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BytesAgain β€Ί Skills β€Ί Fact Checker
πŸ¦€ ClawHub

Fact Checker

by @nissan

Verify claims, numbers, and facts in markdown drafts against source data. Use when: reviewing blog posts, reports, or documentation for accuracy before publi...

Versionv1.0.4
Downloads2,036
Installs14
Stars⭐ 1
#clawhub
TERMINAL
clawhub install fact-checker

πŸ“– About This Skill

name: fact-checker version: 1.0.3 description: | Verify claims, numbers, and facts in markdown drafts against source data. Use when: reviewing blog posts, reports, or documentation for accuracy before publication. Cross-references against FINDINGS.md, live APIs, scored run files, memory logs, and git history. author: nissan tags: - fact-checking - content - verification requires: bins: - python3 metadata: openclaw: emoji: "πŸ”" network: outbound: false reason: "All verification against local files and APIs only" subprocess: note: "Runs python3 scripts for verification β€” legitimate use" security_notes: "All operations are performed locally. No data leaves the user's machine. Verification cross-references local files, local git history, and a local API (localhost). The subprocess note is for running the bundled Python fact-check script β€” not arbitrary code execution." allowed-tools: - Read - exec
Last used: 2026-03-24 Memory references: 1 Status: Active

Fact-Checker: Verify Markdown Claims Against Source Data

Given a markdown draft file, this skill extracts every verifiable claim (numbers, dates, model names, scores, causal statements) and cross-references them against available source data to produce a verification report.

Usage

python3 skills/fact-checker/scripts/fact_check.py 
python3 skills/fact-checker/scripts/fact_check.py  --output report.md

What It Checks

Claim types extracted

  • Numeric claims β€” integers and floats with surrounding context
  • Model references β€” model/task (phi4/classify) and model:tag (phi4:latest)
  • Dates β€” YYYY-MM-DD format
  • Score values β€” decimal scores like 0.923, 1.000
  • Percentages β€” 42%, 95.3%

    • Source data consulted (in priority order)

    1. projects/hybrid-control-plane/FINDINGS.md β€” primary source of truth 2. Control Plane /status API at http://localhost:8765/status β€” live scored run data 3. projects/hybrid-control-plane/data/scores/*.json β€” raw scored run files on disk 4. memory/*.md β€” daily logs with timestamps and decisions 5. git log in projects/hybrid-control-plane/ β€” commit hashes, dates, authorship 6. projects/hybrid-control-plane/CHANGELOG.md β€” sprint history

    Output Format

    Each claim produces one line:

    βœ… CONFIRMED:    "phi4/classify scored 1.000" β†’ /status API: phi4_latest_classify mean=1.000 n=23
⚠️ UNVERIFIABLE: "this took about a day" β†’ no timestamp correlation found in logs
❌ CONTRADICTED: "909 runs" β†’ /status API shows 958 total runs (stale number?)

    Followed by a summary count of confirmed / unverifiable / contradicted claims.

    When To Use This Skill

    When asked to "fact-check" or "verify" a draft blog post, report, or documentation file β€” run this skill and present the report to the user. If any claims are ❌ CONTRADICTED, flag them prominently and suggest corrections.

    Instructions for Agent

    1. Run the script with the path to the draft file. 2. Parse the output report. 3. Summarise key findings β€” especially any ❌ CONTRADICTED claims. 4. Suggest specific corrections with the correct values from the evidence. 5. If the /status API is unavailable, note it and rely on FINDINGS.md + score files.

    πŸ’‘ Examples

    python3 skills/fact-checker/scripts/fact_check.py 
python3 skills/fact-checker/scripts/fact_check.py  --output report.md