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/audit-augmentation

Augments Trailmark code graphs with external audit findings from SARIF static analysis results, weAudit annotation files, and version-gated Trailmark 0.4.x binary-analysis graph exports. Maps findings to graph nodes by file and line overlap, creates severity-based subgraphs, and

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$ npx -y skills add trailofbits/skills --skill audit-augmentation --agent claude-code

SKILL.md

audit-augmentation.SKILL.md
name: audit-augmentation
description: >
  Augments Trailmark code graphs with external audit findings from SARIF static
  analysis results, weAudit annotation files, and version-gated Trailmark 0.4.x
  binary-analysis graph exports. Maps findings to graph nodes by
  file and line overlap, creates severity-based subgraphs, and enables
  cross-referencing findings with pre-analysis data (blast radius, taint, etc.).
  Use when projecting SARIF results onto a code graph, overlaying weAudit
  annotations, importing binary graph findings, cross-referencing Semgrep,
  CodeQL, or binary-analysis findings with call graph data, or visualizing audit
  findings in the context of code structure.

Audit Augmentation

Projects findings from external tools (SARIF) and human auditors (weAudit) onto Trailmark code graphs as annotations and subgraphs. Trailmark 0.4.0+ can also import an external binary-analysis graph JSON export via `engine.augment_binary()`.

When to Use

  • Importing Semgrep, CodeQL, or other SARIF-producing tool results into a graph
  • Importing weAudit audit annotations into a graph
  • Importing binary-analysis graph data into a source graph (Trailmark 0.4.0+)
  • Cross-referencing static analysis findings with blast radius or taint data
  • Querying which functions have high-severity findings
  • Visualizing audit coverage alongside code structure
  • Preparing one SARIF or weAudit result for `trailmark-finding-triage`

When NOT to Use

  • Running static analysis tools (use semgrep/codeql directly, then import)
  • Building the code graph itself (use the `trailmark` skill)
  • Generating diagrams (use the `diagramming-code` skill after augmenting)

Rationalizations to Reject

| Rationalization | Why It's Wrong | Required Action | |-----------------|----------------|-----------------| | "The user only asked about SARIF, skip pre-analysis" | Without pre-analysis, you can't cross-reference findings with blast radius or taint | Always run `engine.preanalysis()` before augmenting | | "Unmatched findings don't matter" | Unmatched findings may indicate parsing gaps or out-of-scope files | Report unmatched count and investigate if high | | "One severity subgraph is enough" | Different severities need different triage workflows | Query all severity subgraphs, not just `error` | | "SARIF results speak for themselves" | Findings without graph context lack blast radius and taint reachability | Cross-reference with pre-analysis subgraphs | | "weAudit and SARIF overlap, pick one" | Human auditors and tools find different things | Import both when available | | "Tool isn't installed, I'll do it manually" | Manual analysis misses what tooling catches | Install trailmark first |

---

Installation

**MANDATORY:** If `uv run trailmark` fails, install trailmark first:

uv tool install trailmark
# Python snippets: uv run --with trailmark python -   (a tool env is not importable)

Version Gate

SARIF and weAudit augmentation are v0.2-safe. Binary graph augmentation is Trailmark 0.4.0+ only. Before calling `engine.augment_binary()`, check:

if not hasattr(engine, "augment_binary"):
    raise RuntimeError("Binary augmentation requires Trailmark >= 0.4.0")

On Trailmark 0.5.0+, known links between source functions and imported binary or external endpoints can also be declared once in `.trailmark/links.toml` (see the main `trailmark` skill's Repository Links section) instead of being re-derived per session. Declared external endpoints materialize as `proxy.external:<symbol>` nodes on every parse.

Quick Start

CLI

# Augment with SARIF
uv run trailmark augment {targetDir} --sarif results.sarif

# Augment with weAudit
uv run trailmark augment {targetDir} --weaudit .vscode/alice.weaudit

# Both at once, output JSON
uv run trailmark augment {targetDir} \
    --sarif results.sarif \
    --weaudit .vscode/alice.weaudit \
    --json

Binary graph augmentation is programmatic in Trailmark 0.4.0+; do not invent a CLI flag if `trailmark augment --help` does not show one.

Programmatic API

from trailmark.query.api import QueryEngine

engine = QueryEngine.from_directory("{targetDir}", language="auto")

# Run pre-analysis first for cross-referencing
engine.preanalysis()

# Augment with SARIF
result = engine.augment_sarif("results.sarif")
# result: {matched_findings: 12, unmatched_findings: 3, subgraphs_created: [...]}

# Augment with weAudit
result = engine.augment_weaudit(".vscode/alice.weaudit")

# Augment with an external binary graph export (v0.4+)
if hasattr(engine, "augment_binary"):
    result = engine.augment_binary("binary_graph.json")

# Query findings
engine.findings()                                       # All findings
engine.subgraph("sarif:error")                          # High-severity SARIF
engine.subgraph("weaudit:high")                         # High-severity weAudit
engine.subgraph("sarif:semgrep")                        # By tool name
engine.annotations_of("function_name")                  # Per-node lookup

If auto-detection is wrong for the target, rerun with an explicit language or comma-separated list such as `python,rust`.

Workflow

Augmentation Progress:
- [ ] Step 1: Build graph and run pre-analysis
- [ ] Step 2: Locate SARIF/weAudit/binary graph files
- [ ] Step 3: Run augmentation
- [ ] Step 4: Inspect results and subgraphs
- [ ] Step 5: Cross-reference with pre-analysis

**Step 1:** Build the graph and run pre-analysis for blast radius and taint context:

engine = QueryEngine.from_directory("{targetDir}", language="auto")
engine.preanalysis()

If auto-detection is wrong for the target, rerun with an explicit language or comma-separated list such as `python,rust`.

**Step 2:** Locate input files:

  • **SARIF**: Usually output by tools like `semgrep --sarif -o results.sarif`

or `codeql database analyze --format=sarif-latest`

  • **weAudit**: Stored in `.vscode/<username>.weaudit` within the works
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Questions about audit-augmentation.

Which coding agents can use audit-augmentation?
audit-augmentation installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.