fact-checking

fact-checking is a skill for Claude Code, Codex from seb1n/awesome-ai-agent-skills. It costs 48 tokens per session (2,131 once invoked), scanned A, original, MIT.

A workflow for checking factual claims against reliable sources. It separates text into individual claims, compares each with evidence, and gives a confidence-based result.

In plain words
What is it for?
Use it to check articles, reports, scientific statements, statistics, historical claims, definitions, and attributions.
Why use it?
It helps identify unsupported, incorrect, or opinion-based statements instead of treating an entire document as simply true or false.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to check articles, reports, scientific statements, statistics, historical claims, definitions, and attributions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/seb1n/awesome-ai-agent-skills/fact-checking
Install

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.

Any agent
npx skills add seb1n/awesome-ai-agent-skills --skill fact-checking
Clone the repo
git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for fact-checking

README.md
[![agentmods](https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/fact-checking/github.svg)](https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/fact-checking)
Your own site
<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/fact-checking"><img src="https://agentmods.dev/badge/skills/seb1n/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.

agentmods 80×15 button for fact-checking

Your own site · 80×15
<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/fact-checking"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/fact-checking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,131 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
SkillSpector: 1 finding, up to low

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • low Excessive Agency · line 12
    Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.
    Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00048 $0.02131
Opus 5 $0.00024 $0.01066
Sonnet 5 $0.00010 $0.00426
Haiku 4.5 $0.00005 $0.00213

Measured 7d ago against content hash 5b70b61fa746, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 7d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

research-and-knowledge/fact-checking/SKILL.md · 109 lines

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

  1. 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.

  2. 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.

  3. 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).

  4. 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.

  5. 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.

Read the full file on GitHub · 109 lines

Changes

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.

  1. 7d ago First seen · 109 lines · 48 tokens per session scan A 5b70b61fa746

Subscribe to this mod's changes

fact-checking is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 2,131 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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