sarif-parsing

sarif-parsing is a skill for Claude Code from waybarrios/opencode-power-pack. It costs 91 tokens per session (3,710 once invoked), scanned A, original, MIT.

A skill for reading and processing SARIF files, a standard JSON format for results from code-scanning tools such as CodeQL or Semgrep. It works with findings that have already been produced by a scanner.

In plain words
What is it for?
Use it to parse SARIF, aggregate results from several scanners, extract or filter vulnerabilities, deduplicate alerts, and connect scan output to CI/CD pipelines, which automatically test code during delivery.
Why use it?
It helps combine scan results, remove duplicate alerts, filter findings, and convert them for other uses without confusing result processing with running scans or investigating source code directly.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the opencode-power-pack plugin — 54 skills shipped together

Good fit Use it to parse SARIF, aggregate results from several scanners, extract or filter vulnerabilities, deduplicate alerts, and connect scan output to CI/CD pipelines, which automatically test code during delivery.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/waybarrios/opencode-power-pack/sarif-parsing
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add waybarrios/opencode-power-pack --skill sarif-parsing
Clone the repo
git clone --depth 1 https://github.com/waybarrios/opencode-power-pack

Made for: Claude Code.

Or install opencode-power-pack, the plugin that ships this one along with the rest of its 54 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sarif-parsing

README.md
[![agentmods](https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/sarif-parsing/github.svg)](https://agentmods.dev/skills/waybarrios/opencode-power-pack/sarif-parsing)
Your own site
<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/sarif-parsing"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/sarif-parsing/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for sarif-parsing

Your own site · 80×15
<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/sarif-parsing"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/sarif-parsing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,710 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00091 $0.03710
Opus 5 $0.00046 $0.01855
Sonnet 5 $0.00018 $0.00742
Haiku 4.5 $0.00009 $0.00371

Measured 9d ago against content hash 8ccbfabf10e3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

sarif-parsing scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (resources/sarif_helpers.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

from urllib.parse import unquote
Origin

Copies of this mod

4 near-identical copies found in the catalogue:

skills/sarif-parsing/SKILL.md · 476 lines

How it starts

The opening of the file, as written. The whole thing — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 476 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 476 lines · 91 tokens per session scan A 8ccbfabf10e3

Subscribe to this mod's changes

sarif-parsing is a skill published in the GitHub repository waybarrios/opencode-power-pack (498 stars, last pushed 2d ago), licensed MIT. It adds 91 tokens to every session and 3,710 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.