Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add h4vzz/awesome-ai-agent-skills --skill fact-checkinggit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/fact-checking)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/fact-checking"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/fact-checking/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/fact-checking"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/fact-checking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.02116 |
| Opus 5.5 | $0.00013 | $0.00846 |
| Sonnet 5.5 | $0.00007 | $0.00423 |
| Haiku 4.5 | $0.00003 | $0.00212 |
Grade A, and why
fact-checking scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to fact-checking — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fact-Checking
This skill enables an AI agent to systematically verify claims and statements. Rather than offering a simple true/false judgment, the agent extracts discrete checkable claims from the input, identifies authoritative sources for each, cross-references evidence, and produces a structured verdict with a confidence score and supporting reasoning. The approach is designed to handle everything from single factual assertions to full articles containing dozens of claims.
Workflow
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Extract Claims: Parse the input text and isolate individual, verifiable assertions. Each claim should be a single, self-contained statement that can be independently checked. Discard opinions, subjective judgments, and unfalsifiable statements, but note them as "not checkable" in the output.
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Classify Claim Types: Categorize each claim by type — statistical (involves numbers or data), historical (references past events), scientific (references research findings), definitional (defines a term), or attribution (attributes a statement to a person or organization). The category guides where to look for verification.
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Identify Authoritative Sources: For each claim, determine the most appropriate verification sources. Use primary sources whenever possible: official datasets for statistics, peer-reviewed papers for scientific claims, archived transcripts for quotations, and government records for legal or policy assertions. Supplement with reputable secondary sources like established fact-checking organizations (Snopes, PolitiFact, Full Fact).
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Cross-Reference and Evaluate Evidence: Check each claim against at least two independent sources. Note whether sources corroborate, partially support, or contradict the claim. Assess source credibility by considering authority, recency, methodology, and potential bias.
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Assign Verdicts and Confidence Scores: For each claim, assign a verdict from the scale: True, Mostly True, Half True, Mostly False, False, or Unverifiable. Accompany each verdict with a confidence score (0.0-1.0) reflecting the strength and consistency of available evidence, and a brief justification.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 109 lines · 33 tokens per session scan A bb07ea4e7a6b
fact-checking is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 33 tokens to every session and 2,116 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to fact-checking, differing in 2 lines, and is treated as a copy.
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