skills/security/static-vulnerability-detector/SKILL.md
Scans source code for security vulnerabilities by applying Project CodeGuard rules — injection, unsafe deserialization, XSS, path traversal, broken access control. Use when performing a security audit, when reviewing a PR that touches request handlers or database queries, when the user asks for a vulnerability scan, or when wiring security checks into CI.
npx skillsauth add santosomar/general-secure-coding-agent-skills static-vulnerability-detectorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill delegates to Project CodeGuard — the CoSAI open-source, model-agnostic security framework. Rather than maintaining a parallel vulnerability taxonomy, it dispatches to CodeGuard's rule set by language and sink class.
Upstream: https://github.com/cosai-oasis/project-codeguard/tree/main/skills/software-security
Given the language of the code under review, apply these CodeGuard rules:
| Finding class | CodeGuard rule |
| -------------------------------------- | ------------------------------------------- |
| SQL/NoSQL/LDAP/OS-command injection | codeguard-0-input-validation-injection |
| XXE, unsafe deserialization | codeguard-0-xml-and-serialization |
| XSS, DOM sinks, CSP, CSRF | codeguard-0-client-side-web-security |
| Path traversal, upload validation | codeguard-0-file-handling-and-uploads |
| IDOR, missing authz, mass assignment | codeguard-0-authorization-access-control |
| SSRF, missing authn, rate limits | codeguard-0-api-web-services |
For the full language→rules table, see the upstream SKILL.md. Always-apply rules (codeguard-1-hardcoded-credentials, codeguard-1-crypto-algorithms, codeguard-1-digital-certificates) run regardless of language.
development
Extracts human-readable pseudocode from a verified formal artifact (Dafny, Lean, TLA+) while preserving the verified properties as annotations, so the proof-carrying logic can be reimplemented in a production language. Use when porting verified code to an unverified target, when documenting what a formal spec actually does, or when handing a verified algorithm to an implementer.
development
Translates natural-language or pseudocode descriptions of concurrent and distributed systems into TLA+ specifications ready for the TLC model checker. Identifies state variables, actions, type invariants, safety properties, and liveness properties from the description. Use when formalizing a protocol, when the user describes a distributed algorithm to verify, when designing a consensus or locking scheme, or when starting formal verification of a concurrent system.
testing
Reduces a TLA+ model so TLC can actually check it — shrinks constants, adds state constraints, abstracts data, or applies symmetry — when the state space is too large to enumerate. Use when TLC runs out of memory, when checking takes hours, or when a spec works at N=2 and you need confidence at larger scale.
development
TLA+-specific instance of model-guided repair — reads a TLC error trace, identifies the enabling condition that should have been false, strengthens the corresponding action, and maps the fix to source code. Use when TLC reports an invariant violation or deadlock and you have the code-to-TLA+ mapping from extraction.