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。
Detects fail-open insecure defaults (hardcoded secrets, weak auth, permissive security) that allow apps to run insecurely in production. Use when auditing security, reviewing config management, or analyzing environment variable handling.
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。
Finds fail-open vulnerabilities where apps run insecurely with missing configuration. Distinguishes exploitable defaults from fail-secure patterns that crash safely.
SECRET = env.get('KEY') or 'default' → App runs with weak secretSECRET = env['KEY'] → App crashes if missingDo not use this skill for:
test/, spec/, __tests__/).example, .template, .sample suffixes)When in doubt: trace the code path to determine if the app runs with the default or crashes.
Follow this workflow for every potential finding:
Determine language, framework, and project conventions. Use this information to further discover things like secret storage locations, secret usage patterns, credentialed third-party integrations, cryptography, and any other relevant configuration. Further use information to analyze insecure default configurations.
Example
Search for patterns in **/config/, **/auth/, **/database/, and env files:
getenv.*\) or ['"], process\.env\.[A-Z_]+ \|\| ['"], ENV\.fetch.*default:password.*=.*['"][^'"]{8,}['"], api[_-]?key.*=.*['"][^'"]+['"]DEBUG.*=.*true, AUTH.*=.*false, CORS.*=.*\*MD5|SHA1|DES|RC4|ECB in security contextsTailor search approach based on discovery results.
Focus on production-reachable code, not test fixtures or example files.
For each match, trace the code path to understand runtime behavior.
Questions to answer:
Determine if this issue reaches production:
If production config provides the variable → Lower severity (but still a code-level vulnerability) If production config missing or uses default → CRITICAL
Example report:
Finding: Hardcoded JWT Secret Fallback
Location: src/auth/jwt.ts:15
Pattern: const secret = process.env.JWT_SECRET || 'default';
Verification: App starts without JWT_SECRET; secret used in jwt.sign() at line 42
Production Impact: Dockerfile missing JWT_SECRET
Exploitation: Attacker forges JWTs using 'default', gains unauthorized access
Fallback Secrets: SECRET = env.get(X) or Y
→ Verify: App starts without env var? Secret used in crypto/auth?
→ Skip: Test fixtures, example files
Default Credentials: Hardcoded username/password pairs
→ Verify: Active in deployed config? No runtime override?
→ Skip: Disabled accounts, documentation examples
Fail-Open Security: AUTH_REQUIRED = env.get(X, 'false')
→ Verify: Default is insecure (false/disabled/permissive)?
→ Safe: App crashes or default is secure (true/enabled/restricted)
Weak Crypto: MD5/SHA1/DES/RC4/ECB in security contexts → Verify: Used for passwords, encryption, or tokens? → Skip: Checksums, non-security hashing
Permissive Access: CORS *, permissions 0777, public-by-default
→ Verify: Default allows unauthorized access?
→ Skip: Explicitly configured permissiveness with justification
Debug Features: Stack traces, introspection, verbose errors → Verify: Enabled by default? Exposed in responses? → Skip: Logging-only, not user-facing
For detailed examples and counter-examples, see examples.md.
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。