audit-augmentation — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited audit-augmentation (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Projects findings from external tools (SARIF) and human auditors (weAudit) onto Trailmark code graphs as annotations and subgraphs.
trailmark skill)diagramming-code skill after augmenting)| 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 |
MANDATORY: If uv run trailmark fails, install trailmark first:
uv pip install trailmark# 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 \
--jsonfrom 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 lookupIf auto-detection is wrong for the target, rerun with an explicit language or comma-separated list such as python,rust.
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-analysisStep 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:
semgrep --sarif -o results.sarifor codeql database analyze --format=sarif-latest
.vscode/<username>.weaudit within the workspaceStep 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:
sarif:error with tainted subgraphhigh_blast_radiusprivilege_boundaryFindings are stored as standard Trailmark annotations:
finding (tool-generated) or audit_note (human notes)sarif:<tool_name> or weaudit:<author>[SEVERITY] rule-id: message (tool)
| 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) |
Findings are matched to graph nodes by file path and line range overlap:
root_pathlocation.file_path matches AND whose line range overlaps areselected
SARIF paths may be relative, absolute, or file:// URIs — all are handled. weAudit uses 0-indexed lines which are converted to 1-indexed automatically.
weAudit file format field reference
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.