agentleFS
Sign inStart free
FahrneticFahrnetic/engineering-agent-skill-workflow/skills/enterprise-quality/secure-code-review/SKILL.md

secure-code-review

Skill2 starsChanged 5 months agoLicence not identifiedView on GitHub

Review code for security vulnerabilities and reliability anti-patterns: SQL injection, XSS, authorization bypass, unsafe type handling, and missing framework-level enforcement. Use when conducting a security code review or auditing a codebase for exploitable issues and hardened-by-construction fixes.

16 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use vulnerability-triage, threat-modeling and 2 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.
  • superagent-aiSkill

    vulnerability-triage

    Triage inbound vulnerability reports - GitHub Advisories (GHSA/CVE), bug bounty submissions, HackerOne/Bugcrowd/Intigriti exports, or a researcher's issue - to decide whether a finding is real, by-design, or noise. Reads the report offline, cross-references the project's documented intent and threat model (SECURITY.md, README, code, closed issues), statically audits any PoC without executing it, and emits a structured markdown triage report with a P0-P3 / By-Design severity and a recommended action. Use whenever the user asks to triage, assess, or score a vulnerability report, advisory, CVE, GHSA, or bug bounty submission, or asks "is this a real finding or by-design?".

    Also in 3 projects that use this

  • msewellSkill

    threat-modeling

    Produces structured threat models for software systems using STRIDE on data flow diagrams. Generates DFDs with trust boundaries, identifies threats per element, scores risks, and defines concrete mitigations. Outputs a complete threat model Markdown document through phased, interactive delivery. Use when threat modeling a system, analyzing security threats, identifying attack surfaces, performing STRIDE analysis, assessing security risks in architecture, creating a threat model document, or when the user mentions threat model, attack surface, trust boundaries, STRIDE, or security risk analysis.

    Also in 3 projects that use this

  • AnthropicSkill

    competitive-analysis

    Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.

    Also in 3 projects that use this

  • AnthropicSkill

    incident-response

    Run an incident response workflow — triage, communicate, and write postmortem. Trigger with "we have an incident", "production is down", an alert that needs severity assessment, a status update mid-incident, or when writing a blameless postmortem after resolution.

    Also in 3 projects that use this

The file

Show its rules and text

We split this skill into the rules it gives an agent

15 rules. 0 are common, 13 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 15 edited versions we read keep these.

  • 13

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

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

The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 15 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.

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.

Check your setup, free