Fact Checker
/install 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
python3 skills/fact-checker/scripts/fact_check.py \x3Cdraft.md>
python3 skills/fact-checker/scripts/fact_check.py \x3Cdraft.md> --output report.md
What It Checks
Claim types extracted
- Numeric claims — integers and floats with surrounding context
- Model references —
model/task(phi4/classify) andmodel:tag(phi4:latest) - Dates —
YYYY-MM-DDformat - 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
/statusAPI athttp://localhost:8765/status— live scored run data projects/hybrid-control-plane/data/scores/*.json— raw scored run files on diskmemory/*.md— daily logs with timestamps and decisionsgit loginprojects/hybrid-control-plane/— commit hashes, dates, authorshipprojects/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
/statusAPI is unavailable, note it and rely on FINDINGS.md + score files.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install fact-checker - 安装完成后,直接呼叫该 Skill 的名称或使用
/fact-checker触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Fact Checker 是什么?
Verify claims, numbers, and facts in markdown drafts against source data. Use when: reviewing blog posts, reports, or documentation for accuracy before publi... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 920 次。
如何安装 Fact Checker?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install fact-checker」即可一键安装,无需额外配置。
Fact Checker 是免费的吗?
是的,Fact Checker 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Fact Checker 支持哪些平台?
Fact Checker 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Fact Checker?
由 Nissan Dookeran(@nissan)开发并维护,当前版本 v1.0.4。