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 ```bash 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.