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.
日本語の概要は準備中です。原文の説明を表示しています。
Systematically verifies suspected security bugs to eliminate false positives. Produces TRUE POSITIVE or FALSE POSITIVE verdicts with documented evidence for each bug.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
If you catch yourself thinking any of these, STOP.
| Rationalization | Why It's Wrong | Required Action |
|---|---|---|
| "Rapid analysis of remaining bugs" | Every bug gets full verification | Return to task list, verify next bug through all phases |
| "This pattern looks dangerous, so it's a vulnerability" | Pattern recognition is not analysis | Complete data flow tracing before any conclusion |
| "Skipping full verification for efficiency" | No partial analysis allowed | Execute all steps per the chosen verification path |
| "The code looks unsafe, reporting without tracing data flow" | Unsafe-looking code may have upstream validation | Trace the complete path from source to sink |
| "Similar code was vulnerable elsewhere" | Each context has different validation, callers, and protections | Verify this specific instance independently |
| "This is clearly critical" | LLMs are biased toward seeing bugs and overrating severity | Complete devil's advocate review; prove it with evidence |
Before any analysis, restate the bug in your own words. If you cannot do this clearly, ask the user for clarification. Half of false positives collapse at this step — the claim doesn't make coherent sense when restated precisely.
Document:
parse_header() when content_length exceeds 4096")memcpy at line 142")After Step 0, choose a verification path.
Use when ALL of these hold:
Follow standard-verification.md. No task creation — work through the linear checklist, documenting findings inline.
Use when ANY of these hold:
Follow deep-verification.md. Break the work into a full task dependency graph and execute each phase methodically, delegating to subagents where the host supports it.
Start with standard. Standard verification has two built-in escalation checkpoints that route to deep when complexity exceeds the linear checklist.
When verifying multiple bugs at once:
After processing ALL suspected bugs, provide:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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".
日本語の概要は準備中です。原文の説明を表示しています。