Add Jev job-lead eval and bounded shadowing - #417
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Included review availability: Your plan provides up to 1 included review per hour; 0 remain after this review. 📝 WalkthroughWalkthroughThe pull request adds a job-lead classification evaluation harness. It evaluates heuristic, Jev, and Luna classifiers against a synthetic corpus, computes deterministic metrics, supports CLI report generation, adds unit tests, and records Jev evaluation results. Production classification remains unchanged. ChangesJob-lead classification evaluation
Priority: ⬇️ Low Estimated code review effort: 4 (Complex) | ~45 minutes Change: Other Sequence Diagram(s)sequenceDiagram
participant CLI
participant EvaluationSuite
participant HeuristicClassifier
participant OpenRouterDecisions
participant OpenAIChatCompletions
participant ReportWriter
CLI->>EvaluationSuite: load corpus and select profiles
EvaluationSuite->>HeuristicClassifier: classify each case
EvaluationSuite->>OpenRouterDecisions: request Jev decision
OpenRouterDecisions-->>EvaluationSuite: return classification and usage
EvaluationSuite->>OpenAIChatCompletions: request Luna structured output
OpenAIChatCompletions-->>EvaluationSuite: return parsed classification and usage
EvaluationSuite->>ReportWriter: summarize observations and write report
ReportWriter-->>CLI: return JSON or Markdown output
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 11.43% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 70 functions across 2 files. (1 skipped: 1 unsupported.)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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In `@packages/shared/src/five08/job_lead_evals.py`:
- Line 18: Add openai to the shared package’s project.dependencies because
five08.job_lead_evals imports OpenAI at module load time and the job-lead-eval
entry point requires it. Keep the existing import and ensure environments
installing only the shared package can import five08.job_lead_evals
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.context/reports/2026-09-21-jev-job-lead-classification-eval.md.gitignorepackages/shared/pyproject.tomlpackages/shared/src/five08/job_lead_evals.pytests/evals/job-lead-classification/README.mdtests/evals/job-lead-classification/fixtures/v1/corpus.jsontests/unit/test_job_lead_evals.py
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* feat: add Jev job lead shadow observations * fix: bound Jev shadow execution * fix: report captured Jev shadow configuration * fix: sanitize Jev shadow failures
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| int(item[2].get("output_tokens") or 0) for item in successful | ||
| ), | ||
| "total": sum(int(item[2].get("total_tokens") or 0) for item in successful), | ||
| "cost_usd": round(sum(cost_values), 8) if cost_values else None, |
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In the new scrape_job_leads shadow report, when multiple Jev calls succeed but even one response omits usage.cost, cost_values contains only the priced responses and this line publishes their sum as the run's total. Since JobLeadJevDecision.cost_usd is explicitly optional, the dashboard can silently understate provider spend; require a cost value for every successful observation before reporting a total, otherwise return None or expose pricing coverage.
AGENTS.md reference: AGENTS.md:L133-L133
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| if self._jev_shadow_calls_started >= self._jev_shadow_max_calls: | ||
| self._jev_shadow_budget_exhaustion_reason = "max_calls" | ||
| return None |
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When a monthly HN tree is scraped repeatedly in the same order and at least max_calls earlier comments pass the deterministic sample, this prefix-based cap is reached before every later comment on every run. Newly appended posts can therefore remain permanently unobserved while the same older cohort consumes all 25 calls, biasing the live canary telemetry; prioritize new/unobserved posts or choose the capped cohort by a stable hash rank across all eligible comments rather than stopping at the first sampled prefix.
AGENTS.md reference: AGENTS.md:L131-L131
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| "- Jev uses OpenRouter's Decisions endpoint and the pinned `typesafe/jev-1.13` request ID. The resolved dated snapshot is retained in the JSON observation report.", | ||
| "- The Luna baseline uses the production job-lead prompt and schema through direct OpenAI. A preflight through OpenRouter returned HTTP 403 under provider terms, so the report does not present an unsupported route as a benchmark failure.", |
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Every generated report claims that Jev used the pinned default model and that a Luna preflight through OpenRouter returned HTTP 403, but the runner never performs or records that preflight and both models are configurable through --jev-model and --llm-model. Reports produced with custom models—and even ordinary fresh runs—therefore contain unsupported provenance statements; render model identities from report.requested_models and omit the preflight claim unless the run records that event.
AGENTS.md reference: AGENTS.md:L133-L133
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Summary
Result
Across 144 calls per network profile, Jev achieved 100.0% contractor-friendly F1, 95.8% joint accuracy, and stable labels on all 48 cases. It measured 428 ms p50, 616 ms p95, and USD 0.003093 total provider-reported cost.
The corrected production-equivalent Luna rerun achieved 60.2% contractor-friendly F1 and 71.5% joint accuracy at 2046 ms p50, 2873 ms p95, and USD 0.034287 estimated cost. The report recommends starting the shadow at a symmetric 0.80 confidence gate while retaining deterministic guards and the current classifier.
Jev calls OpenRouter's Decisions endpoint directly with OPENROUTER_API_KEY. Shadow reports retain normalized decisions and sanitized error categories, not raw post text or provider messages.
Validation