secure-code-review
Skill48 starsChanged 17 days agoMITView on GitHub
Reviews code for common security flaws such as injection, broken authentication, unsafe deserialization, hardcoded secrets, path traversal and weak cryptography, and reports findings with severity and fixes. Use when the user asks for a security review, a pre-release audit or a check of code that handles user input, files or credentials.
16 other projects use a version of this.
Adopted by
Most recently taken up by
- narlyseorg/superhackerstheir own version, first seen Nov 8, 2026
- jaskirat1616/grok-skillstheir own version, first seen Nov 1, 2026
- kaandgnsh/Claude-Bluetheir own version, first seen Nov 1, 2026
- zebbern/claude-code-guidetheir own version, first seen Oct 30, 2026
- Fahrnetic/engineering-agent-skill-workflowtheir own version, first seen Oct 15, 2026
- JayRHa/agent-skillstheir own version, first seen Oct 15, 2026
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-aiSkillvulnerability-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
msewellSkillthreat-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
AnthropicSkillcompetitive-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
AnthropicSkillincident-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
Other versions16 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 16
- zebbern/claude-code-guide★ 4.7ksame name
- nahid-sparktales/agent-dispatcher★ 59same name
- jaskirat1616/grok-skills★ 22same name
- JayRHa/agent-skills★ 12same name
- JayRHa/AgentSkills★ 12same name
- narlyseorg/superhackers★ 8same name
- Fahrnetic/engineering-agent-skill-workflow★ 2same name
- xAmirHamza77/PenKit51★ 2same name
- Adarshukumar/skills★ 1same name
- alizafarbati/opencode-agents-mcp★ 1same name
6 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
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- changelog-generatorSkill
- commit-message-writerSkill
- csv-profilerSkill
- dockerfile-linterSkill
- env-file-auditorSkill
- json-diffSkill
- log-analyzerSkill
- meeting-notes-to-actionsSkill
- pr-description-writerSkill
- python-code-smellsSkill
- readme-doctorSkill
- skill-authorSkill
- sql-query-reviewerSkill
- todo-extractorSkill
The file
We split this skill into the rules it gives an agent
6 rules. 0 are common, 6 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 15 edited versions we read keep these.
6
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
- Distinctive: line 16
Before a release, or when code from an unknown source is about to be merged.
- Distinctive: line 39
Only report issues you can point to in the code.
- Distinctive: line 40
Do not run or test against systems the user does not own.
- Distinctive: line 41
Do not paste real secrets into the report.
- Distinctive: line 42
Prefer the framework's built-in protection over custom code in fixes.
- Distinctive: line 46
If only a snippet is available, state which parts of the application (auth, config, deployment) were out of view.
“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.
Files
Secure code review
When to use
- Code parses user input, builds queries or commands, handles uploads or sessions.
- Before a release, or when code from an unknown source is about to be merged.
- The user asks "is this safe?" about a function or a service.
What to look for
- Injection. User input concatenated into SQL, shell commands, templates, LDAP or file paths.
- Authentication and sessions. Missing checks on endpoints, weak password storage, tokens in URLs, predictable ids.
- Authorization. Object access that trusts an id from the client without an ownership check.
- Secrets. Keys, tokens and passwords in code, config files, logs or error messages.
- Unsafe deserialization.
pickle,yaml.loadwithout a safe loader, Java object streams,evalon data. - Path traversal and uploads. User controlled file names, missing type and size limits, files saved under the web root.
- Cryptography. Home made schemes, MD5 or SHA-1 for passwords, fixed IVs,
randomused for tokens. - Network. Disabled TLS verification, permissive CORS, server side requests to user supplied URLs.
- Dependencies. Pinned versions with known advisories, install scripts that fetch code at run time.
- Error handling. Stack traces returned to clients, verbose logs with personal data.
Report format
For every finding give: severity (critical, high, medium, low), the file and line, one sentence on how it can be abused, and a corrected snippet. Order by severity. Finish with what was not reviewed.
Rules
- Only report issues you can point to in the code. Mark guesses as "possible" and say what would confirm them.
- Do not run or test against systems the user does not own.
- Do not paste real secrets into the report. Show the first and last two characters at most.
- Prefer the framework's built-in protection over custom code in fixes.
Errors
If only a snippet is available, state which parts of the application (auth, config, deployment) were out of view.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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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.

