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.
npx skills add marduk191/qwen3_mcp --skill sarif-parsinggit clone --depth 1 https://github.com/marduk191/qwen3_mcpWrote 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.
[](https://agentmods.dev/skills/marduk191/qwen3_mcp/sarif-parsing)<a href="https://agentmods.dev/skills/marduk191/qwen3_mcp/sarif-parsing"><img src="https://agentmods.dev/badge/skills/marduk191/qwen3_mcp/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.
<a href="https://agentmods.dev/skills/marduk191/qwen3_mcp/sarif-parsing"><img src="https://agentmods.dev/badge/skills/marduk191/qwen3_mcp/sarif-parsing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.03695 |
| Opus 5.5 | $0.00022 | $0.01478 |
| Sonnet 5.5 | $0.00011 | $0.00739 |
| Haiku 4.5 | $0.00006 | $0.00369 |
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 2mo ago.
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 This is a copy
88% identical to sarif-parsing — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 480 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.
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.
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.
- 2mo ago First seen · 480 lines · 56 tokens per session scan A cd105f0d4498
sarif-parsing is a skill published in the GitHub repository marduk191/qwen3_mcp (14 stars, last pushed 9mo ago), licensed MIT. It adds 56 tokens to every session and 3,695 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to sarif-parsing, differing in 12 lines, and is treated as a copy.
sarif-parsing compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| sarif-parsingmarduk191/qwen3_mcp | Skill | 14 | 9mo ago | MIT | A | 56 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |