How do I install the Fact Checker skill?

Run npx skillmds@latest add dvcrn/fact-checker in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Fact Checker safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Fact Checker?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Fact Checker free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Fact Checker?

dvcrn (@dvcrn) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Fact Checker by dvcrn — Agent Skill

Name: Fact Checker
Rating: 4.2 (5 reviews)
Author: dvcrn

File contents

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)

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

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

---name: fact-checkerdescription: Verify factual claims in content using web search and source cross-referencing. Use when reviewing content for accuracy before publishing.---# Fact-Checker: Verify Markdown Claims Against Source DataGiven a markdown draft file, this skill extracts every verifiable claim(numbers, dates, model names, scores, causal statements) and cross-referencesthem against available source data to produce a verification report.## Usage```bashpython3 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 truth2. Control Plane `/status` API at `http://localhost:8765/status` — live scored run data3. `projects/hybrid-control-plane/data/scores/*.json` — raw scored run files on disk4. `memory/*.md` — daily logs with timestamps and decisions5. `git log` in `projects/hybrid-control-plane/` — commit hashes, dates, authorship6. `projects/hybrid-control-plane/CHANGELOG.md` — sprint history## Output FormatEach 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 SkillWhen asked to "fact-check" or "verify" a draft blog post, report, ordocumentation file — run this skill and present the report to the user.If any claims are ❌ CONTRADICTED, flag them prominently and suggest corrections.## Instructions for Agent1. 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.

Frequently asked questions

Fact Checker