What makes Fact Checker notable?

3.4K downloads. Trust evidence available

How should Fact Checker be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

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... Skill: Fact Checker Owner: nissan Summary: Verify claims, numbers, and facts in markdown drafts against source data. Use when: reviewing blog posts, reports, or documentation for accuracy before publi... Tags: latest:1.0.4 Version history: v1.0.4 | 2026-03-28T04:42:10.049Z | user Add security_notes: local-only verification, subprocess is bundled Python script v1.0.3 | 2026-03-28T03:54:28.755Z | user Frontmatter fix:

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

3.4k

Updated

Oct 9, 2026

Version

1.0.4

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 3.4K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 3.4K downloadsadoption · observed Oct 9, 2026
Latest release: 1.0.4release · observed Mar 28, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17f2fw07zktjmcgagf5c29tbd83rt7v:fact-checker

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-nissan-fact-checker/snapshot"

Documentation

CLAWHUB

19,951 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
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: "🔍"
    network:
      outbound: false
      reason: "All verification against local files and APIs only"
    subprocess:
      note: "Runs python3 scripts for verification — 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 — 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

```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

_meta.json

{
  "ownerId": "kn72rbtybr3jzhcj2260qz6p2181x1mr",
  "slug": "fact-checker",
  "version": "1.0.4",
  "publishedAt": 1774672930049
}

skill-card.md

## Description:

Verify claims, numbers, and facts in markdown drafts against local source data before publication.

This skill is ready for commercial/non-commercial use.

## Publisher:

[nissan](https://clawhub.ai/user/nissan)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, editors, and documentation authors use this skill to check markdown drafts for verifiable numeric claims, dates, model references, scores, and percentages before publication. It produces a report that highlights confirmed, unverifiable, and contradicted claims with suggested corrections when contradictions are found.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill can read broad workspace evidence, including memory logs that may contain private or unrelated information.

Mitigation: Review the skill before installation and run it only in workspaces where those local evidence sources are appropriate for the draft being checked.

Risk: The skill automatically queries a localhost status API when available.

Mitigation: Use it when the local service is expected, and treat unavailable API data as a normal condition that shifts verification to local files.

Risk: Fact-check results can be incomplete when source files, score data, logs, or the local API are missing or stale.

Mitigation: Review unverifiable and contradicted claims manually before publishing, and confirm corrections against the underlying source data.

## Reference(s):

- [ClawHub Skill Page](https://clawhub.ai/nissan/skills/fact-checker)
- [Publisher Profile](https://clawhub.ai/user/nissan)
- [Control Plane status API](http://localhost:8765/status)

## Skill Output:

**Output Type(s):** [Text, Markdown, Shell commands, Guidance]

**Output Format:** [Markdown verification report with per-claim statuses and a summary of confirmed, unverifiable, and contradicted claims.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Can print the report to stdout or write it to a markdown file when an output path is provided.]

## Skill Version(s):

1.0.4 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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