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docs(solution-architecture): list self-hosted-llm-batch-inference in the plugin README - #361

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realAlexBarge:docs/plugin-readme-batch-inference-row

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The skill has shipped in the plugin since 2026-09-05 but was missing from the skills table. This adds its row. Most of the diff is Prettier widening the first column.

…the plugin README

The skill has shipped in the plugin since 2026-09-05 but was missing
from the skills table. Most of the diff is Prettier widening the
first column.
@realAlexBarge
realAlexBarge requested a review from a team as a code owner October 27, 2026 18:06

@solution-architecture-reviewer solution-architecture-reviewer Bot left a comment

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solution-architecture-reviewer bot: 3 item(s) for a human

Not approving, not blocking. Something below is worth a look, but none of it is the solution-architecture-reviewer bot's call to settle: a human decides.

Reviewed 1 changed file(s).

Axis assessment

  • 🟡 Manifest drift — The PR description claims the skill self-hosted-llm-batch-inference has shipped in the plugin since 2026-09-05 but was simply missing from the table; this diff only shows the README.md table, so there is no visibility into the actual skills/ directory to confirm the skill really already exists and matches this description. If the skill directory does not already exist, this would be manifest drift (documenting a skill not yet shipped), but nothing in the provided diff contradicts the claim.
  • 🟡 Upstream duplication (advisory) — The new README row describes self-hosted-llm-batch-inference as covering 'offline inference for a 1B to 8B open-weight model on AWS Batch with Amazon ECS Managed Instances Spot: no idle GPU between runs, and automatic recovery from Spot interruption.' This is plausibly startup-shaped (cost/idle-GPU framing) but the skill's own SKILL.md text was not supplied, so I cannot verify its actual description against aws-compute's Spot/ASG coverage or aws-containers's ECS Managed Instances coverage; this is a judgment criterion 2 defers to human review and I lack the skill body to make a concrete claim.
  • 🟡 Scope (startup-specific) (advisory) — The table description frames the skill as a specific technical problem (batch GPU inference cost/idle avoidance) which looks startup-shaped per criterion 1, but since only the README row is in the diff and not the skill's SKILL.md, a reviewer should still confirm the full skill body doesn't drift into general-purpose Batch/ECS guidance that would read identically for an enterprise.
  • ✅ Contradiction — No conflicting guidance introduced; this is a table-row addition and column widening in README.md.
  • ✅ Staleness — The new row references self-hosted-llm-batch-inference with AWS Batch and Amazon ECS Managed Instances Spot, both current services; no sunset-service mention appears in the diff.
  • ✅ Activation quality (advisory) — The added description for self-hosted-llm-batch-inference is specific (model size range, AWS Batch, ECS Managed Instances Spot, idle-GPU and Spot-interruption framing) and does not overlap textually with sibling rows agentcore-patterns, cost-regression-check, or hcls-stack-check.
  • ✅ Injection posture (advisory) — No new agent or untrusted-input-consuming skill is introduced in this diff; it is a documentation table edit.
  • ✅ PR hygiene (advisory) — The PR description discloses that most of the diff is Prettier widening the first column and that the change simply adds a missing row for an already-shipped skill; this matches solution-architecture/CONTRIBUTING.md's carve-out for corrections to existing content ('wording that misstates what the skill does') not requiring an RFC, which is a disclosed and plausible rationale.
  • ✅ Authoring conventions (advisory) — The table-formatting change in README.md matches the described Prettier/dprint widening behavior in the PR description and introduces no departure from documented conventions in solution-architecture/CONTRIBUTING.md.

🔴 concern · 🟡 borderline · ✅ clear. Advisory axes are reported but never block, since the contribution gate reserves those criteria for human reviewers.

Reviewed 1 file(s). Model: us.anthropic.claude-sonnet-5. Findings below 0.6 confidence are surfaced as borderline rather than hedged. Judgment-only: formatting, schema, and security scanning are covered by existing CI.

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