name: audit-augmentation description: > Augments Trailmark code graphs with external audit findings from SARIF static analysis results and weAudit annotation files. 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, cross-referencing Semgrep or CodeQL 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.

When to Use

• Importing Semgrep, CodeQL, or other SARIF-producing tool results into a graph
• Importing weAudit audit annotations into a graph
• Cross-referencing static analysis findings with blast radius or taint data
• Querying which functions have high-severity findings
• Visualizing audit coverage alongside code structure

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

Installation

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

uv pip install trailmark

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

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")

# 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 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 workspace

Step 3: Run augmentation via engine.augment_sarif() or engine.augment_weaudit(). Check unmatched_findings in the result — these are findings whose file/line locations didn't overlap any parsed code unit.

Step 4: Query findings and subgraphs. Use engine.findings() to list all annotated nodes. Use engine.subgraph_names() to see available subgraphs.

Step 5: Cross-reference with pre-analysis data to prioritize:

• Findings on tainted nodes: overlap sarif:error with tainted subgraph
• Findings on high blast radius nodes: overlap with high_blast_radius
• Findings on privilege boundaries: overlap with privilege_boundary

Annotation Format

Findings are stored as standard Trailmark annotations:

• Kind: finding (tool-generated) or audit_note (human notes)
• Source: sarif:<tool_name> or weaudit:<author>
• Description: Compact single-line: [SEVERITY] rule-id: message (tool)

Subgraphs Created

How Matching Works

Findings are matched to graph nodes by file path and line range overlap:

1. Finding file path is normalized relative to the graph's root_path
2. Nodes whose location.file_path matches AND whose line range overlaps are selected
3. The tightest match (smallest span) is preferred
4. If a finding's location doesn't overlap any node, it counts as unmatched

SARIF paths may be relative, absolute, or file:// URIs — all are handled. weAudit uses 0-indexed lines which are converted to 1-indexed automatically.

Supporting Documentation

• references/formats.md — SARIF 2.1.0 and weAudit file format field reference