audit-augmentation

SKILL.md

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 pip install trailmark

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 workspace

Binary graph (v0.4+): External JSON with artifact, functions, and
calls fields. Trailmark imports this graph; it does not disassemble
binaries itself.

Step 3: Run augmentation via engine.augment_sarif() or
engine.augment_weaudit(). For binary graphs, run engine.augment_binary()
only after the Version Gate succeeds. Check unmatched_findings in SARIF and
weAudit results — 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

For one candidate finding that needs a reachability verdict or PoC handoff,
continue with trailmark-finding-triage and use the augmented node as the
bound candidate.

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

Subgraph
Contents

sarif:error
Nodes with SARIF error-level findings

sarif:warning
Nodes with SARIF warning-level findings

sarif:note
Nodes with SARIF note-level findings

sarif:<tool>
Nodes flagged by a specific tool

weaudit:high
Nodes with high-severity weAudit findings

weaudit:medium
Nodes with medium-severity weAudit findings

weaudit:low
Nodes with low-severity weAudit findings

weaudit:findings
All weAudit findings (entryType=0)

weaudit:notes
All weAudit notes (entryType=1)

binary:<artifact>
Binary function nodes imported from a v0.4+ binary graph

How Matching Works

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

Finding file path is normalized relative to the graph's root_path

Nodes whose location.file_path matches AND whose line range overlaps are
selected

The tightest match (smallest span) is preferred

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.

Binary graph imports create origin=binary function nodes, origin=proxy
external proxy nodes for unresolved binary calls, and inferred
corresponds_to edges when a binary function maps back to a source node. The
expected JSON shape is intentionally small:

{
  "artifact": {"name": "libexample", "architecture": "x86_64", "sha256": "..."},
  "functions": [
    {"symbol": "parse_packet", "address": "0x401000",
     "source": {"file": "src/parser.c", "line": 42}}
  ],
  "calls": [
    {"source": "parse_packet", "target": "malloc", "confidence": "inferred"}
  ]
}

Supporting Documentation

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

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