Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.
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Review code for bugs, logic errors, security vulnerabilities, code quality issues, and adherence to project conventions, using confidence-based filtering to report only high-priority issues that truly matter. Use this skill when reviewing a small set of changes locally (such as unstaged diff), when dispatched as a sub-task during feature-dev quality review, or when the user wants a critique of a specific file or function.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Review the assigned change set with high precision. Read enough surrounding code to establish reachability and report only actionable defects introduced by the scope.
[REDACTED] and retain only the minimum location, type, and remediation evidence.partial or blocked with the missing coverage rather than disclose it.Honor an explicit file, function, or commit range. Without explicit scope, review all pending staged, unstaged, and untracked changes. Use the HEAD baseline, inspect the combined working-tree diff, list untracked paths, and read every untracked file as an addition.
Freeze the baseline, implementation, changed paths and statuses, untracked paths, and applicable project rules before analysis. Do not silently narrow the review to one Git state.
Treat a supplied scope manifest as authoritative. Do not rediscover or widen the change set. Compare the baseline and implementation supplied by the parent, and use its identifiers, role focus, exclusions, baseline evidence, candidate IDs, and completion criteria.
A feature-dev dispatch consumes the Phase 5 implementation baseline and implementation delta. Preserve its baseline commit, pre-existing change ledger, implementation commits, exact changed paths, and exact committed/staged/unstaged/untracked provenance. Review only the implementation attributable to that handoff.
Return the exact response contract supplied by the parent. For feature-dev, start with Status: complete | partial | blocked, repeat ASSIGNMENT_ID, and report covered scope, uncovered scope, evidence, findings, and errors or blockers. For a code-review scope manifest, return:
STATUS: complete | partial | blocked
SCOPE_ID:
ROLE:
COVERAGE:
CANDIDATES: none | candidate records
ERRORS: none | details
Report partial or blocked rather than success whenever required coverage or evidence is missing.
Maintain a scope/reading ledger for every changed path. Record provenance, applicable rules, changed functions or classes, full-file read status, callers read, shared-state paths traced, tests inspected, and uncovered work. A clean result requires every in-scope path to have complete required reading.
For each changed function or class:
Apply only explicit AGENTS.md or CLAUDE.md rules governing the path. Quote the violated rule.
Check logic, null handling, races, memory/resource lifetime, security, and material performance failures.
Check significant duplication, missing critical error handling, accessibility failures, inadequate test coverage, and scope creep that does not trace to the change's goal. Do not report style preferences.
Check empty and boundary inputs, malformed data, downstream failure or timeout, partial success, ordering, idempotency, and cache invalidation.
Walk every ledger path through all four review categories. Create candidate records with stable IDs, evidence, baseline comparison, initial confidence, and a concrete reachable reproduction scenario.
For every changed function and every candidate, check:
Drop a candidate if no concrete reachable input or condition can trigger it. Record why each dropped candidate was rejected rather than silently omitting it.
Score candidates from 0–100 based on direct evidence, reachability, baseline attribution, and impact:
Quality takes precedence over quantity. Do not flag speculative failures whose required runtime state or input is not shown to be reachable.
State the exact reviewed scope and its provenance. Put findings first, grouped by Critical then Important. For each finding include:
If no finding reaches the threshold, emit a clean result only when the scope/reading ledger is complete. Otherwise report partial or blocked status with covered scope, missing coverage, preserved candidates, and errors.
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Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.
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Audit and improve project-rules files (AGENTS.md, CLAUDE.md, .agents/instructions, local overrides) so the agent keeps accurate project context. Use when the user asks to check, audit, review, update, improve, or fix their AGENTS.md or CLAUDE.md, mentions "project rules maintenance" or "agent context optimization", or when the codebase has changed enough that the rules file may be stale. Scans the repository for every rules file, grades each against a quality rubric, outputs a quality report, and applies targeted edits only after user approval.
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Capture learnings from the current session into the project-rules file (AGENTS.md, CLAUDE.md, or local override) so future sessions benefit. Use when the user says "revise the rules", "update AGENTS.md / CLAUDE.md with what we just learned", "save this to project memory", "remember this for next time", or at the end of a productive session when valuable context has emerged that is not yet documented. This complements agents-md-improver — improver audits, while this one captures.
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Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside frontend-design.
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Design a feature architecture by analyzing existing codebase patterns and conventions, then provide a comprehensive implementation blueprint with specific files to create or modify, component designs, data flows, and a build sequence. Use this skill when the user asks for an architecture design, an implementation plan for a non-trivial feature, or when dispatched as a sub-task during feature-dev architecture phase.
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Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".
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