Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
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 openclawclawhub Free
Verify claims, numbers, and facts in markdown drafts against source data. Use when: reviewing blog posts, reports, or documentation for accuracy before publi...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/fact_check.py, scripts/test_fact_check.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- nissan
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
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.
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Scripts1 Docs
- SKILL.md
- scripts/fact_check.py
- scripts/test_fact_check.py