Official agent skill

Sarif Parsing

by trailofbits in trailofbits/skills

Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners.

OfficialCC-BY-SA-4.0Auto-check: notesSecurity

Install Sarif Parsing

skills CLI
$ npx skills add trailofbits/skills --skill sarif-parsing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install trailofbits/skills sarif-parsing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/trailofbits/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/static-analysis/skills/sarif-parsing .claude/skills/sarif-parsing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sarif-parsing
GitHub stars
7.4k
Used in
3 other repos
Token cost
~4.4k tokens
SKILL.md length
894 words
Files
8 (incl. assets)
Skills in repo
79
Repo updated
First seen
Licence
CC-BY-SA-4.0

At a glance

Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners.

  • Works in 5 steps: Path Normalization Issues → Fingerprint Mismatch Across Runs → Missing or Incomplete Data → …
  • Read scan results
  • SKILL.md covers When to Use, When NOT to Use, SARIF Structure Overview and Tool Selection Guide, plus 10 more sections
  • Runs Python scripts from its folder; calls jq, uv and npm; reaches json.schemastore.org

What it does

Sarif Parsing is an agent skill from trailofbits/skills, published by the product's own GitHub organization. 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.

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including assets (for example `agents/openai.yaml`, `resources/jq-queries.md` and `resources/sarif_helpers.py`).

It sits in Security, covering Static analysis and SAST. It works with Semgrep and Python. The repository describes itself as: Trail of Bits Claude Code skills for security research, vulnerability detection, and audit workflows. The licence is CC-BY-SA-4.0.

When your agent uses it

  • Read scan results
  • Aggregate findings
  • Deduplicate alerts
  • Process sarif output

Example prompts

  • “parse sarif”
  • “read scan results”
  • “aggregate findings”
  • “/sarif-parsing”

Requirements

  • Python 3
  • Node.js
  • Pre-approved tools (allowed-tools): Bash, Read, Glob, Grep

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Path Normalization Issues
  2. Fingerprint Mismatch Across Runs
  3. Missing or Incomplete Data
  4. Large File Performance
  5. Schema Validation

What it can do on your machine

Read from SKILL.md and the folder at commit 82fe822. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Glob
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • jq
    • uv
    • npm
    • brew
    • apt
    • go

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • json.schemastore.org

    Also links to:

    • github.com
    • docs.oasis-open.org
    • docs.github.com
    • sarifweb.azurewebsites.net

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Sarif Parsing loads about 4.4k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 894 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Glob, Grep

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from trailofbits/skills at commit 82fe822, republished under its CC-BY-SA-4.0 licence (© trailofbits). 894 words, ~4,439 tokens.

Download SKILL.mdSave it as .claude/skills/sarif-parsing/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
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
    │   ├── ruleIndex (index into tool.driver.rules[])
    │   ├── level (OPTIONAL, inherited from the rule when absent)
    │   ├── message.text
    │   ├── locations[]
    │   │   └── physicalLocation
    │   │       ├── artifactLocation.uri
    │   │       └── region (startLine, startColumn, etc.)
    │   ├── fingerprints{}
    │   └── partialFingerprints{}
    └── artifacts[] (scanned files metadata)
Severity Is Not Always on the Result

result.level is optional. CodeQL omits it on every result and records severity on the rule as defaultConfiguration.level, which the result inherits. Read result.level directly and a CodeQL run scores as clean however many errors it found, which is how a severity gate ends up exiting 0 on a failing repo.

Resolve severity in this order (SARIF 2.1.0 section 3.27.10):

  1. kind other than "fail" (a pass/notApplicable record), so "none"
  2. result.level, when present
  3. the matched rule's defaultConfiguration.level, joining ruleIndex into runs[].tool.driver.rules[], or matching ruleId against rules[].id when the tool omits ruleIndex
  4. "warning", the SARIF default

Every severity query in this skill starts from that resolution. In jq it is the LEVEL_FN definition in {baseDir}/resources/jq-queries.md; in Python it is resolve_level(result, run) in {baseDir}/resources/sarif_helpers.py.

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 CaseToolInstall / run
Quick CLI queriesjqbrew install jq / apt install jq
Python scripting (simple)pysarifuv run --with pysarif python script.py
Python scripting (advanced)sarif-toolsuv run --with sarif-tools python script.py
.NET applicationsSARIF SDKNuGet package
JavaScript/Node.jssarif-jsnpm package
Go applicationsgarifgo get github.com/chavacava/garif
ValidationSARIF Validatorsarifweb.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

# Severity resolution, needed by every query below that filters on level.
# See resources/jq-queries.md for the annotated version.
LEVEL_FN='
  def rule($run):
    . as $r
    | ($run.tool.driver.rules // []) as $rules
    | (if ($r.ruleIndex | type) == "number" and $r.ruleIndex >= 0
       then $rules[$r.ruleIndex] else null end)
      // first($rules[] | select(.id == $r.ruleId))
      // null;
  def level($run):
    . as $r
    | if ($r.kind // "fail") != "fail" then "none"
      else ($r.level // rule($run).defaultConfiguration.level // "warning") end;
'

# Extract errors only
jq "$LEVEL_FN"'.runs[] as $run | $run.results[] | select(level($run) == "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 "$LEVEL_FN"'[.runs[] as $run | $run.results[] | select(level($run) == "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:
        # pysarif fills a missing result.level with "warning", so .level here is NOT the
        # rule-inherited severity: a CodeQL error (no level on the result, severity on the
        # rule) reads as "warning". Gate severity with Strategy 1's level() or with
        # resolve_level() in resources/sarif_helpers.py, which resolve it from the rule.
        print(f"  {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 by severity. sarif-tools hands back raw result dicts, and a result's level may
# live on its rule, so resolve it against the run instead of reading r["level"].
from sarif_helpers import extract_findings, filter_by_level, load_sarif

high_severity = filter_by_level(extract_findings(load_sarif("results.sarif")), "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 results.sarif > report.html

Strategy 4: Aggregating Multiple SARIF Files

When combining results from multiple tools:

python
import json

from sarif_helpers import deduplicate, extract_findings

def aggregate_sarif_files(sarif_paths: list[str]) -> dict:
    """Combine multiple SARIF files into one."""
    aggregated = {
        "version": "2.1.0",
        "$schema": "https://json.schemastore.org/sarif-2.1.0.json",
        "runs": []
    }

    for path in sarif_paths:
        with open(path) as f:
            sarif = json.load(f)
            aggregated["runs"].extend(sarif.get("runs", []))

    return aggregated

unique = deduplicate(extract_findings(aggregate_sarif_files(["tool1.sarif", "tool2.sarif"])))

deduplicate() prefers whatever fingerprints or partialFingerprints the tool supplied and falls back to hashing rule ID, the whole normalized path, line, and message. Keep the directory in that key: the same rule at the same line in auth/login.py and admin/login.py is two findings, and a basename-only key throws one of them away.

Show full SKILL.md (393 more words)Show less

Strategy 5: Extracting Actionable Data

resources/sarif_helpers.py covers this with the standard library alone. extract_findings() returns Finding objects whose severity is already resolved, and filter_by_level(), sort_by_severity(), deduplicate() and diff_findings() consume those:

python
from sarif_helpers import extract_findings, filter_by_level, load_sarif, sort_by_severity

findings = sort_by_severity(extract_findings(load_sarif("results.sarif")))
for f in filter_by_level(findings, "error"):
    print(f"{f.file_path}:{f.start_line} [{f.level}] {f.rule_id}: {f.message}")

Writing your own extractor, severity is the part that goes wrong silently:

python
def resolve_level(result: dict, run: dict) -> str:
    """Severity of a result: its own level, else its rule's default, else "warning"."""
    if result.get("kind", "fail") != "fail":
        return "none"
    if result.get("level"):
        return result["level"]

    rules = run.get("tool", {}).get("driver", {}).get("rules", [])
    index = result.get("ruleIndex")
    rule = rules[index] if isinstance(index, int) and 0 <= index < len(rules) else next(
        (r for r in rules if r.get("id") == result.get("ruleId")), {}
    )
    return rule.get("defaultConfiguration", {}).get("level") or "warning"

Results carry ruleIndex on some tools and only ruleId on others, so a resolver that joins one way alone silently returns the default for every result the other kind of tool produces.

Common Pitfalls and Solutions

1. Path Normalization Issues

Different tools report paths differently (absolute, relative, URI-encoded), so file:///src/a%20b.py and src/a b.py can be the same file. Strip the file:// scheme, percent-decode, resolve against a base path, and normalize separators before comparing or hashing anything: normalize_path() in resources/sarif_helpers.py does all four.

2. Fingerprint Mismatch Across Runs

Fingerprints may not match if:

  • File paths differ between environments
  • Tool versions changed fingerprinting algorithm
  • Code was reformatted (changing line numbers)

Solution: Use multiple fingerprint strategies:

python
def compute_stable_fingerprint(result: dict, file_content: str = None) -> str:
    """Compute environment-independent fingerprint."""
    import hashlib

    components = [
        result.get("ruleId", ""),
        result.get("message", {}).get("text", "")[:100],  # First 100 chars
    ]

    # Add code snippet if available
    if file_content and result.get("locations"):
        region = result["locations"][0].get("physicalLocation", {}).get("region", {})
        if region.get("startLine"):
            lines = file_content.split("\n")
            line_idx = region["startLine"] - 1
            if 0 <= line_idx < len(lines):
                # Normalize whitespace
                components.append(lines[line_idx].strip())

    return hashlib.sha256("".join(components).encode()).hexdigest()[:16]
3. Missing or Incomplete Data

SARIF allows many optional fields. Always use defensive access:

python
def safe_get_location(result: dict) -> tuple[str, int]:
    """Safely extract file and line from result."""
    try:
        loc = result.get("locations", [{}])[0]
        phys = loc.get("physicalLocation", {})
        file_path = phys.get("artifactLocation", {}).get("uri", "unknown")
        line = phys.get("region", {}).get("startLine", 0)
        return file_path, line
    except (IndexError, KeyError, TypeError):
        return "unknown", 0
4. Large File Performance

For very large SARIF files (100MB+):

python
import ijson  # run via: uv run --with ijson

def stream_results(sarif_path: str):
    """Stream results without loading entire file."""
    with open(sarif_path, "rb") as f:
        # Stream through results arrays
        for result in ijson.items(f, "runs.item.results.item"):
            yield result
5. Schema Validation

Validate before processing to catch malformed files:

bash
# Using ajv-cli
npm install -g ajv-cli
ajv validate -s sarif-schema-2.1.0.json -d results.sarif

# Using Python jsonschema
uv run --with jsonschema python your_script.py   # e.g. the function below
python
from jsonschema import validate, ValidationError
import json

def validate_sarif(sarif_path: str, schema_path: str) -> bool:
    """Validate SARIF file against schema."""
    with open(sarif_path) as f:
        sarif = json.load(f)
    with open(schema_path) as f:
        schema = json.load(f)

    try:
        validate(sarif, schema)
        return True
    except ValidationError as e:
        print(f"Validation error: {e.message}")
        return False

CI/CD Integration Patterns

GitHub Actions
yaml
- name: Upload SARIF
  uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: results.sarif

- name: Check for high severity
  run: |
    # select(.level == "error") counts zero on CodeQL output, which records severity on
    # the rule instead. Resolve the level or the gate passes on a repo full of errors.
    HIGH_COUNT=$(jq '
      def rule($run):
        . as $r
        | ($run.tool.driver.rules // []) as $rules
        | (if ($r.ruleIndex | type) == "number" and $r.ruleIndex >= 0
           then $rules[$r.ruleIndex] else null end)
          // first($rules[] | select(.id == $r.ruleId))
          // null;
      def level($run):
        . as $r
        | if ($r.kind // "fail") != "fail" then "none"
          else ($r.level // rule($run).defaultConfiguration.level // "warning") end;
      [.runs[] as $run | $run.results[] | select(level($run) == "error")] | length
    ' results.sarif)
    if [ "$HIGH_COUNT" -gt 0 ]; then
      echo "Found $HIGH_COUNT high severity issues"
      exit 1
    fi
Fail on New Issues
python
from sarif import loader

def check_for_regressions(baseline: str, current: str) -> int:
    """Return count of new issues not in baseline."""
    baseline_data = loader.load_sarif_file(baseline)
    current_data = loader.load_sarif_file(current)

    baseline_fps = {get_fingerprint(r) for r in baseline_data.get_results()}
    new_issues = [r for r in current_data.get_results()
                  if get_fingerprint(r) not in baseline_fps]

    return len(new_issues)

Key Principles

  1. Validate first: Check SARIF structure before processing
  2. Resolve severity, never read result.level: it is optional, and CodeQL always omits it
  3. Handle optionals: Many fields are optional; use defensive access
  4. Normalize paths: Tools report paths differently; normalize early
  5. Fingerprint wisely: Combine multiple strategies for stable deduplication
  6. Stream large files: Use ijson or similar for 100MB+ files
  7. Aggregate thoughtfully: Preserve tool metadata when combining files

Skill Resources

For ready-to-use query templates, see {baseDir}/resources/jq-queries.md:

  • 40+ jq queries for common SARIF operations
  • LEVEL_FN - the severity resolution every filtering query starts from
  • Severity filtering, rule extraction, aggregation patterns

For Python utilities, see {baseDir}/resources/sarif_helpers.py:

  • resolve_level() - Severity from the result or the rule it inherits from
  • normalize_path() - Handle tool-specific path formats
  • compute_fingerprint() - Rule, normalized path, line, and message
  • deduplicate() - Remove duplicates across runs

Two SARIF fixtures live in {baseDir}/resources/fixtures, one with severity on the rules only and one with severity on the results. Each contains exactly one error, so a gate can be checked against a known answer before it is trusted.

© trailofbits, CC-BY-SA-4.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (assets) in plugins/static-analysis/skills/sarif-parsing of trailofbits/skills.

  • SKILL.md
  • agents/openai.yaml
  • assets/trail-of-bits-mark.svg
  • resources/fixtures/codeql-no-level.sarif
  • resources/fixtures/levels-on-results.sarif
  • resources/jq-queries.md
  • resources/sarif_helpers.py
  • resources/test_sarif_helpers.py

Open the folder on GitHubat commit 82fe822

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in trailofbits/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Sarif Parsing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Sarif Parsing compared with similar skills
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Detection Breadthdeonmenezes/mantishack504—~510Automated safety check: PassApache-2.0
Sast Scanningsickn33/agentic-awesome-skills47k1 repos~2.4kAutomated safety check: PassMIT
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    7.4k GitHub stars~2.5k tokensUpdated yesterday
    Auto-check: notes
  • Semgrep Security Scan

    trailofbits/skills

    Official

    Detects languages, proposes rulesets for approval, then runs the approved Semgrep scan across a codebase and merges the output into one SARIF file.

    7.4k GitHub stars~3.7k tokensUpdated yesterday
    Auto-check: notes
  • Burp Suite Project Parser

    trailofbits/skills

    Official

    Searches and extracts data from Burp Suite project files on the command line: regex searches over responses, audit findings, proxy history and site map data.

    7.4k GitHub starsUsed in 3 repos~4.2k tokens
    Auto-check: notes

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Questions about Sarif Parsing

What does Sarif Parsing do?

Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners. Sarif Parsing is an agent skill from trailofbits/skills, published by the product's own GitHub organization. Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners.

When should I use Sarif Parsing?

Sarif Parsing fits situations like: read scan results; aggregate findings; deduplicate alerts; process sarif output.

How do I install Sarif Parsing in Claude Code?

Run `npx skills add trailofbits/skills --skill sarif-parsing -a claude-code`. Or copy the skill folder (plugins/static-analysis/skills/sarif-parsing in trailofbits/skills) into .claude/skills/sarif-parsing in your project. Claude Code loads it when a task matches its description.

How do I install Sarif Parsing in Codex?

Run `npx skills add trailofbits/skills --skill sarif-parsing -a codex`. Or copy the skill folder (plugins/static-analysis/skills/sarif-parsing in trailofbits/skills) into .agents/skills/sarif-parsing in your project. Codex loads it when a task matches its description.

Can I use Sarif Parsing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add trailofbits/skills --skill sarif-parsing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sarif-parsing, .gemini/skills/sarif-parsing, .github/skills/sarif-parsing and .opencode/skills/sarif-parsing in your project.

What does Sarif Parsing need to run?

Going by SKILL.md and its folder, Sarif Parsing needs Python for the scripts in its folder and the command-line tools its instructions call (jq, uv, npm, brew, apt and go). Our summary lists: Python 3; Node.js. Its frontmatter pre-approves these tools: Bash, Read, Glob, Grep.

Does Sarif Parsing access the network?

SKILL.md names 5 domains. In commands or code: json.schemastore.org; the agent is likely to contact it when it follows the instructions. As links in the text: github.com, docs.oasis-open.org, docs.github.com and sarifweb.azurewebsites.net. This is read from the text; nothing was executed.

Is Sarif Parsing safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Sarif Parsing use?

Sarif Parsing is published under the CC-BY-SA-4.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sarif Parsing use?

About 4.4k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Sarif Parsing?

Skills that share tags, products or a category with Sarif Parsing: Kedro Security Review (kedro-org/kedro, 11k stars), Detection Breadth (deonmenezes/mantishack, 504 stars), Sast Scanning (sickn33/agentic-awesome-skills, 47k stars) and Sast Scanning (BagelHole/DevOps-Security-Agent-Skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sarif Parsing?

trailofbits (a GitHub organization, an official publisher) maintains it in trailofbits/skills, which has 7,440 GitHub stars. The repository holds 79 skills in this directory. The repository was last updated on October 7, 2026.

Source: trailofbits/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.