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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...
对照源数据验证 Markdown 草稿中的声明、数字和事实。适用场景:发布前审核博客文章、报告或文档的准确性。
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内容创作 clawhub v1.0.4 3 版本 99860.3 Key: 无需
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概述

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 referencesmodel/task (phi4/classify) and model:tag (phi4:latest)
  • DatesYYYY-MM-DD format
  • Score values — decimal scores like 0.923, 1.000
  • Percentages42%, 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.

版本历史

共 3 个版本

  • v1.0.4 当前
    2026-05-03 02:56 安全 安全
  • v1.0.2
    2026-03-29 13:24 安全 安全
  • v1.0.1
    2026-03-07 01:58

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

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