gitnexus-taint-analysis

Analyze source-to-sink data-flow findings in GitNexus's CFG/taint/PDG subsystem.

Updated Jun 23, 2026
One-click install
npx skills add https://github.com/guideops/agentic-shared --skill gitnexus-taint-analysis-guideops
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gitnexus-taint-analysis
Source: https://github.com/guideops/agentic-shared/tree/main/skills/gitnexus-taint-analysis
Command: npx skills add https://github.com/guideops/agentic-shared --skill gitnexus-taint-analysis-guideops

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides expert knowledge and tools for working with GitNexus's CFG/taint/PDG subsystem, helping users understand, extend, and reason about source→sink data-flow findings.

Core Features & Use Cases

  • Expert Knowledge: Offers detailed information on control-flow graphs, reaching definitions, and intra- + inter-procedural taint.
  • Use Case: When debugging false positives/negatives in --pdg output, or when reviewing and extending the taint code.
  • Layered Approach: Provides a layered substrate for understanding the build order and how each layer contributes to the analysis.

Quick Start

Use the gitnexus-taint-analysis skill to review the interprocedural taint code for a specific function.

Frequently Asked Questions about gitnexus-taint-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I debug false positives in source to sink taint analysis output?

Taint analysis tracks how untrusted data flows from sources to sinks using control-flow graphs and reaching definitions. It is needed when reviewing code for security vulnerabilities or understanding interprocedural data dependencies across function calls.

How do I debug false positives in source to sink data-flow findings?

To debug false positives in source to sink data-flow findings, examine the program dependence graph output and the layered taint analysis build order. Tracing reaching definitions and interprocedural control-flow edges helps isolate which layer introduces the incorrect path.

What's the best way to review and extend interprocedural taint code?

Reviewing interprocedural taint code requires understanding the layered build order of the analysis subsystem. Examine how each layer contributes to control-flow graphs and reaching definitions to extend source-to-sink data-flow tracking accurately.

How does a program dependence graph help with taint analysis?

A program dependence graph models both data and control dependencies between program statements. It provides the structural substrate needed to trace source-to-sink data-flow paths and reason about reaching definitions during interprocedural taint analysis.

Why does my interprocedural taint analysis miss certain data-flow paths?

Interprocedural taint analysis produces false negatives when reaching definitions or control-flow edges are incomplete across function boundaries. Debugging requires reviewing the interprocedural taint code and verifying the layered build order to locate missing source-to-sink paths.