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jbaruch/coding-policy

General-purpose coding policy for Baruch's AI agents

76

Quality

95%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Low

Low-risk findings worth noting

Overview
Quality
Evals
Security
Files

copilot-instructions.mdskills/onboard-repo/templates/

Copilot code review — scope

This repository is reviewed by a separate policy reviewer — the central jbaruch/coding-policy fleet reviewer (a scheduled GitHub App that reviews every PR against the installed jbaruch/coding-policy rules, enrolled via the .github/fleet-review-enabled marker). It owns conventions and policy. Your job is the complementary lane: correctness and risk. Spend your review budget where the policy reviewer does not look.

Review for

  • Logic errors, wrong conditions, off-by-one, incorrect edge-case handling.
  • Race conditions, ordering bugs, non-idempotent retries, lost updates.
  • Resource leaks (unclosed files/connections), unbounded growth, needless recompute on hot paths.
  • Missing or wrong error handling that lets a real failure pass silently or crash — not stylistic preference.
  • Security: injection, unsafe deserialization, authz gaps, secrets in code or logs, unvalidated untrusted input.
  • Test coverage gaps: a changed branch or failure path with no test; an assertion that would pass even if the code were wrong.

Do NOT comment on (the policy reviewer owns these)

  • Naming/style conventions, formatting, import order.
  • Commit-message or PR-title format, changelog entries, branch naming.
  • Rule/policy compliance, doc structure, skill/rule authoring conventions.
  • Restating project rules — assume they are enforced by the policy reviewer.

Repo facts (avoid false positives)

  • Verify a tool's real semantics before flagging — prefer a missed bug over a confident false positive.

README.md

tile.json