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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Turn one confirmed security finding into a disclosure-ready GitHub advisory with root cause, proof of concept, impact, and source evidence. Use for reporting an established vulnerability, not discovering or validating one.
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安装前可以查看交给 AI 代理的指令内容。
Draft one disclosure-ready report for one confirmed bug. Keep the report evidence-driven, concrete, and concise. Prefer the section order and phrasing rules in references/report-template.md.
report.md inside a folder named with the bug's severity identifier (C1, H1, M1, etc.) followed by a lowercase hyphenated slug derived from the final report title. Use C for Critical, H for High, M for Medium, sequentially numbered if there are multiple bugs of the same severity. Example: C1-cross-site-websocket-hijacking-re-enabled-by-allow-websocket/report.md. Also, ensure the bug report title and internal references use this ID (e.g., '[C1] Cross-Site WebSocket Hijacking'). Do not write reports for Low severity findings — document them in the summary table only.Always include these sections in this order:
SummaryDetailsRoot CauseProof of Concept (PoC)ImpactIf the repository already uses Technical Details with Root Cause nested under it, preserve that local pattern. Otherwise keep Root Cause as its own section.
Details or Root Cause.git rev-parse HEAD or the most recent commit visible in context) instead of a branch name such as main or master, so links remain stable after future commits.The following code in [build_request](https://github.com/org/repo/blob/main/src/executor.rs#L10) reads attacker-controlled input without validation.report.md is a disclosure-ready artefact. The reader must understand the vulnerability, the trace, the impact, and the reproduction without opening any sibling working file (drafts, debate transcripts, review notes, internal metadata).
See draft.md, See debate.md, See adversarial-review.md, See metadata.json, See pN-NNN for full trace, See AP-NNN, Refer to the draft for impact analysis, or for the full trace see .... If that content is needed in the report, inline it.pN-NNN, p10-NNN, AP-NNN) — these are pipeline bookkeeping, not reader-facing references.poc.<ext>, evidence/<file>), and only inside the Proof of Concept or Impact sections. Quote the decisive lines from logs inline rather than telling the reader to open them.Open with the vulnerable behavior, attacker control, and outcome in one short paragraph. Name the component only if it improves clarity.
Explain the code path and why the protection fails. Include relevant conditions such as auth mode, stateless mode, parser behavior, MIME confusion, or transport assumptions. Support the explanation with code snippets and GitHub markdown links to the exact source locations.
State the design or implementation mistake in one focused subsection. Prefer causal language such as missing origin validation, unsafe trust in extension-derived MIME, or policy enforced only in one execution mode.
Use the shortest reliable reproduction. Prefer numbered steps and a runnable request, command, or code block. State the expected result.
Describe exploitability and consequence, not just severity labels. Cover who is exposed, what an attacker gains, and which environments are most at risk.
Include an optional section only when it adds concrete triage value.
Allowed optional sections include:
CWECVSS vector or severity guidanceDo not add Affected Components or Remediation sections unless the user explicitly asks for them.
<ID>-<title-slug>/report.md.draft.md, debate.md, adversarial-review.md, metadata.json). See the Self-Contained Rule.pN-NNN, AP-NNN). Inline the content.还没有评价。欢迎分享你的使用感受。
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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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