本文へ移動
cccskills
無料GitHub で公開

code-security-review

Passive code-review lane for authorized repositories, local checkouts, uploaded source archives, and diffs. Use when the security research agent needs deterministic candidate discovery before expensive AI investigation, scoped PR-style review, append-only evidence records, or independent revalidation of code findings.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md4.4 KB
  • references/upload-processing-audit.md10.0 KB

SKILL.md(原文)

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。

Code Security Review

Use this product-side skill for repository and diff analysis. The goal is to turn source files into durable candidate signals, then investigate only the most useful files with clear evidence and revalidation. This is a passive workflow; any build, test, shell, network, browser, or mutation action requires target authorization plus a matching durable approval per docs/durable-approvals.md — chat consent alone is insufficient.

For services accepting document, image, audio/video or archive uploads, apply the upload processing audit baseline. Trace intake through delayed or optional processing and actual native dependencies; include its chain inventory, control assessment and unresolved edges in the review output. This applies even when the upload handler only stores bytes.

Ground Rules

  • Treat a repository, local checkout, source archive, or diff as authorized only for passive reading unless a durable approval grants a broader action.
  • Do not run install, build, test, scripts, hooks, or package-manager commands without a durable approval.
  • Never load secret or config files (.env, key material, credentials) into prompts, model context, or evidence while reading the target; record their existence by path only and keep their values out of prompts, commands, telemetry, and evidence.
  • Division of labor: deterministic repo/diff candidate scans live here; spec/protocol invariant tracing belongs to spec-implementation-review.
  • Prefer deterministic candidate discovery before model-heavy investigation.
  • Keep project context short. Include target shape, key auth primitives, entry points, false-positive patterns, and priority paths; do not dump the full repo.
  • Keep scan state append-only: rescans add history, investigations add analysis entries, revalidation annotates findings.
  • Separate candidate signals from findings. A route handler, sink, or tool definition is a reason to review, not a vulnerability by itself.
  • Revalidate candidate findings independently before marking them validated, remediated, or false positive.

Workflow

  1. Scope. Identify repo/path/diff, target id, commit or artifact version, allowed actions, and whether this is a full pass or changed-file review.
  2. Compact context. Summarize architecture, auth/session controls, data stores, agent/tool boundaries, priority paths, and known false positives in a short project context.
  3. Candidate scan. Prefer passive matchers for entry points, auth boundaries, raw SQL, SSRF-like fetches, file writes, process execution, dangerous HTML, secret handling, and agent tool definitions.
  4. Prioritize. Investigate precise candidates before normal and noisy candidates. In diff mode, still inspect changed files even when no matcher fires.
  5. Investigate. For each file or small batch, produce evidence-backed draft findings, no-finding coverage notes, and missing-evidence questions.
  6. Revalidate. Try to disprove each draft finding by rereading the code, checking guards/reachability, and looking for fixes. Mark true-positive, false-positive, fixed, uncertain, or duplicate.
  7. Persist evidence. Save scan summaries, candidate manifests, analysis notes, and validated finding artifacts through the Evidence Interface (src/server/evidence/artifact-service.ts) and the normal finding path; cite returned artifact IDs.

Output

Return:

  • Scope: repository/path/diff, target id, commit or artifact version, and passive/approved actions.
  • Scan Summary: files considered, candidates by matcher/category/noise tier, clean changed files, and priority paths.
  • Candidate Queue: ranked files with candidate reasons and exact paths/lines.
  • Findings: draft or validated findings with evidence, confidence, severity, and revalidation state.
  • No-Finding Coverage: files or entry points reviewed with no issue found.
  • Next Steps: approval needs, follow-up matchers, validation plans, patch candidates, or report-ready artifacts.

レビュー

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。

同じリポジトリのスキル

概要と使いどころ

Plan and use user-consented local camera and microphone observations for an explicitly authorized physical target. Use when a capture can establish a bounded physical-state observation such as whether a test fixture changed state, an instrument reading changed, or equipment noise changed relatively.

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。

justsml/ExploitHunter.app142026年10月15日 更新

Plan vulnerability research using conventional bug-hunting and pentest stage names, specialty playbooks, and a research-hypothesis-experiment loop. Use when designing a bug bounty workflow, selecting tools and strategies, building a decision tree, or turning a target/scope into a staged hunting plan.

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。

justsml/ExploitHunter.app142026年10月15日 更新

Run independent, task-matched expert attempts, select a base, and synthesize the strongest result.

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。

justsml/ExploitHunter.app142026年10月15日 更新

Select established CTF and security-research tools for authorized artifact triage, web inspection, reversing, crypto, forensics, networking, and exploit validation. Use when a user provides a CTF-style tool catalog, asks which tools to use, or needs safe usage instructions for common offensive/security tools.

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。

justsml/ExploitHunter.app142026年10月15日 更新

diagnose

無料

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

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。

justsml/ExploitHunter.app142026年10月15日 更新

Autonomous driver loop for ExploitHunter vulnerability-research campaigns. Runs breadth-first waves of research lanes on rented hosts with no per-request human approval, steers lanes via the shared Investigation Graph (demoting dead ends, respawning on high-signal priorities), saturates host capacity, and enforces wave/budget/safety discipline. Use when driving a multi-target bug-hunting campaign, launching parallel research lanes, or resuming a campaign from a state snapshot.

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。

justsml/ExploitHunter.app142026年10月15日 更新

justsml のスキルをすべて見る

このスキルの問題を報告する