Clarity Gate — AI skill for Claude Code

Clarity Gate — Prevent LLMs from Misinterpreting Facts

**⚠️ LATEST:** Version 2.1 released (2026-01-27). RFC-001 applied: claim status semantics, bundled scripts. See [CHANGELOG](CHANGELOG.md).

✅ **This README passed Clarity Gate verification** (2026-01-13, adversarial mode, Claude Opus 4.5)

**Open-source pre-ingestion verification for epistemic quality in RAG systems.**

[](https://creativecommons.org/licenses/by/4.0/)

*"Detection finds what is; enforcement ensures what should be. In practice: find the missing uncertainty markers before they become confident hallucinations."*

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The Problem

If you feed a well-aligned model a document that states "Revenue will reach $50M by Q4" as fact (when it's actually a projection), the model will confidently report this as fact.

The model isn't hallucinating. It's faithfully representing what it was told.

**The failure happened before the model saw the input.**

Document Says	Accuracy Check	Epistemic Check
"Revenue will be $50M" (unmarked projection)	✅ PASS	❌ FAIL — projection stated as fact
"Our approach outperforms X" (no evidence)	✅ PASS	❌ FAIL — ungrounded assertion
"Users prefer feature Y" (no methodology)	✅ PASS	❌ FAIL — missing epistemic basis

**Accuracy verification asks:** "Does this match the source?" **Epistemic verification asks:** "Is this claim properly qualified?"

Both matter. Accuracy verification has mature open-source tools. Epistemic verification has detection systems (UnScientify, HedgeHunter, BioScope), but at the date of 2.0 release (January 13th, 2026), I found no open-source pre-ingestion epistemic enforcement system (methodology: deep research conducted via multiple LLMs). Corrections welcome.

Clarity Gate is a proposal for that layer.

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What Is Clarity Gate?

Clarity Gate is an **open-source pre-ingestion verification system** for epistemic quality.

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