
+    const [head, ...rest] = tableRows;
+    html += '<table class="research-md-table"><thead><tr>'
+      + head.map((c) => `<th>${inline(c)}</th>`).join('')
+      + '</tr></thead><tbody>'
+      + rest.map((row) => `<tr>${row.map((c) => `<td>${inline(c)}</td>`).join('')}</tr>`).join('')
+      + '</tbody></table>';
+    tableRows = [];
+  };
+  for (const raw of lines) {
+    const line = raw.trimEnd();
+    if (/^\|.*\|$/.test(line.trim())) {
+      const cells = line.trim().slice(1, -1).split('|').map((c) => c.trim());
+      if (cells.every((c) => /^:?-{3,}:?$/.test(c))) continue;
+      tableRows.push(cells);
+      continue;
+    }
+    flushTable();
+    if (!line.trim()) continue;
+    const heading = /^(#{1,4})\s+(.*)$/.exec(line);
+    if (heading) {
+      const level = Math.min(heading[1].length + 1, 5);
+      html += `<h${level}>${inline(heading[2])}</h${level}>`;
+      continue;
+    }
+    if (/^(-{3,}|\*{3,})$/.test(line.trim())) { html += '<hr>'; continue; }
+    const bullet = /^[-*]\s+(.*)$/.exec(line);
+    if (bullet) { html += `<li>${inline(bullet[1])}</li>`; continue; }
+    const ordered = /^\d+[.)]\s+(.*)$/.exec(line);
+    if (ordered) { html += `<li>${inline(ordered[1])}</li>`; continue; }
+    const quote = /^>\s?(.*)$/.exec(line);
+    if (quote) { html += `<blockquote>${inline(quote[1])}</blockquote>`; continue; }
+    html += `<p>${inline(line)}</p>`;
+  }
+  flushTable();
+  return html;
+}
+
+function showResearchWorkbench() {
+  const view = document.getElementById('researchWorkbenchView');
+  if (!view) return;
+  const playground = document.getElementById('playgroundView');
+  researchWorkbenchReturnView = playground && playground.style.display === 'block' ? playground : null;
+  if (playground) playground.style.display = 'none';
+  const backtestPanel = document.querySelector('.playground-backtest-panel')
+    || document.querySelector('.main-container');
+  if (backtestPanel) backtestPanel.style.display = 'none';
+  view.style.display = 'block';
+  window.scrollTo(0, 0);
+}
+
+function hideResearchWorkbench() {
+  stopResearchPolling();
+  const view = document.getElementById('researchWorkbenchView');
+  if (view) view.style.display = 'none';
+  if (researchWorkbenchReturnView) {
+    researchWorkbenchReturnView.style.display = 'block';
+    researchWorkbenchReturnView = null;
+  }
+}
+
+async function openResearchWorkbench(templateId) {
+  stopResearchPolling();
+  researchWorkbenchTemplateId = templateId;
+  showResearchWorkbench();
+  const nameEl = document.getElementById('researchAgentName');
+  const descEl = document.getElementById('researchAgentDesc');
+  const metaEl = document.getElementById('researchAgentMeta');
+  const fieldsEl = document.getElementById('researchFormFields');
+  const resultArea = document.getElementById('researchResultArea');
+  const runsList = document.getElementById('researchRunsList');
+  nameEl.textContent = 'Loading…';
+  descEl.textContent = '';
+  metaEl.textContent = '';
+  fieldsEl.innerHTML = '';
+  resultArea.hidden = true;
+  runsList.innerHTML = '<p class="control-helper">Loading…</p>';
+
+  let manifest;
+  try {
+    manifest = await API.get(`${RESEARCH_API}/agents/${encodeURIComponent(templateId)}/manifest`);
+  } catch (error) {
+    nameEl.textContent = 'Could not load this research agent';
+    descEl.textContent = error.message || '';
+    return;
+  }
+
+  nameEl.textContent = manifest.name || templateId;
+  descEl.textContent = manifest.description || '';
+  const runtimeMin = Math.max(1, Math.round((manifest.estimated_runtime_seconds || 300) / 60));
+  metaEl.innerHTML =
+    '<svg class="ui-icon research-fact-icon" aria-hidden="true"><use href="#icon-clock"></use></svg> '
+    + `~${runtimeMin} min per run · `
+    + '<svg class="ui-icon research-fact-icon" aria-hidden="true"><use href="#icon-file-text"></use></svg> '
+    + `Output: ${escapeHtml((manifest.output_formats || []).join(' / '))}`;
+
+  fieldsEl.innerHTML = (manifest.settings_schema?.fields || []).map((field) => {
+    const value = field.default != null ? String(field.default) : '';
+    const requiredMark = field.required ? ' <span class="research-required">*</span>' : '';
+    let control;
+    if (field.type === 'longtext') {
+      control = `<textarea id="rf_${escapeHtml(field.id)}" rows="3" placeholder="${escapeHtml(field.placeholder || '')}">${escapeHtml(value)}</textarea>`;
+    } else if (field.type === 'select') {
+      control = `<select id="rf_${escapeHtml(field.id)}">${(field.options || []).map((option) => `<option value="${escapeHtml(option)}"${option === value ? ' selected' : ''}>${escapeHtml(option)}</option>`).join('')}</select>`;
+    } else {
+      control = `<input id="rf_${escapeHtml(field.id)}" type="${field.type === 'date' ? 'date' : field.type === 'number' ? 'number' : 'text'}" value="${escapeHtml(value)}" placeholder="${escapeHtml(field.placeholder || '')}">`;
+    }
+    return `<label class="research-field"><span>${escapeHtml(field.label || field.id)}${requiredMark}</span>${control}<small class="research-field-desc">${escapeHtml(field.description || '')}</small></label>`;
+  }).join('');
+
+  const form = document.getElementById('researchRunForm');
+  if (!form.dataset.bound) {
+    form.dataset.bound = '1';
+    form.addEventListener('submit', (event) => {
+      event.preventDefault();
+      submitResearchRun();
+    });
+  }
+  document.getElementById('researchBackBtn').onclick = hideResearchWorkbench;
+  await loadResearchRuns(templateId);
+}
+
+async function loadResearchRuns(templateId) {
+  const runsList = document.getElementById('researchRunsList');
+  if (!runsList) return;
+  try {
+    const data = await API.get(`${RESEARCH_API}/runs`);
+    const runs = (data.runs || []).filter((run) => run.template_id === templateId);
+    if (!runs.length) {
+      runsList.innerHTML = '<p class="control-helper">No runs yet.</p>';
+      return;
+    }
+    runsList.innerHTML = runs.map((run) => `
+      <div class="research-run-row" data-run-id="${escapeHtml(run.run_id)}">
+        <span class="research-run-status is-${escapeHtml(run.status)}">${escapeHtml(researchStatusLabel(run.status))}</span>
+        <span class="research-run-date">${escapeHtml(String(run.created_at || '').slice(0, 16))}</span>
+      </div>`).join('');
+    runsList.querySelectorAll('.research-run-row').forEach((row) => {
+      row.addEventListener('click', () => showCompletedResearchReport(row.dataset.runId));
+    });
+  } catch (error) {
+    runsList.innerHTML = `<p class="control-helper">${escapeHtml(error.message || 'Runs could not be loaded.')}</p>`;
+  }
+}
+
+async function showCompletedResearchReport(runId) {
+  const resultArea = document.getElementById('researchResultArea');
+  const body = document.getElementById('researchReportBody');
+  const downloadBtns = document.getElementById('researchDownloadBtns');
+  resultArea.hidden = false;
+  body.innerHTML = '<p class="control-helper">Loading report…</p>';
+  downloadBtns.innerHTML = '';
+  try {
+    const data = await API.get(`${RESEARCH_API}/runs/${encodeURIComponent(runId)}/report`);
+    body.innerHTML = renderSimpleMarkdown(data.report_markdown);
+    const kinds = [['markdown', 'Markdown'], ['docx', 'Word'], ['pdf', 'PDF'], ['evidence_json', 'Evidence JSON']];
+    downloadBtns.innerHTML = kinds
+      .map(([kind, label]) => `<a class="auth-btn auth-btn-secondary" href="${RESEARCH_API}/runs/${encodeURIComponent(runId)}/artifacts/${kind}" download>${label}</a>`)
+      .join('');
+  } catch (error) {
+    body.innerHTML = `<p class="control-helper">${escapeHtml(error.message || 'Report could not be loaded.')}</p>`;
+  }
+}
+
+async function submitResearchRun() {
+  if (researchSubmitInFlight || !researchWorkbenchTemplateId) return;
+  const templateId = researchWorkbenchTemplateId;
+  const errorEl = document.getElementById('researchFormError');
+  const submitBtn = document.getElementById('researchSubmitBtn');
+  const settings = {};
+  document.querySelectorAll('#researchFormFields [id^="rf_"]').forEach((input) => {
+    const key = input.id.slice(3);
+    const value = String(input.value || '').trim();
+    if (value) settings[key] = value;
+  });
+  const emailMe = document.getElementById('researchEmailMe')?.checked || false;
+  researchSubmitInFlight = true;
+  if (submitBtn) submitBtn.disabled = true;
+  errorEl.hidden = true;
+  try {
+    const data = await API.post(
+      `${RESEARCH_API}/agents/${encodeURIComponent(templateId)}/runs`,
+      { settings, email_me: emailMe },
+    );
+    errorEl.hidden = false;
+    errorEl.textContent = 'Research started — this usually takes a few minutes. You can keep this page open.';
+    const runId = data.run_id;
+    await loadResearchRuns(templateId);
+    stopResearchPolling();
+    researchPollTimer = setInterval(async () => {
+      try {
+        const status = await API.get(`${RESEARCH_API}/runs/${encodeURIComponent(runId)}`);
+        if (status.status === 'completed') {
+          stopResearchPolling();
+          await loadResearchRuns(templateId);
+          await showCompletedResearchReport(runId);
+        } else if (status.status === 'failed') {
+          stopResearchPolling();
+          errorEl.hidden = false;
+          errorEl.textContent = status.error || 'The research run failed.';
+        }
+      } catch (_error) { /* transient — next tick retries */ }
+    }, 10000);
+  } catch (error) {
+    const detail = error?.message || 'Submit failed.';
+    errorEl.hidden = false;
+    errorEl.textContent = typeof detail === 'string' ? detail : 'Submit failed.';
+    try {
+      const fieldErrors = error?.field_errors || {};
+      const first = Object.keys(fieldErrors)[0];
+      if (first) {
+        const input = document.getElementById(`rf_${first}`);
+        input?.focus();
+        errorEl.textContent = `${first}: ${fieldErrors[first]}`;
+      }
+    } catch (_ignored) { /* keep the generic message */ }
+  } finally {
+    researchSubmitInFlight = false;
+    if (submitBtn) submitBtn.disabled = false;
+  }
+}
+
diff --git a/dashboard/frontend/styles.css b/dashboard/frontend/styles.css
index ccaf5412..984ab3d4 100644
--- a/dashboard/frontend/styles.css
+++ b/dashboard/frontend/styles.css
@@ -13835,3 +13835,48 @@ table.sr-only {
         padding: 14px 16px;
     }
 }
+
+/* ===== Research agents (design N2/PR2) ===== */
+.research-card-facts{display:flex;flex-wrap:wrap;gap:8px 18px;margin:10px 0 0;color:var(--text-muted, #94a3b8);font-size:12.5px}
+.research-back-btn{margin-bottom:14px}
+.research-agent-head h2{margin-bottom:6px}
+.research-agent-desc,.research-agent-meta{margin:0 0 4px;color:var(--text-muted, #94a3b8)}
+.research-columns{display:grid;grid-template-columns:minmax(0,1.2fr) minmax(240px,0.8fr);gap:26px;margin-top:18px}
+.research-form-card,.research-side{background:rgba(15,27,55,.5);border:1px solid rgba(148,163,184,.16);border-radius:12px;padding:20px}
+.research-form-card h3,.research-side h3{margin:0 0 14px;font-size:15px}
+.research-field{display:grid;gap:6px;margin-bottom:14px}
+.research-field span{font-size:12.5px;color:var(--text-muted, #94a3b8)}
+.research-field input,.research-field textarea,.research-field select{width:100%;padding:9px 11px;border-radius:8px;border:1px solid rgba(148,163,184,.28);background:rgba(2,8,23,.45);color:inherit;font:inherit}
+.research-field textarea{resize:vertical}
+.research-field-desc{font-size:11px;opacity:.65}
+.research-required{color:#f08080}
+.research-email-row{display:flex;align-items:center;gap:8px;margin:4px 0 14px;font-size:13px;color:var(--text-muted, #94a3b8)}
+.research-error{color:#f08080;font-size:12.5px;margin:0 0 10px}
+.research-runs-list{display:grid;gap:8px}
+.research-run-row{display:flex;align-items:center;justify-content:space-between;gap:10px;padding:9px 12px;border:1px solid rgba(148,163,184,.14);border-radius:8px;cursor:pointer;font-size:12.5px}
+.research-run-row:hover{border-color:rgba(103,232,249,.4)}
+.research-run-status.is-completed{color:#42cc8b}
+.research-run-status.is-running,.research-run-status.is-queued{color:#e6b75c}
+.research-run-status.is-failed{color:#f08080}
+.research-run-date{opacity:.6;font-variant-numeric:tabular-nums}
+.research-result{margin-top:26px}
+.research-result-head{display:flex;align-items:center;justify-content:space-between;gap:16px;margin-bottom:12px}
+.research-download-btns{display:flex;gap:8px;flex-wrap:wrap}
+.research-download-btns .auth-btn{text-decoration:none;display:inline-flex;align-items:center}
+.research-report-body{background:rgba(15,27,55,.5);border:1px solid rgba(148,163,184,.16);border-radius:12px;padding:26px 30px;line-height:1.65}
+.research-report-body h1,.research-report-body h2,.research-report-body h3,.research-report-body h4{margin:1.1em 0 .4em;line-height:1.25}
+.research-report-body h1{font-size:24px}.research-report-body h2{font-size:19px}.research-report-body h3{font-size:16px}
+.research-report-body table{width:100%;border-collapse:collapse;margin:12px 0;font-size:13px}
+.research-report-body th,.research-report-body td{border:1px solid rgba(148,163,184,.22);padding:7px 10px;text-align:left}
+.research-report-body code{background:rgba(2,8,23,.6);padding:2px 6px;border-radius:4px;font-size:12.5px}
+.research-report-body blockquote{margin:10px 0;padding:6px 14px;border-left:3px solid rgba(103,232,249,.5);opacity:.85}
+.research-report-body a{color:#67e8f9}
+.research-agent-card{cursor:default;display:grid;gap:8px}
+.research-agent-card-head{display:flex;align-items:center;justify-content:space-between;gap:10px}
+.research-agent-card-head h4{margin:0;font-size:15px}
+.research-agent-card-desc{margin:0;font-size:12.5px;color:var(--text-muted, #94a3b8)}
+.research-agent-card-meta{margin:0;font-size:11.5px;opacity:.7}
+.research-open-btn{margin-top:4px}
+@media(max-width:900px){.research-columns{grid-template-columns:1fr}}
+.marketplace-card--research{border-color:rgba(103,232,249,.25)}
+.research-fact-icon{width:14px;height:14px;vertical-align:-2px;margin-right:2px}
diff --git a/docs/integrations/research-agent-contract.md b/docs/integrations/research-agent-contract.md
new file mode 100644
index 00000000..2c78f94a
--- /dev/null
+++ b/docs/integrations/research-agent-contract.md
@@ -0,0 +1,234 @@
+# 研报 Agent 接入契约（v1.0-draft）
+
+> 双方：**ATL 平台**（Agentic Trading Lab 后端，调用方）与 **研报 Agent 服务**（Lin-Feihan 的
+> Shell Company Screening / Due Diligence Agent，被调用方）。
+> 本文档是两边开发的唯一依据；定稿后改动需双方在群里确认并更新版本号。
+>
+> 状态：**draft，待双方确认** · 2026-09-18
+
+---
+
+## 0. 总览
+
+- Agent 服务以 **HTTP + JSON** 暴露 4 个端点，部署后给 ATL 一个 **base URL**（如
+  `https://xxx.onrender.com`）和一个 **服务间密钥**（任意随机串，双方各存一份）。
+- ATL 每个请求带请求头 `X-Service-Token: <密钥>`；密钥不对返回 `401`。
+- 除本契约 4 个端点外，ATL 不调用服务的任何其他接口。
+
+调用时序：
+
+```
+ATL ── GET  /manifest            ──▶ 启动时/打开表单时拉取（可缓存）
+ATL ── POST /runs   {表单值}      ──▶ 服务后台开始研究，立即返回 run_id
+ATL ── GET  /runs/{id}           ──▶ 每 15 秒轮询一次状态
+ATL ── GET  /runs/{id}/result    ──▶ status=completed 后取结果
+```
+
+---
+
+## 1. `GET /manifest` — Agent 自我介绍 + 表单说明
+
+**响应 200：**
+
+```json
+{
+  "agent_id": "shell-company-screening",
+  "name": "Shell Company Screening Agent",
+  "description": "Deep Research agent for identifying and evaluating listed shell company candidates in M&A transactions.",
+  "category": "M&A Research",
+  "provider_note": "Runs on OpenAI / OpenRouter / Gemini / Perplexity Deep Research (configured server-side).",
+  "estimated_runtime_seconds": 300,
+  "max_runtime_seconds": 1800,
+  "output_formats": ["markdown", "docx", "pdf", "evidence_json"],
+  "settings_schema": {
+    "fields": [
+      {
+        "id": "client_name",
+        "label": "Client Name",
+        "type": "text",
+        "required": true,
+        "default": null,
+        "placeholder": "Name of the commissioning client",
+        "description": "Name of the client or organization commissioning the screening task."
+      },
+      {
+        "id": "target_market",
+        "label": "Target Listed Market",
+        "type": "text",
+        "required": true,
+        "default": null,
+        "placeholder": "e.g. HKEX Main Board",
+        "description": "Listed market or jurisdiction for candidate identification."
+      },
+      {
+        "id": "research_cutoff_date",
+        "label": "Research Cut-off Date",
+        "type": "date",
+        "required": false,
+        "default": "today",
+        "description": "Latest date up to which information is considered."
+      },
+      {
+        "id": "control_requirement",
+        "label": "Control Requirement",
+        "type": "longtext",
+        "required": false,
+        "default": null,
+        "description": "Client requirement on ownership, effective control, or control path."
+      }
+    ]
+  }
+}
+```
+
+**`type` 允许的取值**（ATL 前端按此渲染控件）：
+
+| type | 前端控件 |
+|---|---|
+| `text` | 单行输入框 |
+| `longtext` | 多行文本框 |
+| `date` | 日期选择器，值格式 `YYYY-MM-DD` |
+| `number` | 数字输入框 |
+| `select` | 下拉框，字段需带 `options: ["a", "b"]` |
+
+**约定：**
+
+- `fields[].id` 全表唯一；用户提交的表单值以 `id` 为键。
+- `required: false` 且用户未填的字段，ATL **不下发该键**（服务端用 `default_settings.md` 的默认值语义处理，如 "Not specified"）。
+- Due Diligence 的 manifest 同构（字段：`target_company` 必填 text、`research_cutoff_date` 必填 date、
+  `additional_context` 可选 longtext），`agent_id` 用 `due-diligence-agent`。
+
+---
+
+## 2. `POST /runs` — 提交一次研究任务
+
+**请求头**：`X-Service-Token` + `Content-Type: application/json`
+
+**请求体：**
+
+```json
+{
+  "settings": {
+    "client_name": "华泰资本",
+    "target_market": "港股主板",
+    "research_cutoff_date": "2026-09-01",
+    "control_requirement": "需取得 ≥60% 控制权",
+    "transaction_objective": "注入新能源资产借壳上市"
+  }
+}
+```
+
+**响应 202：**
+
+```json
+{
+  "run_id": "run_8f3k2",
+  "status": "queued",
+  "created_at": "2026-09-18T08:30:00Z"
+}
+```
+
+**错误：**
+
+| 状态码 | 场景 | 响应体 |
+|---|---|---|
+| 401 | 密钥错误/缺失 | `{"detail": "unauthorized"}` |
+| 422 | 表单校验失败 | `{"detail": "validation failed", "field_errors": {"target_market": "required"}}` |
+
+**约定：**
+
+- 服务收到请求后**立即返回**，研究在后台异步执行（Deep Research 分钟级，不能占着 HTTP 请求等）。
+- `run_id` 全局唯一即可（如 `run_` + 随机串）。
+- 必填字段缺失/类型不对 → 422，并尽量给出 `field_errors`（ATL 会在对应表单项下方标红）。
+- 服务重启后已完成的 run 仍可查询：**run 状态与结果需持久化**（SQLite 即可，内存会在部署重启时丢失）。
+
+---
+
+## 3. `GET /runs/{run_id}` — 查询状态
+
+**响应 200：**
+
+```json
+{
+  "run_id": "run_8f3k2",
+  "status": "running",
+  "created_at": "2026-09-18T08:30:00Z"
+}
+```
+
+**`status` 只允许 4 个值：** `queued` → `running` → `completed` | `failed`（终态不可逆）。
+
+**约定：**
+
+- ATL 每 15 秒轮询一次；超过 manifest 的 `max_runtime_seconds` 仍未终态，ATL 判定超时并停止轮询。
+- `failed` 时本端点可附带 `"error": "人类可读的失败原因"`（可选）。
+- 未知 run_id → 404 `{"detail": "run not found"}`。
+
+---
+
+## 4. `GET /runs/{run_id}/result` — 取结果
+
+仅 `status=completed` 时调用（其他状态调用返回 `409 {"detail": "run not completed"}`）。
+
+**响应 200：**
+
+```json
+{
+  "run_id": "run_8f3k2",
+  "status": "completed",
+  "completed_at": "2026-09-18T08:37:12Z",
+  "report_markdown": "# Shell Company Screening Report\n\n## 1. Executive Summary\n...",
+  "artifacts": {
+    "docx": {
+      "filename": "shell_screening_huatai_20260901.docx",
+      "content_base64": "UEsDBBQAAAAIAA..."
+    },
+    "pdf": {
+      "filename": "shell_screening_huatai_20260901.pdf",
+      "content_base64": "JVBERi0xLjcK..."
+    }
+  },
+  "evidence": {
+    "provider": "openai",
+    "citations": [ { "title": "HKEX announcement", "url": "https://..." } ],
+    "sources": [ "https://..." ],
+    "metadata": {}
+  }
+}
+```
+
+**约定：**
+
+- `report_markdown` **必返**（ATL 在线渲染的主体）；docx/pdf 生成失败时对应键可缺省
+  （她的 runtime 已有此降级逻辑），但 markdown + evidence 必须有。
+- 二进制文件走 `content_base64`（base64 编码传输，体积膨胀约 33%，几百 KB 的文档可接受；
+  避免 ATL 再去拉带鉴权的文件 URL）。单文件建议 ≤ 5 MB。
+- `evidence` 直接内联为 JSON 对象（对应她 `output_handler.py` 的 `save_evidence_json` 结构）。
+- 结果可重复获取（ATL 可能把同一报告发给多个使用者），服务端无需“取后即焚”。
+
+---
+
+## 5. 通用错误与超时
+
+| 状态码 | 含义 |
+|---|---|
+| 401 | `X-Service-Token` 缺失或不匹配 |
+| 404 | 路径或 run_id 不存在 |
+| 409 | 结果端点被调用但 run 未完成 |
+| 422 | 表单校验失败 |
+| 5xx | 服务内部错误（ATL 会在轮询中容忍偶发 5xx，连续失败才判 failed） |
+
+- 服务不必自己做速率限制（调用方只有 ATL）。
+- ATL 侧总超时 = manifest 的 `max_runtime_seconds`（默认 1800 秒），超时后 run 标记 failed 并提示用户。
+
+---
+
+## 6. 待确认清单（双方各答一遍）
+
+1. base URL 与部署平台（建议 Render，与 ATL 后端同栈）
+2. 服务间密钥的生成与存放方式（建议：Joy 生成随机串，双方各配环境变量）
+3. 单次研究实际耗时分布（决定 `estimated_runtime_seconds` 填多少）
+4. docx/pdf 生成在她部署的 Linux 环境是否可用（她 README 提到 PDF 依赖 Windows Word；
+   Linux 下 markdown + evidence 保底是否可接受——按本契约 §4 可缺省）
+5. 两个 Agent 是否共用一个服务（一个 base URL、manifest 里区分 agent_id），还是各部署一份
+   （建议：**共用一个服务**，`GET /manifest` 变为 `GET /manifest?agent_id=...` 或返回数组，减少部署份数——定稿时二选一写死）
diff --git a/docs/integrations/research-agents.md b/docs/integrations/research-agents.md
new file mode 100644
index 00000000..09b0dc89
--- /dev/null
+++ b/docs/integrations/research-agents.md
@@ -0,0 +1,84 @@
+# Research Agents — 研报 Agent 模块（N2/PR2）
+
+> **Language / 语言:** 中文为主，接口字段为英文。对应设计：2026-09-15 admin layer
+> redesign 的 D5/N2（本模块）与 09-17 nav consolidation (#488) / universal top nav
+> (#490) 的既有实现。
+
+## 这是什么
+
+ATL 原有的外部 Agent 只有**交易决策型**一种：外部程序按小时循环接收行情快照、
+提交买卖指令（见 `docs/source/lab/external_agents.rst`）。研报模块新增了
+**报告输出型 Agent** 分类：外部 Deep Research 服务接收一份表单委托
+（mandate），异步产出结构化研究报告，ATL 负责发现、安装、提交、展示与下载。
+
+首批接入的两个 Agent（Lin-Feihan）：
+
+| Agent | 输入 | 输出 |
+|---|---|---|
+| Shell Company Screening Agent | 客户委托表单（12 字段） | 壳公司筛选报告（md/docx/pdf）+ 证据 JSON |
+| Due Diligence Agent | 目标公司 + 截止日 + 补充背景 | 公开信息尽调报告（md/pdf）+ 证据 JSON |
+
+## 架构
+
+```
+Community "Research Agents" 货架
+   │ Add to My Agents（仅记录 user→template 链接，不占资金、不进排行榜）
+   ▼
+My Agents "Research Agents" 货架 → Open workbench
+   │ 动态表单（字段由 Agent 服务的 manifest 实时定义）
+   ▼
+ATL POST /api/v1/research/agents/{id}/runs ──HTTP──▶ Agent 服务 (FastAPI)
+   │ ◀── 轮询状态 / 拉取结果 ──────────────────────┘
+   ▼
+报告入库 → 在线阅读 + 下载（md/docx/pdf/evidence.json）→ 完成邮件（可选）
+```
+
+原则与交易型接入一致：**ATL 不托管模型、不代付 LLM 费用**；真实研究发生在
+Agent 作者自己的服务与 API key 上。
+
+## 服务间契约（4 端点）
+
+完整契约见 `docs/integrations/research-agent-contract.md`（v1.0-draft，与
+Lin-Feihan 侧共享同一份），摘要：
+
+| 端点 | 作用 |
+|---|---|
+| `GET /manifest?agent_id=…` | Agent 卡片 + 表单 schema（字段 id/类型/必填/默认值） |
+| `POST /runs` | 提交 `{agent_id, settings}` → `{run_id}`，后台异步执行 |
+| `GET /runs/{id}` | 状态：`queued → running → completed \| failed` |
+| `GET /runs/{id}/result` | `report_markdown`（必返）+ `artifacts`（base64 docx/pdf，可缺省）+ `evidence` |
+
+- 鉴权：请求头 `X-Service-Token`（`RESEARCH_SERVICE_TOKEN`，双方同值）。
+- 服务必须持久化 run（部署重启后仍可查询）。
+- markdown 与 evidence 必返；docx/pdf 生成失败可缺省（fail-visible，不是静默空报告）。
+
+## ATL 侧实现
+
+| 件 | 位置 |
+|---|---|
+| 模板目录 | `dashboard/config/marketplace.json` 的 `shelf: "research"` 条目（含服务地址 env 名与缺省 URL） |
+| 货架/卡片 | Community `Research Agents` 柜台（无收益曲线，卡片为服务事实）+ My Agents 同名货架 |
+| 存取 | `dashboard/backend/domain/agents/research_store.py`（adds / runs / artifacts 三张 sqlite 表，产物 base64 入库——Render 文件系统易失） |
+| 路由 | `dashboard/backend/api/routers/research.py`（manifest 代理带短缓存、run 提交/轮询/完成入库、报告与下载、完成邮件钩子） |
+| 工作台 | `researchWorkbenchView`（app.html）+ `app.js` 研报模块（动态表单、10 秒轮询、安全 Markdown 渲染、下载按钮组） |
+| 邮件 | 完成 discovered-by-poll 时经 Brevo `send_email()` 发报告页链接（纯文本 v1；附件待 v1.5） |
+
+**完成发现的语义（v1 限制）**：run 完成的副作用（入库、邮件）发生在
+"某次状态轮询首次发现 completed" 时。前端每 10-15 秒轮询一次；用户关页后
+完成事件推迟到下一次任何人拉取该 run 状态时补发。后台 sweeper 是后续增强。
+
+## 权限与计费
+
+- manifest/runs/报告/下载全部要求登录（`get_current_user`）；报告**仅提交人可见**。
+- 研报 Agent 不参与资金分配、不进排行榜、不产生回测。
+- 计费暂未接入：每次运行消耗 Agent 作者侧的 Deep Research 配额；周期订阅
+  （v2，未实现）上线前需先定计费与限额。
+
+## 尚未完成（按序）
+
+1. **真服务接入**：把模板的 `service_base_url_default` 换成已部署的
+   Lin-Feihan 服务 URL（或配 `RESEARCH_*_SERVICE_URL` 环境变量），替换开发用 stub。
+2. **邮件本地验证**：本地无 Brevo 密钥，发信路径已实现、待 Render 环境实测。
+3. **周期订阅（链路 2 v2）**：`research_subscriptions` 设计已备（见设计讨论），
+   等订阅语义（完成通知 vs 周期重跑）与计费确认后实现。
+4. 旧 `/app?view=admin` 控制台代码保留一个发布周期后删除（现已被重定向取代）。