agentleFS
TwigpineTwigpine/openclaude-skills/skills/security-audit/SKILL.md

security-audit

Skill19 starsChanged 6 months agoMITView on GitHub

Reviews code changes for common security risks.

192 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use create-stories, story-done and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • lilangMaxSkill

    create-stories

    Break a single epic into implementable story files. Reads the epic, its GDD, governing ADRs, and control manifest. Each story embeds its GDD requirement TR-ID, ADR guidance, acceptance criteria, story type, and test evidence path. Run after /create-epics for each epic.

    Also in 11 projects that use this

  • lilangMaxSkill

    story-done

    End-of-story completion review. Reads the story file, verifies each acceptance criterion against the implementation, checks for GDD/ADR deviations, prompts code review, updates story status to Complete, and surfaces the next ready story from the sprint.

    Also in 11 projects that use this

  • nicepkgSkill

    community-led-growth

    Expert in community-led growth (CLG) - leveraging user communities to drive acquisition, retention, and expansion. Covers building developer communities, user groups, ambassador programs, and turning customers into advocates. Knows the difference between community as a feature vs community as a growth engine, and how to measure community ROI. Use when "community-led, CLG, ambassador program, champion program, user community, developer community, word of mouth, user group, " mentioned.

    Also in 5 projects that use this

  • nicepkgSkill

    ph-community-outreach

    Engage Reddit, Indie Hackers, Hacker News and other communities for Product Hunt launches. Use this skill to identify relevant communities, craft appropriate posts, and drive cross-platform engagement without appearing spammy.

    Also in 5 projects that use this

  • +7 more from lilangMax
  • +1 more from nicepkg

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 173 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Do not flag theoretical issues without a path to exploitation.

    line 87
  2. Distinctive:

    When in doubt, check the framework's defaults.

    line 90
  3. Distinctive:

    If the input is unbounded, recommend a length cap upstream, a non-backtracking engine, or a rewrite to a linear pattern.

    line 123

“Common” is measured: kept by at least 6 in 10 of the 173 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Security Audit

Review a diff, file, or codebase area through a security lens. Look for classes of bugs that have specific, well-understood exploits, not for vague "could be more secure" comments.

Use this skill when

  • The user asks for a security review, security audit, or threat check.
  • The user wants to know whether a change has security implications before merging.
  • The user pastes code involving auth, user input parsing, file uploads, network calls, deserialization, or secret handling and asks "is this safe?".
  • The user pastes a security alert (Dependabot, Snyk, CodeQL) and asks whether it applies to their usage.

Do NOT use this skill when

  • The user wants to write a new authentication or authorization flow from scratch. This skill audits existing code; it does not design auth.
  • The user wants a general code review without a security focus. Use pr-review.
  • The user is asking about red-team techniques, exploitation steps, or how to attack a system they do not own. Decline and explain.

Procedure

  1. Identify the trust boundaries. Note where data crosses from untrusted (user input, network, file uploads, env-controlled values) into trusted contexts (DB queries, shell commands, template renders, redirects). Most security bugs live at boundaries.
  2. Walk the changes against each of the following categories. Skip a category only if it is genuinely irrelevant — explain why.
    • Injection: SQL (concatenation vs parameterization), command injection (shell metacharacters in exec/spawn), template injection (user data into Jinja/EJS without escaping), LDAP/XPath injection, NoSQL injection (operator passthrough).
    • Auth and authz: missing checks on new endpoints, IDOR (object IDs trusted from the URL without ownership check), privilege escalation through admin flags settable by the client, missing rate limits on sensitive operations.
    • Secret handling: secrets in logs, secrets in error responses, secrets in client-side bundles, secrets committed to the repo, secrets passed as command-line args (visible in ps).
    • Unsafe file ops: path traversal (../), zip-slip, symlink attacks, writes to user-controlled paths, reads of arbitrary server-side files.
    • Unsafe network ops: SSRF (user-controlled URLs fetched server-side), open redirects (user-controlled redirect targets), CORS misconfiguration, missing TLS verification.
    • Deserialization: pickle/yaml.load/Java serialization on untrusted input, JSON parsing with __proto__ (prototype pollution), unsafe schema-less object spreads.
    • XXE / SSRF in parsers: XML parsers with external entity resolution enabled, SVG uploads rendered server-side, image processors fed user input without limits.
    • Dependency risks: newly added or upgraded dependencies — check advisories, check the actual functions called against the CVE description (most CVEs are not exploitable in every usage).
  3. For each finding, write down: the input that triggers it, the path from input to the dangerous sink, the impact (data exposure, RCE, account takeover, denial of service), and a concrete fix.
  4. Distinguish severity:
    • Critical — RCE, auth bypass, data exposure of secrets or PII at scale.
    • High — privilege escalation, exploitable injection, SSRF that reaches internal services.
    • Medium — IDOR with limited scope, misconfig that requires other bugs to chain, missing rate limit.
    • Low — defense-in-depth hardening, informational.
  5. Do not flag theoretical issues without a path to exploitation. If you cannot describe the input that triggers the bug, you do not have a finding — you have a hunch.
  6. When in doubt, check the framework's defaults. Many "looks unsafe" patterns are actually protected by the framework (ORM parameterization, template auto-escaping, secure-by-default cookie flags). A finding that the framework already mitigates is noise.
  7. For each finding, also note whether mitigations already exist (WAF rules, schema constraints, downstream validation). A bug that is mitigated three layers deeper is still a bug, but its severity is usually lower.

Examples

In scope: "Audit this new file-upload endpoint."

→ Trace the request path. Note that the filename is taken from the multipart form and concatenated into a path passed to fs.writeFile without normalization (finding: path traversal at upload.ts:42). Note that there is no size limit and no content-type check (finding: DoS via large uploads at upload.ts:30). Suggest fixes: validate filename against a charset, write to a controlled directory using only a server-generated name, enforce a size limit upstream of the body parser.

In scope: "Dependabot says marked@4.x has a ReDoS — do we use it?"

→ Search the repo for marked usage. If the only use is rendering trusted markdown from a fixed source (e.g., bundled docs), the advisory does not apply. If it parses user-supplied markdown, the advisory applies — upgrade and add a regex-runtime safeguard.

In scope: "Is this regex safe for user input?"

→ Check for catastrophic backtracking patterns (nested quantifiers like (a+)+, alternation with overlapping branches). If the input is unbounded, recommend a length cap upstream, a non-backtracking engine, or a rewrite to a linear pattern.

Out of scope: "Build a secure password reset flow."

→ This skill audits existing flows; it does not design new ones. Suggest a coding skill plus an audit of the result.

Out of scope: "Find me a way to bypass the auth on this API."

→ Decline. This skill defends; it does not produce exploitation recipes for systems the user does not clearly own and is not auditing in good faith.

Self-check before responding

  • Did I anchor every finding to a specific file:line?
  • Can I describe the exact input that triggers each finding?
  • Did I distinguish exploitable bugs from defense-in-depth hardening?
  • Did I assign a severity?
  • Did I propose a concrete fix, not "consider sanitizing input"?
  • Did I check the new code for new trust boundaries, not just the diff hunks?
  • Did I avoid flagging things that look risky but are demonstrably safe in this codebase (e.g., constants concatenated into SQL)?
  • Did I keep red-team / exploitation detail to the minimum needed to describe the bug?
  • Did I note any existing mitigations and reflect them in severity?
  • Did I check whether each finding is actually reachable from a real request path, not just "in theory"?

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.