Documentation Claim Validator

Verify that what documentation says is actually true by extracting testable claims and checking them against the codebase. Complements doc-maintenance (which handles structural health) by handling semantic accuracy.

When to Use

After significant code changes (refactors, renames, API changes)
Before releases — catch docs that describe removed or changed behavior
When onboarding devs report "the docs are wrong"
As a periodic trust audit on project documentation
After running doc-maintenance to go deeper than structural checks

Quick Reference

Resource	Purpose	Load when
`scripts/extract_claims.py`	Deterministic claim extraction from markdown	Always (Phase 1)
`scripts/verify_claims.py`	Automated verification against codebase	Always (Phase 2)
`references/claim-taxonomy.md`	Full taxonomy of claim types with examples	Triaging unclear claims

Workflow Overview

Phase 1: Extract    → Pull verifiable claims from docs (deterministic script)
Phase 2: Verify     → Check claims against codebase (automated + AI)
Phase 3: Report     → Classify failures by severity and type
Phase 4: Remediate  → Fix or flag broken claims

Phase 1: Extract Claims

Run the extraction script to parse all markdown files and pull out verifiable assertions:

python3 skills/doc-claim-validator/scripts/extract_claims.py [--json] [--root PATH] [--scope docs|manual|all]

The script extracts these claim types from markdown:

Type	What it captures	Example in docs
`file_path`	Inline code matching file path patterns	`src/auth/login.ts`
`command`	Code blocks or inline code with shell commands	`npm run build`
`code_ref`	Function, class, method references in inline code	`authenticate()`
`import`	Import/require statements in code blocks	`import { Router } from 'express'`
`config`	Configuration keys, env vars, settings	`MAX_RETRIES=3`
`url`	External links (http/https)	`[docs](https://example.com)`
`dependency`	Package/library name claims	"Uses Redis for caching"
`behavioral`	Assertions about what code does	"The system retries 3 times"

The first 6 types are extracted deterministically. The last 2 (dependency, behavioral) require AI analysis and are handled in Phase 2.

Output: A structured list of claims with source file, line number, claim type, and the literal text of the claim.

Phase 2: Verify Claims

Step 2a — Automated verification

Run the verification script on the extracted claims:

python3 skills/doc-claim-validator/scripts/verify_claims.py [--json] [--root PATH] [--claims-file PATH] [--check-staleness]

Pass --check-staleness to enable git-based drift analysis (see below).

The script checks each claim type differently:

Claim type	Verification method	Pass condition
`file_path`	`os.path.exists()`	File exists at referenced path
`command`	`shutil.which()` + script check	Binary exists or script file exists
`code_ref`	`grep -r` for function/class name	Symbol found in codebase
`import`	Check module exists in project or deps	Module resolvable
`config`	Grep for config key in source	Key found in config files or code
`url`	HTTP HEAD request (optional, off by default)	Returns 2xx/3xx

Pass --check-urls to enable URL verification (slow, requires network).

Step 2b — AI-assisted verification

After the automated pass, dispatch haiku agents to verify claims the script cannot:

Agent 1 — Dependency claim verifier (subagent_type: "Explore", model: "haiku"): Read package.json, requirements.txt, go.mod, Cargo.toml, or equivalent dependency manifests. Cross-reference any doc claims about libraries, frameworks, or services used. Report claims that reference dependencies not in the project.

Agent 2 — Behavioral claim verifier (subagent_type: "Explore", model: "haiku"): For each behavioral claim (e.g., "retries 3 times", "caches for 5 minutes", "validates input before processing"), find the relevant code and verify the claim is accurate. Report mismatches between documented behavior and actual implementation.

Agent 3 — Code example verifier (subagent_type: "Explore", model: "haiku"): For code blocks in docs that show usage examples, verify the function signatures, parameter names, return types, and import paths match the current codebase. Report examples that would fail if copy-pasted.

Launch all three agents in parallel.

Step 2c — Git staleness scoring

For claims that pass existence checks, compute a drift score to surface likely-stale claims:

python3 skills/doc-claim-validator/scripts/verify_claims.py --check-staleness

For each passing claim, the script:

Gets the doc file's last git modification timestamp
Gets the target file(s) last git modification timestamp
Counts how many commits touched the target after the doc was last edited
Assigns a drift score: low (1-3 commits), medium (4-9), high (10+)

High-drift claims are the best candidates for AI review — the target changed heavily but the doc didn't, so the doc is probably describing outdated behavior.

The staleness report is appended as a ranked table, sorted by score descending.

Phase 3: Report

Merge automated and AI findings into a single report. Classify each failed claim:

Severity

Level	Meaning	Example
P0	User-facing doc claims something that would break if followed	Tutorial shows deleted API endpoint
P1	Dev doc references nonexistent code construct	README references `auth.validate()` which was renamed
P2	Behavioral claim no longer accurate	"Retries 3 times" but retry logic was removed
P3	Dependency/import claim outdated	"Uses Express" but migrated to Fastify
P4	Minor inaccuracy, cosmetic	Config key renamed but behavior unchanged

Failure Categories

Category	Description
`missing_target`	Referenced file, function, or symbol doesn't exist
`wrong_signature`	Function exists but signature differs from doc
`stale_behavior`	Behavioral claim doesn't match implementation
`dead_dependency`	Doc references a dependency not in the project
`broken_example`	Code example would fail if executed
`dead_url`	External link returns 4xx/5xx
`phantom_config`	Config option referenced in docs doesn't exist in code

Phase 4: Remediate

For each failed claim, decide the action:

Action	When	How
Update doc	Code is correct, doc is stale	Edit doc to match code
Flag for review	Unclear if code or doc is wrong	Create issue for human review
Remove claim	Referenced feature was deleted	Remove or rewrite section
Update example	Code example is outdated	Rewrite example against current code

Route remediation to the appropriate agent per doc-maintenance conventions:

reference-builder for API/CLI reference docs
technical-writer for architecture and developer docs
learning-guide for user-facing tutorials and guides

Integration with doc-maintenance

This skill is designed to run after doc-maintenance:

doc-maintenance  →  Structural health (links, orphans, folders, staleness)
doc-claim-validator  →  Semantic accuracy (do claims match reality?)

The two skills share the same severity scale and remediation agent routing. Results from both can be combined into a single documentation health report.

Anti-Patterns

Do not auto-fix behavioral claims — they require human judgment about intent
Do not treat every inline code reference as a file path (`true` is not a file)
Do not validate claims in archived docs (docs/archive/) — they're historical
Do not fail on optional/conditional features — mark as "conditional" instead
Do not check URLs by default — it's slow and flaky; opt-in only
Do not validate code blocks marked with  comment

Bundled Resources

Scripts

scripts/extract_claims.py — Deterministic claim extraction from markdown files
scripts/verify_claims.py — Automated verification of extracted claims against codebase

References

references/claim-taxonomy.md — Full taxonomy of claim types with extraction patterns and examples

doc-claim-validator

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