fact-checker

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.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "fact-checker" with this command: npx skills add nissan/fact-checker

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

What It Checks

Claim types extracted

  • Numeric claims — integers and floats with surrounding context
  • Model referencesmodel/task (phi4/classify) and model:tag (phi4:latest)
  • DatesYYYY-MM-DD format
  • Score values — decimal scores like 0.923, 1.000
  • Percentages42%, 95.3%

Source data consulted (in priority order)

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

Output Format

Each claim produces one line:

✅ CONFIRMED:    "phi4/classify scored 1.000" → /status API: phi4_latest_classify mean=1.000 n=23
⚠️ UNVERIFIABLE: "this took about a day" → no timestamp correlation found in logs
❌ CONTRADICTED: "909 runs" → /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 — 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 — 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.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Security

GauntletScore

Trust verification for AI output — verify any document or code before you act on it

Registry SourceRecently Updated
3010Profile unavailable
Automation

Multi-Agent Debate

Verify facts, reduce hallucinations, and explore multiple viewpoints through structured multi-agent debate. Multiple agents independently answer the same que...

Registry SourceRecently Updated
1110Profile unavailable
Automation

2O Human Verification

Human verification for AI agents. Submit claims, draft responses, or observation requests to human domain experts via the 2O API. Returns structured verdicts...

Registry SourceRecently Updated
2570Profile unavailable
Research

Facticity.AI Complete Integration

Complete Facticity.AI integration - fact-check claims, extract claims from content, transcribe links, check link reliability, check credits, and monitor task...

Registry SourceRecently Updated
5000Profile unavailable