skills/security/taint-instrumentation-assistant/SKILL.md
Sets up taint tracking by defining sources, sinks, and sanitizers from Project CodeGuard's input-validation taxonomy, then configures the target tool (CodeQL, Semgrep, custom instrumentation). Use when wiring taint analysis into CI, when the user asks for taint tracking, or when you need a source/sink catalog for a specific language.
npx skillsauth add santosomar/general-secure-coding-agent-skills taint-instrumentation-assistantInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill delegates to Project CodeGuard for its source/sink/sanitizer taxonomy — specifically codeguard-0-input-validation-injection, which defines the trust boundaries (HTTP params, env, files, IPC) and dangerous sinks (query execution, shell, eval, filesystem) per language.
Upstream: https://github.com/cosai-oasis/project-codeguard/tree/main/skills/software-security
| Taint component | CodeGuard source |
| --------------- | -------------------------------------------------------------- |
| Sources | codeguard-0-input-validation-injection → "Core Strategy" trust boundaries, per-framework request-object tables |
| Sinks | Same rule → SQL/LDAP/OS-command sections; plus codeguard-0-xml-and-serialization for deserialization sinks |
| Sanitizers | Same rule → parameterization APIs, escaping functions, allow-list validators listed as "primary defense" |
.ql source/sink predicates, Semgrep pattern-sources/pattern-sinks, or inline annotations.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.