Clarity Gate v2.1

Purpose: Pre-ingestion verification system that enforces epistemic quality before documents enter RAG knowledge bases. Produces Clarity-Gated Documents (CGD) compliant with the Clarity Gate Format Specification v2.1.

Core Question: "If another LLM reads this document, will it mistake assumptions for facts?"

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

What's New in v2.1

Specifications

This skill implements and references:

Note: v2.0 unifies CGD and SOT into a single .cgd.md format. SOT is now a CGD with an optional tier: block.

Validation Codes

Clarity Gate defines validation codes for structural and semantic checks per FORMAT_SPEC v2.1:

HITL Claim Validation (§1.3.2-1.3.3)

Body Structure (§1.2.1)

SOT Table Validation (§3.1)

Note: Additional validation codes may be defined in RFC-001 (clarification document) but are not part of the normative FORMAT_SPEC.

Bundled Scripts

This skill includes Python scripts for deterministic computations per FORMAT_SPEC.

scripts/claim_id.py

Computes stable, hash-based claim IDs for HITL tracking (per §1.3.4).

# Generate claim ID
python scripts/claim_id.py "Base price is $99/mo" "api-pricing/1"
# Output: claim-75fb137a

# Run test vectors
python scripts/claim_id.py --test

Algorithm: 1. Normalize text (strip + collapse whitespace) 2. Concatenate with location using pipe delimiter 3. SHA-256 hash, take first 8 hex chars 4. Prefix with "claim-"

Test vectors: - claim_id("Base price is $99/mo", "api-pricing/1") → claim-75fb137a - claim_id("The API supports GraphQL", "features/1") → claim-eb357742

scripts/document_hash.py

Computes document SHA-256 hash per FORMAT_SPEC §2.2-2.4 with full canonicalization.

# Compute hash
python scripts/document_hash.py my-doc.cgd.md
# Output: 7d865e959b2466918c9863afca942d0fb89d7c9ac0c99bafc3749504ded97730

# Verify existing hash
python scripts/document_hash.py --verify my-doc.cgd.md
# Output: PASS: Hash verified: 7d865e...

# Run normalization tests
python scripts/document_hash.py --test

Algorithm (per §2.2-2.4): 1. Extract content between opening ---\n and <!-- CLARITY_GATE_END --> 2. Remove document-sha256 line from YAML frontmatter ONLY (with multiline continuation support) 3. Canonicalize: - Strip trailing whitespace per line - Collapse 3+ consecutive newlines to 2 - Normalize final newline (exactly 1 LF) - UTF-8 NFC normalization 4. Compute SHA-256

Cross-platform normalization: - BOM removed if present - CRLF to LF (Windows) - CR to LF (old Mac) - Boundary detection (prevents hash computation on content outside CGD structure) - Whitespace variations produce identical hashes (deterministic across platforms)

The Key Distinction

Existing tools like UnScientify and HedgeHunter (CoNLL-2010) detect uncertainty markers already present in text ("Is uncertainty expressed?").

Clarity Gate enforces their presence where epistemically required ("Should uncertainty be expressed but isn't?").

| Tool Type | Question | Example | |-----------|--