Sarif ParsingSAFE
Vigolium - High-fidelity vulnerability scanner fusing agentic AI with native speed, modularity, and precision
Overview
Vigolium - High-fidelity vulnerability scanner fusing agentic AI with native speed, modularity, and precision
abfda8d468e1OBSERVED · 2026-10-13Install
Commands as the repository documents them. They are shown, not run.
npm install -g ajv-cli
pip install jsonschema
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: sarif-parsing
description: >-
Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other
scanners. Triggers on "parse sarif", "read scan results", "aggregate findings", "deduplicate
alerts", or "process sarif output". Handles filtering, deduplication, format conversion, and
CI/CD integration of SARIF data. Does NOT run scans — use the Semgrep or CodeQL skills for that.
allowed-tools:
- Bash
- Read
- Glob
- Grep
---
# SARIF Parsing Best Practices
You are a SARIF parsing expert. Your role is to help users effectively read, analyze, and process SARIF files from static analysis tools.
## When to Use
Use this skill when:
- Reading or interpreting static analysis scan results in SARIF format
- Aggregating findings from multiple security tools
- Deduplicating or filtering security alerts
- Extracting specific vulnerabilities from SARIF files
- Integrating SARIF data into CI/CD pipelines
- Converting SARIF output to other formats
## When NOT to Use
Do NOT use this skill for:
- Running static analysis scans (use CodeQL or Semgrep skills instead)
- Writing CodeQL or Semgrep rules (use their respective skills)
- Analyzing source code directly (SARIF is for processing existing scan results)
- Triaging findings without SARIF input (use variant-analysis or audit skills)
## SARIF Structure Overview
SARIF 2.1.0 is the current OASIS standard. Every SARIF file has this hierarchical structure:
```
sarifLog
├── version: "2.1.0"
├── $schema: (optional, enables IDE validation)
└── runs[] (array of analysis runs)
├── tool
│ ├── driver
│ │ ├── name (required)
│ │ ├── version
│ │ └── rules[] (rule definitions)
│ └── extensions[] (plugins)
├── results[] (findings)
│ ├── ruleId
│ ├── level (error/warning/note)
│ ├── message.text
│ ├── locations[]
│ │ └── physicalLocation
│ │ ├── artifactLocation.uri
│ │ └── region (startLine, startColumn, etc.)
│ ├── fingerprints{}
│ └── partialFingerprints{}
└── artifacts[] (scanned files metadata)
```
### Why Fingerprinting Matters
Without stable fingerprints, you can't track findings across runs:
- **Baseline comparison**: "Is this a new finding or did we see it before?"
- **Regression detection**: "Did this PR introduce new vulnerabilities?"
- **Suppression**: "Ignore this known false positive in future runs"
Tools report different paths (`/path/to/project/` vs `/github/workspace/`), so path-based matching fails. Fingerprints hash the *content* (code snippet, rule ID, relative location) to create stable identifiers regardless of environment.
## Tool Selection Guide
| Use Case | Tool | Installation |
|----------|------|--------------|
| Quick CLI queries | jq | `brew install jq` / `apt install jq` |
| Python scripting (simple) | pysarif | `pip install pysarif` |
| Python scripting (advanced) | sarif-tools | `pip install sarif-tools` |
| .NET applications | SARIF SDK | NuGet package |
| JavaScript/Node.js | sarif-js | npm package |
| Go applications | garif | `go get github.com/chavacava/garif` |
| Validation | SARIF Validator | sarifweb.azurewebsites.net |
## Strategy 1: Quick Analysis with jq
For rapid exploration and one-off queries:
```bash
# Pretty print the file
jq '.' results.sarif
# Count total findings
jq '[.runs[].results[]] | length' results.sarif
# List all rule IDs triggered
jq '[.runs[].results[].ruleId] | unique' results.sarif
# Extract errors only
jq '.runs[].results[] | select(.level == "error")' results.sarif
# Get findings with file locations
jq '.runs[].results[] | {
rule: .ruleId,
message: .message.text,
file: .locations[0].physicalLocation.artifactLocation.uri,
line: .locations[0].physicalLocation.region.startLine
}' results.sarif
# Filter by severity and get count per rule
jq '[.runs[].results[] | select(.level == "error")] | group_by(.ruleId) | map({rule: .[0].ruleId, count: length})' results.sarif
# Extract findings for a specific file
jq --arg file "src/auth.py" '.runs[].results[] | select(.locations[].physicalLocation.artifactLocation.uri | contains($file))' results.sarif
```
## Strategy 2: Python with pysarif
For programmatic access with full object model:
```python
from pysarif import load_from_file, save_to_file
# Load SARIF file
sarif = load_from_file("results.sarif")
# Iterate through runs and results
for run in sarif.runs:
tool_name = run.tool.driver.name
print(f"Tool: {tool_name}")
for result in run.results:
print(f" [{result.level}] {result.rule_id}: {result.message.text}")
if result.locations:
loc = result.locations[0].physical_location
if loc and loc.artifact_location:
print(f" File: {loc.artifact_location.uri}")
if loc.region:
print(f" Line: {loc.region.start_line}")
# Save modified SARIF
save_to_file(sarif, "modified.sarif")
```
## Strategy 3: Python with sarif-tools
For aggregation, reporting, and CI/CD integration:
```python
from sarif import loader
# Load single file
sarif_data = loader.load_sarif_file("results.sarif")
# Or load multiple files
sarif_set = loader.load_sarif_files(["tool1.sarif", "tool2.sarif"])
# Get summary report
report = sarif_data.get_report()
# Get histogram by severity
errors = report.get_issue_type_histogram_for_severity("error")
warnings = report.get_issue_type_histogram_for_severity("warning")
# Filter results
high_severity = [r for r in sarif_data.get_results()
if r.get("level") == "error"]
```
**sarif-tools CLI commands:**
```bash
# Summary of findings
sarif summary results.sarif
# List all results with details
sarif ls results.sarif
# Get results by severity
sarif ls --level error results.sarif
# Diff two SARIF files (find new/fixed issues)
sarif diff baseline.sarif current.sarif
# Convert to other formats
sarif csv results.sarif > results.csv
sarif html resultTrust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (1)
AGENTS.md
Gates applied: no_behavioural_pass.
abfda8d468e1full audit observations/trust-audit/skill/vigolium__sarif-parsing.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-13 | abfda8d468e1 | SAFE | B | 89 | first audit |
Questions
What does the Sarif Parsing skill do?
Vigolium - High-fidelity vulnerability scanner fusing agentic AI with native speed, modularity, and precision
Is Sarif Parsing safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Sarif Parsing access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (abfda8d468e1), read on 2026-10-13. The repository is watched, and a new audit runs when it changes — this is the first audit.