doc-claim-validator

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

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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

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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)
architectural	Verb-anchored prose claims about technology, integrations, or architectural patterns	"Uses Redis for caching", "follows the actor model", "delegated to Auth0"
dependency	Package/library name claims	"Uses Redis for caching"
behavioral	Assertions about what code does	"The system retries 3 times"

The first 7 types are extracted deterministically. The script uses verb-anchored regex for architectural (rules like uses X, built with X, follows the X pattern, delegated to X, via X, depends on X) — this catches anchorless prose claims that previously slipped through.

The last 2 (dependency, behavioral) require AI analysis and are handled in Phase 2. behavioral in particular is not regex-extracted because behavioral claims are free-form prose ("the cache invalidates when the user logs out") that doesn't pattern-match cleanly — the behavioral verifier discovers and verifies them in one pass.

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

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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 agents to verify claims the script cannot. Three of four verifiers run on general-purpose + sonnet — behavioral, architectural, and code-example verification all require multi-file reasoning that haiku's excerpt-read pattern strains under. The dependency verifier stays on Explore + haiku because it's pure pattern matching against manifest files.

Dispatch strategy: per-docfile batching

For behavioral and architectural verifiers, dispatch one sonnet call per markdown file containing claims of that type, with all claims from that file batched into a single prompt. This keeps each call's context budget on a small number of related claims (cross-referencing within the doc improves verification) while keeping total call count tied to doc-set size rather than claim count. For a project with ~50 docs and ~150 architectural claims, expect ~10–20 sonnet calls (only docs with claims trigger calls), not 150.

For release audits where precision matters more than cost, run with per-claim dispatch — one sonnet call per claim, each with the full doc as context. Higher cost, higher precision.

Verifiers

Verifier 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. Stays on haiku because pattern-matching against manifests doesn't benefit from sonnet's reasoning.

Verifier 2 — Behavioral claim verifier (subagent_type: "general-purpose", model: "sonnet", per-docfile): For each markdown file in scope, dispatch a sonnet agent with the file content. The agent (a) discovers behavioral claims in the file ("retries 3 times", "caches for 5 minutes", "validates input before processing", "the cache invalidates when the user logs out"), (b) finds the relevant code via grep / codanna / Read, (c) verifies whether the claim matches the implementation. Report each claim with confirmed / contradicted / unverifiable / conditional status. Sonnet is needed because behavioral verification often requires tracing across multiple files (handler → middleware → config) and distinguishing happy-path from error-path behavior.

Verifier 3 — Architectural claim verifier (subagent_type: "general-purpose", model: "sonnet", per-docfile): For each markdown file with extracted architectural claims, dispatch a sonnet agent with the file content and the list of pre-extracted claims. The agent verifies each claim by:

• For uses/built/depends/via frames: check the named technology in dependency manifests, config files, and source imports.
• For delegated frames: check for SDK imports or HTTP integrations matching the named service.
• For follows/uses_pattern frames: check directory structure, class names, and code organization for the named architectural pattern (e.g., CQRS: separate command/query handlers + event store; hexagonal: adapters/ports dirs; saga: orchestrator class with named transitions).

Report each claim with confirmed / contradicted / unverifiable / conditional status. Sonnet is needed because architectural patterns aren't 1:1 with any single file — verification requires reading enough of the codebase to recognize the pattern.

Verifier 4 — Code example verifier (subagent_type: "general-purpose", model: "sonnet", per-docfile): 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. Sonnet is needed because signature checking requires reading the current implementation and comparing — haiku's excerpt reads aren't sufficient.

Launch verifiers 1, 2, 3, 4 in parallel. Within verifiers 2/3/4, the per-docfile dispatches run sequentially (or in small parallel batches if cost permits).

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:

1. Gets the doc file's last git modification timestamp
2. Gets the target file(s) last git modification timestamp
3. Counts how many commits touched the target after the doc was last edited
4. 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.

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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
phantom_pattern	Architectural claim ("uses CQRS", "follows actor model") not evidenced in the codebase
wrong_integration	Doc names a service/SDK ("delegated to Auth0") that isn't actually integrated
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

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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

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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.

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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 <!-- no-verify --> comment

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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