Fact Checker

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

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 references — model/task (phi4/classify) and model:tag (phi4:latest)
• Dates — YYYY-MM-DD format
• Score values — decimal scores like 0.923, 1.000
• Percentages — 42%, 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.