DocGuard MCP Server

🛡️ DocGuard

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The enforcement layer for Spec-Driven Development. Validate. Score. Enforce. Ship documentation that AI agents can actually use.

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✨ See what DocGuard catches in 30 seconds — no install, no setup:

npx docguard-cli demo

Runs against a baked-in sample project with intentional drift and shows you the findings + a clear path to fixing them.

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Table of Contents

• What is DocGuard?
• Why DocGuard?
• Quick Start
• Spec Kit Integration
• Usage
• Validators
• Reading a finding
• Templates
• AI Agent Support
• Slash Commands
• Examples
• Testing
• Enterprise Adoption
• CI/CD Integration
• What's New
• File Structure
• Configuration
• Research Credits

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What is DocGuard?

DocGuard enforces Canonical-Driven Development (CDD) — a methodology where documentation is the source of truth, not an afterthought. AI writes the docs, DocGuard validates them.

Traditional Development	Canonical-Driven Development
Code first, docs maybe	Docs first, code conforms
Docs rot silently	Drift is tracked and enforced
Docs are optional	Docs are required and validated
One AI agent, one context	Any agent, shared context via canonical docs

DocGuard is an official GitHub Spec Kit community extension. It validates the artifacts that Spec Kit creates, ensuring your specs stay high-quality throughout the development lifecycle.

🧭 How it works (8-page brief) · 📖 Philosophy · 📋 CDD Standard · ⚖️ Comparisons · 🔬 Validation · 🗺️ Roadmap

Architecture

graph TD
    CLI["CLI Entry<br/>docguard.mjs"] --> Commands["Commands (25)"]
    Commands --> guard["guard"]
    Commands --> generate["generate"]
    Commands --> score["score"]
    Commands --> diagnose["diagnose"]
    Commands --> setup["setup wizard"]
    Commands --> other["diff · init · fix · trace · impact · sync · reconcile · retire · specs<br/>explain · memory · upgrade · agents · hooks · badge · ci · watch"]

    guard --> Validators["Validators (32)"]
    generate --> Scanners["Scanners (4)<br/>routes · schemas · doc-tools · speckit"]
    score --> Scoring["Weighted Scoring<br/>8 categories"]
    diagnose --> Validators
    diagnose --> AIPrompts["AI-Ready<br/>Fix Prompts"]

    Validators --> Output["Output"]
    Scanners --> Output
    Scoring --> Output
    Output --> Terminal["Terminal"]
    Output --> JSON["JSON"]
    Output --> Badge["Badge"]

    style CLI fill:#2d5016,color:#fff
    style Validators fill:#1a3a5c,color:#fff
    style Scanners fill:#1a3a5c,color:#fff
    style Output fill:#5c3a1a,color:#fff

Distribution: Node.js core (npm) · Python wrapper (PyPI) · GitHub Action (action.yml) · Spec Kit Extension (ZIP)

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Why DocGuard?

DocGuard checks declared documentation facts against repository evidence and gives agents structured repair tasks. Deterministic checks cover supported facts, references, and generated sections. Human-authored requirements and architectural decisions retain their authority when implementation diverges.

A guard result describes the checks performed. The CDD grade measures structural maturity. Exact declarations in .docguard-evidence.json can verify selected statements against current local evidence; every other statement remains unverified. Coverage and unresolved claims remain visible, so teams can choose an appropriate enforcement policy.

Research motivates evaluation of this approach. A 2026 study found that repository context files did not generally improve task success and increased inference cost in its evaluated settings. It also found agents generally followed the instructions. These results support testing concise, relevant context and measuring actual task outcomes; they do not establish DocGuard's effectiveness. Evaluating AGENTS.md, revised June 2026.

The current roadmap prioritizes accurate detection, reproducible evidence, document lifecycle management, and contributor-supplied regression cases. Released plans and superseded specifications are removed from active AI context and remain recoverable from Git.

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⚡ Quick Start

Package naming: this repo is raccioly/docguard; the published package is docguard-cli on both npm and PyPI; the installed command is docguard. Same project — the -cli suffix is just the registry name. The package runs no install scripts, so npm i -g docguard-cli --ignore-scripts is equivalent.

Node.js (npm)

# No install needed — run directly
npx docguard-cli diagnose

# Or install globally
npm i -g docguard-cli
docguard diagnose

Python (PyPI)

pip install docguard-cli
docguard diagnose

Note: The Python package is a thin wrapper that delegates to npx. Node.js 18+ is required on the system.

Docker (MCP server)

The MCP server ships as a container image on GHCR — no Node.js install required. Public image, so no authentication is needed to pull it:

# Run the MCP server against the current directory
docker run -i --rm -v "$PWD":/workspace ghcr.io/raccioly/docguard:latest

The entrypoint is the stdio MCP transport: stdout is the JSON-RPC channel, so don't pipe anything else into it. Mount the project you want inspected at /workspace and pass {"projectDir": "/workspace"} in tool calls (or rely on the default working directory).

Pin a version rather than tracking latest in CI:

docker run -i --rm -v "$PWD":/workspace ghcr.io/raccioly/docguard:0.34.9

The server is read-only — it never writes to the mounted project.

More ways to integrate

• pre-commit — changed-only guard on every commit:

  repos:
    - repo: https://github.com/raccioly/docguard
      rev: v0.29.0
      hooks: [{ id: docguard-guard }]   # docguard-guard-full for pre-push
  

• MCP (Claude, Cursor, any MCP client) — claude mcp add docguard -- npx -y docguard-cli mcp; read-only tools to check docs (guard, score, diagnose, …) and to navigate them one bounded section at a time — the full list is in docs/ai-integration.md. Registry manifest ships in-repo (server.json, Smithery-ready).
• GitLab CI — component staged at templates/ci/gitlab-component.yml (guard/score/ci job with a SARIF artifact).
• Homebrew — brew install raccioly/tap/docguard. The release workflow renders the formula template in packaging/homebrew/ from the published npm tarball and pushes it to the tap.

Core Workflow

# 1. Initialize docs for your project
npx docguard-cli init

# 2. Or reverse-engineer docs from existing code
npx docguard-cli generate

# 3. AI diagnoses issues and generates fix prompts
npx docguard-cli diagnose

# 4. Validate — use as CI gate
npx docguard-cli guard

# 5. Check maturity score
npx docguard-cli score

The AI Loop

diagnose  →  AI reads prompts  →  AI fixes docs  →  guard verifies
   ↑                                                       ↓
   └───────────────── issues found? ←──────────────────────┘

diagnose is the primary command. It runs all validators, maps every failure to an AI-actionable fix prompt, and outputs a remediation plan. Your AI agent runs it, fixes the docs, and runs guard to verify.

Mechanical vs. agent fixes

DocGuard splits drift into two kinds and is explicit about which is which:

Kind	Example	How it's fixed
Mechanical (deterministic)	An endpoint documented in API-REFERENCE.md that the OpenAPI spec confirms is gone	docguard fix --write deletes the row + detail block itself — no AI
Agent (needs judgment)	Rewriting an X-Ray prose section as CloudWatch; writing a new endpoint's request/response	Routed to an AI agent via diagnose / fix --doc prompts

docguard fix --write only touches docs marked <!-- docguard:generated true --> (override with --force), is idempotent, and prints exactly what changed. It never rewrites prose — that stays with the agent.

Continuous documentation workflow

guard ──▶ fix --write (mechanical, auto) ──▶ guard ──▶ diagnose (agent prompts for the rest)

• CI / pre-commit: docguard hooks --type pre-commit --auto-fix installs a hook that applies mechanical fixes, re-stages the docs, then runs guard; anything left is surfaced as agent prompts.
• Agent-driven: docguard diagnose --auto scaffolds missing docs and applies mechanical fixes, then emits prompts for the content rewrites that remain.
• JSON for automation: guard/diagnose --format json include a mechanicalFixes array and tag each issue mechanical vs agent, so an agent can apply or delegate precisely.

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🌱 Spec Kit Integration

DocGuard is a community extension for GitHub's Spec Kit framework. While Spec Kit focuses on creating specifications (via AI slash commands like /speckit.specify and /speckit.plan), DocGuard focuses on validating their quality.

How They Work Together

┌─────────────────┐          ┌──────────────────┐
│    Spec Kit      │          │    DocGuard       │
│                  │          │                   │
│  /speckit.specify│ ──────→  │  docguard guard   │
│  Creates specs   │          │  Validates specs  │
│  (AI-driven)     │          │  (automated)      │
└─────────────────┘          └──────────────────┘

Phase	Tool	What happens
1. Initialize	specify init	Creates .specify/ directory and templates
2. Write specs	/speckit.specify	AI creates spec.md with FR-IDs, user stories
3. Validate	docguard guard	Checks spec quality (mandatory sections, FR/SC IDs)
4. Plan	/speckit.plan	AI creates plan.md with technical context
5. Validate	docguard guard	Checks plan quality (sections, structure)
6. Tasks	/speckit.tasks	AI creates tasks.md with phased breakdown
7. Validate	docguard guard	Checks task quality (phases, T-IDs)
8. Implement	/speckit.implement	AI writes code
9. Enforce	docguard guard	Final quality gate — CI/CD

What DocGuard Validates in Spec Kit Projects

• spec.md — Mandatory sections (User Scenarios, Requirements, Success Criteria), FR-xxx IDs, SC-xxx IDs
• plan.md — Summary, Technical Context, Project Structure sections
• tasks.md — Phased task breakdown (Phase 1, 2, 3+), T-xxx task IDs
• constitution.md — Detected at .specify/memory/constitution.md or project root
• Requirement traceability — FR, SC, NFR, US, AC, UC, SYS, ARCH, MOD, T IDs

Installing as a Spec Kit Extension

docguard init does this for you: when the specify CLI (Spec Kit ≥ 0.10.0) is on your PATH, it initializes Spec Kit for the coding agent the repository already uses, then registers the DocGuard extension shipped inside the installed package, so the registered version always matches your CLI and nothing is downloaded. Every failure is printed with Spec Kit's own error and the command to run by hand. Only init touches Spec Kit; other commands print a one-line hint at most.

To register it yourself, from the catalog or a local checkout:

specify extension add docguard
specify extension add ./node_modules/docguard-cli/extensions/spec-kit-docguard --dev

This registers the speckit.docguard.* commands listed in extension.yml (invoked as /speckit-docguard-guard and so on in skills-based agents such as Claude Code) and the workflow hooks that run them.

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Usage

DocGuard ships 25 commands (the "Daily 5" + 20 situational tools, including lifecycle reconciliation, doc dependency review, agent-rule resolution, retirement and spec tracking, the zero-install demo, the mcp server, and the ci pipeline gate). Six additional one-shot scaffolders are accessed via docguard init --with <name>. Legacy command forms remain compatible until v1.0 and print their replacements.

The Daily 5 — what you'll reach for 95% of the time:

Command	What It Does
init	Bootstrap a project (--wizard for interactive · --with <name> for scaffolders)
guard	Validate against canonical docs — 32 validators
diff	Show gaps between docs and code (--since <ref> for impact mode)
sync	Refresh code-truth doc sections, including the module-graph and entity-diagram mermaid diagrams drawn from code — keeps memory always up to date
score	Structural CDD maturity score (0-100; not a guard verdict; --diff for delta between refs)

Tools (situational, but day-to-day useful):

Command	Purpose
demo	Zero-install showcase — runs guard against a baked-in drifting fixture (npx docguard-cli demo)
diagnose	AI orchestrator — guard → emit fix prompts in one command
fix	Generate AI fix instructions for specific docs (--doc <name> --format prompt)
fix --write	Apply deterministic fixes (no AI — version bumps, counts, anchors, sections)
fix --history	Audit log of every mechanical fix applied (from .docguard/fixed.json)
generate	Reverse-engineer docs from existing codebase (--plan for AI scan) — includes auto-generated Mermaid ER diagrams from your detected schemas (Prisma/Drizzle/TypeORM/Sequelize/Django/Rails) in DATA-MODEL.md. --spec <area> writes an as-built Spec Kit spec for one code area: one requirement candidate per route, exported symbol, env var or entity found there (the agent writes every statement), registered as origin: as_built; guard then reports new or vanished facts (SPR007)
agent	One-shot agent task graph, or a bounded current-evidence packet for one task (--task <text>, --format json)
explain <warning|CODE>	Paste any warning — or a finding code like SEC001 — to get the validator's docstring, fix path, and how to suppress
verify --evidence	Evaluate strict statement-to-source declarations for typed JSON values, bounded collection counts, saved oasdiff JSON, and saved Buf JSON Lines. Results distinguish scoped verification, contradiction, stale inputs, inconclusive evidence, and unsupported formats.
verify --semantic	Extract documented numbers/limits/enums (retention days, rate limits, GSI/role counts, status enums) as a task list for an agent to check against code — the semantic-drift class regex/AST can't see
verify --instructions	Audit AGENTS.md/CLAUDE.md themselves for drift: duplicate rules, never-vs-always contradictions, stale file pointers, unknown commands — plus clustered rule pairs as agent judgment tasks
feedback	Review any finding or a synthetic false-positive/false-negative/unsupported fixture; verify its opposite control, reduce it deterministically, search open and closed duplicates, and optionally emit a test-only contribution. Nothing is submitted automatically.
retire	Find completed or superseded planning material (--plan/--check; --fail-on-warning gates advisory candidates) and explicitly remove clean tracked documentation from active AI context. .docguard-archive.json records recovery metadata and retired requirement identities, and --retention-ref proves the source revision remains reachable. This is separate from the Spec Kit Archive extension, which consolidates feature documents.
reconcile	Build a read-only code↔spec review graph since a Git ref. Classifies mechanical facts, approved intent, decisions, unrelated changes, and unsupported evidence; --write applies only mechanical generated-section refreshes.
review	Doc sections whose covered code changed since their last review (--accept <doc>#<id> --reason, --prune, --suggest)
rules	Which agent instruction files each harness (Codex, Claude Code, Cursor, Copilot, OpenHands) loads for a path, why, and how many bytes (--for <path>, --harness)
specs	Maintain the versioned spec registry, preflight new specs, and apply evidence-gated completion transactions with bounded outcomes and active-context regeneration. Verified living specs can record later reviewed maintenance without reopening or duplicating the specification. specs require is the spec-first gate: a change to governed paths must name its spec or declare Spec-Exempt: <kind> — <reason>.
specs --check / specs --write	Validate or refresh .docguard-specs.json, the byte-stable index of immutable spec IDs, reviewed lifecycle/lineage/scope, artifact digests, task state, explicitly scoped test evidence, and archive tombstones. Refreshes preserve the reviewed block.
specs preflight [--path <spec>]	Before specification, print current spec lifecycle and evidence. Before planning, check the generated draft for structural blockers and report semantic overlap as review-only evidence.
mcp	MCP server — exposes checking (guard, score, explain, verify, report, diagnose) and exact doc navigation (docs for a file, document outline, one section, task context) as native tools for Claude, Cursor, and any MCP client; docguard_guard returns each fact once unless called with detail: "full". Stdio: claude mcp add docguard -- npx docguard-cli mcp. Team-shared HTTP: docguard mcp --transport http --port 8585 (loopback by default; non-loopback binds require --api-key)
report	Compliance-evidence bundle for audits — combined readiness, guard verdict, structural maturity, ALCOA+ attributes, and fix history, stamped with git commit and a tamper-evident sha256 integrity hash (--format json, --out <file>). Evidence, not a gate: always exits 0
ci	Pipeline gate: guard + structural maturity in one command with READY/ATTENTION/BLOCKED assessment — never scaffolds or touches source; its only write is its own .docguard/history.jsonl (opt out: --no-history). --threshold <n> fails below a score, --fail-on-warning for strict mode, --format json for parsers
score --trend	Score trajectory from recorded ci runs — sparkline, delta, and the last 10 runs with commit stamps
memory	Per-domain accuracy headline (endpoints / entities / env / tech)
memory --diff	Drill into which specific claims don't match code
memory --pack	Write .docguard/context-pack.md — compact, code-truth-stamped session-start context for AI agents
score --diff	Drill into which checks pulled each category down
trace / trace --reverse <file>	Requirements traceability — forward AND reverse
trace --features	Per-feature spec-adherence scores (requirement coverage, task completion, task evidence, artifacts) — worst-first with fix hints
upgrade [--apply] [--pr]	Check npm for a newer CLI (the one command that contacts a registry) + migrate .docguard.json schema; --pr opens a PR. When the installed release is over 14 days old, guard's text output, the MCP server and the context pack suggest running docguard upgrade; nothing upgrades on its own (DOCGUARD_NO_UPDATE_HINT=1 silences the note)
watch	Live mode: re-run guard on file changes

init --with <name> scaffolders — picked at init time:

Scaffolder	What It Generates
agents	AGENTS.md, CLAUDE.md, .cursor/rules/, .github/copilot-instructions.md
hooks	Git pre-commit / pre-push hooks
ci	GitHub Actions / pipeline YAML
badge	Shields.io score badges for README
llms	llms.txt (AI-friendly summary)
publish	External doc-site config (Mintlify) — experimental

Run them solo (docguard init --with hooks) or stacked (docguard init --with agents,hooks,badge,ci).

To declare an exact fact, copy templates/evidence-manifest.json to .docguard-evidence.json, point its literal Markdown template at one unique statement, and bind that value to a supported local source. Run docguard verify --evidence --format json before enabling the guard in CI. External compatibility declarations consume saved oasdiff or Buf output and require current SHA-256 identities for every declared repository input.

Deprecation aliases — setup · agents · hooks · badge · llms · publish · impact remain compatible until v1.0 with a yellow stderr warning. audit → guard is permanent and silent; ci is a current first-class pipeline command.

CLI Flags

Flag	Description	Commands
--dir <path>	Project directory (default: .); explicit selection suppresses ancestor-root guidance	All
--verbose	Show detailed output	All
--quiet / -q	Suppress banner — for hooks, CI loops, scripts	All
--format json	Machine-readable output (clean JSON, no ANSI bleed)	guard, score, diff, trace, diagnose, memory, impact, explain, verify, reconcile, retire, specs
--format sarif	SARIF 2.1.0 output — findings as rules/results for GitHub Code Scanning and SARIF dashboards	guard
--format junit	JUnit XML output — one testcase per validator, for GitLab CI (artifacts:reports:junit), Jenkins, Azure DevOps, CircleCI	guard
--update-baseline	Adopt DocGuard on a legacy repo without a red day one: freeze today's findings into a committed .docguard.baseline.json; guard/ci then gate only NEW drift. Suppression is always visible ("N pre-existing finding(s) suppressed"), and --no-baseline shows the full picture	guard
--full	Generate llms-full.txt (full doc bodies inlined) instead of the llms.txt link index	llms
--compact	With --format json: each fact once, the form the MCP guard tool returns by default	guard
--pack	Write .docguard/context-pack.md — agent session-start context	memory
--symbols	With --pack: add a symbol map (most central files and their exported names, within memory.symbolMap.maxBytes); opt-in until the v2 benchmark decides	memory
--sync	Regenerate the agent-file family (CLAUDE.md, Copilot, Cursor, …) from AGENTS.md; hash-marked, never touches hand-written files without --force	agents
--check	CI gate for the synced agent-file family — exit 2 when a variant is stale	agents
--force	Overwrite existing files (creates .bak backups)	generate, agents, init
--force-redo	Bypass ping-pong suppression in .docguard/fixed.json	fix --write
--profile <name>	Starter / standard / enterprise	init
--no-spec-kit	Skip auto-init of .specify/ / .agent/ scaffolding	init
--changed-only [--since <ref>]	Pre-commit lite mode: the fast validators (including covered-doc dependencies) on changed files only	guard
--timings	Per-validator wall-time profile (slowest first)	guard
--show-failing	Show warnings/errors even when status is PASS	guard
--pin	Record running CLI version into .docguard.json (reproducibility)	guard
--diff	Per-category drill-down	score, memory
--check-only	Exit 1 if behind (for CI)	upgrade
--apply	Actually run the migration	upgrade
--pr	Open a PR with the migration	upgrade
--reverse <file>	Reverse traceability (code → docs)	trace
--no-indirect	Skip the reverse-import-graph analysis (docs about modules that import a changed file)	impact, diff --since
--prs	Open-PR doc-conflict analysis — two PRs impacting the same canonical doc = merge-order risk (needs the gh CLI)	impact
--transport http --port --host --api-key --path	Serve MCP over Streamable HTTP instead of stdio (team-shared server; loopback-only unless an api-key is set)	mcp
--history	Show fix audit log	fix

When run from a nested package without --dir, DocGuard checks only that selected directory. If a bounded ancestor scan finds a .docguard.json or an npm/pnpm workspace declaration that owns the package, stderr shows an exact repository-scope rerun command. DocGuard never changes scope automatically. JSON, SARIF, and JUnit stdout remain valid; machine runs receive one typed docguard.repository-root-guidance JSON diagnostic on stderr. A local config, an explicit --dir, an unmatched workspace, or a nested Git boundary suppresses the suggestion.

Example Output

$ npx docguard-cli generate

🔮 DocGuard Generate — my-project
   Scanning codebase to generate canonical documentation...

  Detected Stack:
    language: TypeScript ^5.0
    framework: Next.js ^14.0
    database: PostgreSQL
    orm: Drizzle 0.33
    testing: Vitest
    hosting: AWS Amplify

  ✅ ARCHITECTURE.md (4 components, 6 tech)
  ✅ DATA-MODEL.md (12 entities detected)
  ✅ ENVIRONMENT.md (18 env vars detected)
  ✅ TEST-SPEC.md (45 tests, 8/10 services mapped)
  ✅ SECURITY.md (auth: NextAuth.js)
  ✅ REQUIREMENTS.md (spec-kit aligned)
  ✅ AGENTS.md
  ✅ CHANGELOG.md
  ✅ DRIFT-LOG.md

  Generated: 9  Skipped: 0

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🔍 Validators

DocGuard runs 32 automated validators on every guard check. Source-facing validators are language-aware where their evidence model applies; repository and document validators operate independently of source language.

Counting note: guard prints 30 result rows, not 29. Structure emits a second check result (Doc Sections) under the same validator key, so rows are checks, not validators. The published number is the count of shipped cli/validators/*.mjs modules and is enforced by tests — don't derive it by counting output rows.

#	Validator	What It Checks	Default
1	Structure	Required CDD files exist; each AGENTS.md chain fits the agent's load limit (32 KiB default, per-chain allowances for existing debt)	✅ On
2	Doc Sections	Canonical docs have required sections (or N/A markers)	✅ On
3	Docs-Sync	Routes/services referenced in docs + OpenAPI cross-check	✅ On
4	Drift-Comments	// DRIFT: comments logged in DRIFT-LOG.md (skips test files by default)	✅ On
5	Changelog	CHANGELOG.md has [Unreleased] section	✅ On
6	Test-Spec	Tests exist per TEST-SPEC.md rules	✅ On
7	Environment	Env vars documented, .env.example exists	✅ On
8	Security	No hardcoded secrets in source code	✅ On
9	Architecture	Imports follow layer boundaries (honors config.ignore)	✅ On
10	Freshness	Docs not stale relative to code changes (rename-aware via git log --follow)	✅ On
11	Traceability	Requirement IDs (FR, SC, NFR, US, AC, T) trace to tests	✅ On
12	Docs-Diff	Code artifacts match documented entities	✅ On
13	API-Surface	API-REFERENCE.md endpoints match real routes (OpenAPI cross-check)	✅ On
14	Metadata-Sync	Version refs consistent across docs	✅ On
15	Docs-Coverage	Code features referenced in documentation	✅ On
16	Doc-Quality	Writing quality (readability, passive voice, atomicity, IEEE 830)	✅ On
17	TODO-Tracking	Untracked TODOs/FIXMEs and skipped tests (skips test files by default)	✅ On
18	Schema-Sync	Database models documented in DATA-MODEL.md	✅ On
19	Spec-Kit	Spec quality validation (FR-IDs, mandatory sections, phased tasks, unique spec numbers)	✅ On
20	Document-Lifecycle	Exact terminal states, advisory completion signals, incomplete coverage, and manifest/working-tree inconsistencies	✅ On
21	Spec-Registry	Immutable spec identities, byte-stable evidence projection, reviewed lifecycle preservation, and archive/storage consistency	✅ On
22	Evidence	Exact declared Markdown statements match current typed JSON, bounded collections, or saved compatibility reports; unsupported and missing evidence stays visible	✅ On
23	Cross-Reference	Internal markdown links + anchors resolve (with "did you mean?" hints); Obsidian wikilinks validated when the repo uses them as file links (.obsidian present or a target resolves)	✅ On
24	Generated-Staleness	source=code sections match scanner output; status: draft doc age	✅ On
25	Canonical-Sync	DocGuard's own README count claims match code-truth (DocGuard repo only — N/A elsewhere)	✅ On
26	Metrics-Consistency	Hardcoded numbers match actual counts, including runtime-dependency claims against package.json (the Spec Kit constitution is read too)	✅ On
27	Surface-Sync	Item-level enumerable drift — names in doc tables/lists (commands, checks, etc.) match code-truth (opt-in via surfaceSync.surfaces; N/A unless configured)	✅ On
28	Diff-Suspicion	Change-driven: a doc/agent-instruction file that references code changed since the ref AND shares removed domain symbols is flagged for review (arXiv 2010.01625, F1 74.7)	✅ On
29	Reference-Existence	Two-revision check: a backticked code symbol present when the doc was last updated but gone at HEAD is flagged as outdated (arXiv 2212.01479)	✅ On
30	API-Doc-Smells	Bloated (≥300 words) / Lazy (≤6 prose words) API documentation units, keyed on signature-headed sections (F1 0.90/0.95)	✅ On
31	Doc-Dependency	A doc section that declares covers= is reported when a covered symbol's code changes semantically since its last docguard review --accept (formatting, comments and line moves do not count); opt-in by declaration	✅ On
32	Path-Scoped-Rules	Agent instruction files per harness (nested AGENTS.md/CLAUDE.md, Claude Code rules and skills, Cursor .mdc, Copilot .instructions.md, OpenHands skills): scope globs that match no tracked file, pointers to missing paths (including routing tables), instructions loaded for one path over the byte budget, and scopes a harness cannot read	✅ On
33	Doc-Ownership	With an ownership map in .docguard.json: source directories no doc section owns, two equally specific owners, patterns that match nothing, entries naming missing docs; also lints a committed .devin/wiki.json against Devin's limits and for paths that are gone	✅ On

Per-validator controls (in .docguard.json):

{
  "validators": {
    "test-spec": false,                 // disable (kebab-case OR camelCase both accepted)
    "freshness": true
  },
  "severity": {
    "todoTracking": "high",             // warnings fail CI
    "freshness": "low"                  // warnings ignored for exit code
  },
  "findingSeverity": {
    "TRC004": "low",                    // only this finding becomes informational
    "SEC001": "high"                    // this exact code always blocks
  }
}

Exact findingSeverity entries take precedence over validator severity. Guard JSON, SARIF, and JUnit retain the detector's intrinsic severity and add the effective severity plus the policy source. Intrinsic errors stay blocking unless their exact stable code is explicitly configured.

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🎚️ Reading a Finding

A finding used to answer one question — "how worried should you be?" — with one confidence field, which meant the field was doing three incompatible jobs at once. DocGuard now separates them. These axes are independent: a blocking error can be an escalation, and a high-confidence finding can still be one a human must judge.

Field	The question it answers	Values
severity / effectiveSeverity	Does CI block?	error, warn, info
disposition	Who decides — the tool or you?	act, escalate
confidence	How sure is the detector of its observation?	high, low
evidence.status	Has the reviewed corpus ever measured this code?	measured, not-measured
parserTier	Which analyzer produced it?	js-ast, py-ast, regex-fallback, fallback-language, mixed, not-applicable

act — DocGuard asserts a defect and names the correction. Safe to apply, including through docguard fix or an agent.

escalate — DocGuard observed a signal; the judgement is yours. Freshness FRS002 ("13 code commits since the document was reviewed") is the canonical case: the count comes from git log, so it is exact and confidence: high — and it establishes only that a review is due, never that the document is wrong. Editing a document until an escalation stops printing destroys the signal and fixes nothing. A judged-and-left escalation is a correct outcome.

evidence.status tells you what a confidence label is worth. measured quotes the reviewed precision corpus with n and a Wilson lower bound; not-measured means the label is a maintainer's prior and nothing more. Most codes are unmeasured — that does not make their findings wrong, only unverified, and docguard feedback samples them for exactly that reason.

parserTier tells you what the detector could see. regex-fallback or fallback-language means no syntax tree was available for that file — so the absence of a finding there is weak evidence, and the owning validator reports applicability: partial with the reason.

Every channel appears on every finding in guard --format json, in SARIF result.properties, and per-issue in diagnose --format json (which also emits a dispositionCounts summary). guard, diagnose, ci and report all print the act/escalate split beside the verdict; report adds a column per channel to its findings table. Run docguard explain <CODE> for one code's evidence.

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📄 Templates

DocGuard ships 18 professional templates with metadata, badges, and revision history:

Template	Type	Purpose
ARCHITECTURE.md	Canonical	System design, components, layer boundaries
DATA-MODEL.md	Canonical	Schemas, entities, relationships
SECURITY.md	Canonical	Auth, permissions, secrets management
TEST-SPEC.md	Canonical	Test strategy, coverage requirements
ENVIRONMENT.md	Canonical	Environment variables, deployment config
REQUIREMENTS.md	Canonical	Spec-kit aligned FR/SC IDs, user stories
DEPLOYMENT.md	Canonical	Infrastructure, CI/CD, DNS
ADR.md	Canonical	Architecture Decision Records
ROADMAP.md	Canonical	Project phases, feature tracking
KNOWN-GOTCHAS.md	Implementation	Symptom → gotcha → fix entries
TROUBLESHOOTING.md	Implementation	Error diagnosis guides
RUNBOOKS.md	Implementation	Operational procedures
VENDOR-BUGS.md	Implementation	Third-party issue tracker
CURRENT-STATE.md	Implementation	Deployment status, tech debt
AGENTS.md	Agent	AI agent behavior rules
CHANGELOG.md	Tracking	Change log
DRIFT-LOG.md	Tracking	Deviation tracking
llms.txt	Generated	AI-friendly project summary (llmstxt.org)

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🤖 AI Agent Support

One-click MCP install

• Claude Code: claude mcp add docguard -- npx docguard-cli mcp
• Claude Desktop: download docguard-v<version>.mcpb from the latest release and drag it into Settings → Extensions — you'll be asked which project folder to analyze. No npm, no JSON editing.
• Anything MCP: DocGuard is a verified namespace on the official MCP registry (io.github.raccioly/docguard).

DocGuard works with every major AI coding agent. All canonical docs are plain markdown — no vendor lock-in.

Agent	Compatibility	Auto-Generate Config
Google Antigravity	✅	docguard agents --agent antigravity
Claude Code	✅	docguard agents --agent claude
GitHub Copilot	✅	docguard agents --agent copilot
Cursor	✅	docguard agents --agent cursor
Windsurf	✅	docguard agents --agent windsurf
Cline	✅	docguard agents --agent cline
Google Gemini CLI	✅	docguard agents --agent gemini
Kiro (AWS)	✅	—

Always-on nudge hook (Claude Code)

docguard hooks --claude            # install   (remove: docguard hooks --claude --remove)

Registers a PostToolUse hook in the project's .claude/settings.json. After the agent edits a canonical doc it is nudged to run docguard guard --changed-only; after it edits a code file the docs reference, it is nudged toward docguard impact. Merge-safe (only DocGuard's own entry is ever added/removed), throttled to one nudge per file per 30 minutes, and the hook runtime can never break a session (errors are silent by contract). Explicit opt-in — init never installs it for you.

---

⚡ Slash Commands

DocGuard provides AI agent slash commands for integrated workflows. Installed automatically via docguard init or specify extension add docguard:

Command	What It Does
/docguard.init	Initialize Canonical-Driven Development in a new or existing project
/docguard.guard	Run quality validation — check all 32 validators
/docguard.review	Analyze doc quality and suggest improvements
/docguard.fix	Generate targeted fix prompts for specific issues
/docguard.update	Update canonical docs after code changes — detect drift and sync documentation

These commands are installed into your AI agent's command directory:

.github/commands/     → GitHub Copilot
.cursor/rules/        → Cursor
.gemini/commands/     → Google Gemini
.claude/commands/     → Claude Code
.agents/workflows/    → Antigravity

---

🧠 AI Skills (Enterprise)

Beyond slash commands, DocGuard provides 4 enterprise-grade AI skills — deep behavior protocols that tell AI agents not just what to run, but how to think, validate, and iterate. Skills are modeled after Spec Kit's skill architecture.

Skill	Lines	What It Does
docguard-guard	155	6-step quality gate with severity triage (CRITICAL→LOW), structured reporting, remediation
docguard-fix	195	7-step research workflow with per-document codebase research and 3-iteration validation loops
docguard-review	170	Read-only semantic cross-document analysis with 6 analysis passes and quality scoring
docguard-score	165	CDD maturity assessment with ROI-based improvement roadmap and grade progression

Workflow Hooks

DocGuard integrates into the spec-kit workflow as an automated quality gate:

Hook	When	Behavior
after_implement	After /speckit.implement	Mandatory — always runs DocGuard guard
before_tasks	Before /speckit.tasks	Optional — reviews doc consistency
after_tasks	After /speckit.tasks	Optional — shows CDD maturity score

Orchestration Scripts

For advanced users and CI/CD pipelines, DocGuard includes bash scripts with --json output:

Script	Purpose
docguard-check-docs.sh	Discover project docs, return JSON inventory with metadata
docguard-suggest-fix.sh	Run guard, parse results, output prioritized fixes
docguard-init-doc.sh	Initialize canonical doc with metadata header

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📁 Examples

Three real-world projects to see DocGuard in action:

Example	Scenario	What You'll See
01-express-api	Node.js API with zero docs	Cold-start: generate → instant coverage
02-python-flask	Python app with drifted docs	Drift detection: catch when docs lie
03-spec-kit-project	Full CDD + Spec Kit	Gold standard: what maturity looks like

See examples/README.md for step-by-step instructions.

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🧪 Testing

Test Suite

npm test    # 2,232 tests (node:test, zero test dependencies)

Covers all 25 commands, every validator, project type detection, compliance profiles, JSON/SARIF/JUnit output, the packed npm tarball, and downstream field reports replayed as regression cases. Static test-case declarations are a lower bound of that number: Metrics-Consistency flags this line if it ever falls below what the test files declare.

CI Matrix

Node.js	OS	Status
18	ubuntu-latest	✅
20	ubuntu-latest	✅
22	ubuntu-latest	✅
24	ubuntu-latest	✅

Self-Validation (Dogfooding)

DocGuard runs its own guard, score, diff, diagnose, and badge commands against itself in CI — ensuring the tool passes its own checks.

---

🏢 Enterprise Adoption

Everything runs local or in your CI — no SaaS, no data leaving your infra. The pieces that matter at company scale:

Need	DocGuard answer
Adopt on a legacy repo without a red pipeline on day one	guard --update-baseline freezes existing findings into a committed .docguard.baseline.json; only NEW drift gates from then on (suppression always visible)
Audit trail for compliance reviews	docguard report — commit-stamped evidence bundle (guard verdict, findings by code, CDD score, ALCOA+ data-integrity attributes, fix history) with a tamper-evident sha256 integrity hash
Every CI system, not just GitHub	guard --format sarif (GitHub Code Scanning) · --format junit (GitLab, Jenkins, Azure DevOps, CircleCI) · --format json (anything else)
Trajectory, not snapshots	docguard ci records every run to .docguard/history.jsonl; score --trend shows the sparkline + delta
AI agents on the team	MCP server (stdio or team-shared HTTP) exposes guard, score, verify, report, doc navigation and task context as read-only tools; agents --sync keeps the whole agent-file family drift-proof
Data-integrity framing auditors know	ALCOA+ scoring (FDA 21 CFR Part 11 / EMA Annex 11 vocabulary) built into score and report

⚙️ CI/CD Integration

Full recipes: see docs-canonical/CI-RECIPES.md for guard, auto-fix (commits mechanical fixes back to PRs), nightly sync, score-on-PR, and pre-commit configs.

GitHub Actions — Guard (most common)

name: DocGuard Guard
on: [pull_request, push]
permissions: { pull-requests: write }   # for the sticky PR comment (optional)
jobs:
  docguard:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with: { fetch-depth: 0 }
      - uses: raccioly/docguard@v0.42.1
        with:
          command: guard

The action installs the docguard-cli version it was released with, so pinning the action pins the CLI too. Set docguard-version: latest (or an exact x.y.z) to override.

On pull requests, guard mode also gives inline PR feedback (both default on):

Input	Default	Description
annotations	true	Inline ::error/::warning annotations on the PR diff, one per guard finding (capped at 50; a final notice reports how many were elided)
pr-comment	true	Sticky PR comment with the guard verdict, top findings (by code), and which canonical docs the PR's changed files impact (diff --since origin/<base>). Needs permissions: pull-requests: write; degrades to a log warning without it

Both run even when guard fails — that's when the feedback matters. Prefer native code-scanning integration? docguard guard --format sarif uploads straight to GitHub Code Scanning via github/codeql-action/upload-sarif.

GitHub Actions — Auto-Fix (commits mechanical fixes back)

name: DocGuard Auto-Fix
on: { pull_request: { types: [opened, synchronize, reopened] } }
permissions: { contents: write, pull-requests: write }
jobs:
  autofix:
    runs-on: ubuntu-latest
    if: github.event.pull_request.head.repo.full_name == github.repository
    steps:
      - uses: actions/checkout@v4
        with:
          ref: ${{ github.event.pull_request.head.ref }}
          token: ${{ secrets.GITHUB_TOKEN }}
          fetch-depth: 0
      - uses: raccioly/docguard@v0.42.1
        with: { command: fix, auto-commit: 'true', comment-on-pr: 'true' }

Pre-commit Hook

npx docguard-cli hooks --type pre-commit

Workflow starters (copy directly)

Two ready-to-use templates ship with the Spec Kit extension and as standalone files:

• extensions/spec-kit-docguard/templates/github-workflows/docguard-guard.yml — mandatory CI gate
• extensions/spec-kit-docguard/templates/github-workflows/docguard-autofix.yml — PR auto-fix

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✨ What's New

Highlights from recent releases:

• Calibrated finding channels — disposition, evidence.status and parserTier now sit beside severity and confidence on every finding, so "does CI block", "who decides", "how sure is the detector", "has this code ever been measured" and "which analyzer saw it" stop being one overloaded field. See Reading a Finding.
• Adoption baseline — guard --update-baseline freezes a legacy repo's existing findings into a committed .docguard.baseline.json; guard/ci then gate only NEW drift, with suppression always visible. Adopt today, burn down at your own pace.
• docguard report — commit-stamped compliance-evidence bundle (guard verdict, findings by code, CDD score, ALCOA+ attributes, fix history) with a tamper-evident sha256 integrity hash. Also exposed as the docguard_report MCP tool.
• Score history + score --trend — docguard ci records every run to .docguard/history.jsonl; the trend view shows the sparkline and delta over time.
• Three machine formats for guard — --format json, --format sarif (GitHub Code Scanning), and --format junit (GitLab, Jenkins, Azure DevOps, CircleCI).
• MCP server, stdio + team HTTP — guard, score, verify, report, doc navigation and task context as read-only agent tools: claude mcp add docguard -- npx docguard-cli mcp.
• Agent-file family sync — agents --sync treats AGENTS.md as canonical and regenerates CLAUDE.md / .cursor/rules / Copilot / Gemini variants with drift-proof source-hash markers.
• verify --evidence, verify --semantic, and verify --instructions — check exact local evidence declarations first, extract remaining numbers/limits/enums as agent tasks, and audit agent-instruction files for contradictions and stale pointers.
• docguard agent — one-shot ordered task graph with pre-filled code-truth, collapsing ~10 agent round-trips into one call.
• docguard agent --task <text> — opt-in task context from approved current specs and canonical docs, with hashed excerpts, source/test pointers, strict budgets, and honest abstention. The frozen 27-run evaluation preserved every tested behavior and cut median steps by 50% and latency by 17% versus the context pack, while using 80% more uncached input tokens.

See CHANGELOG.md for the full history.

---

📁 File Structure

your-project/
├── .specify/                        # Spec Kit (if using specify init)
│   ├── specs/
│   │   └── 001-feature/
│   │       ├── spec.md              # Requirements (FR-IDs, user stories)
│   │       ├── plan.md              # Implementation plan
│   │       └── tasks.md             # Task breakdown
│   ├── memory/
│   │   └── constitution.md          # Project principles
│   └── templates/
│
├── docs-canonical/                  # CDD canonical docs (the "blueprint")
│   ├── ARCHITECTURE.md              # System design, components
│   ├── DATA-MODEL.md                # Database schemas
│   ├── SECURITY.md                  # Auth, permissions, secrets
│   ├── TEST-SPEC.md                 # Required tests, coverage
│   ├── ENVIRONMENT.md               # Environment variables
│   └── REQUIREMENTS.md              # Spec-kit aligned FR/SC IDs
│
├── docs-implementation/             # Current state (optional)
│   ├── KNOWN-GOTCHAS.md
│   ├── TROUBLESHOOTING.md
│   ├── RUNBOOKS.md
│   └── CURRENT-STATE.md
│
├── AGENTS.md                        # AI agent behavior rules
├── CHANGELOG.md                     # Change tracking
├── DRIFT-LOG.md                     # Documented deviations
├── llms.txt                         # AI-friendly summary
└── .docguard.json                   # DocGuard configuration

---

⚙️ Configuration

Create .docguard.json in your project root (auto-generated by docguard init):

{
  "projectName": "my-project",
  "version": "0.4",
  "profile": "standard",
  "projectType": "webapp",
  "validators": {
    "structure": true,
    "docsSync": true,
    "drift": true,
    "changelog": true,
    "testSpec": true,
    "security": true,
    "environment": true,
    "docQuality": true,
    "specKit": true
  }
}

See Configuration Guide for all options.

---

🔬 Research Credits

DocGuard's quality evaluation and documentation generation patterns are informed by peer-reviewed research from the University of Arizona and the Joint Interoperability Test Command (JITC), U.S. Department of Defense:

• AITPG — AI-driven Test Plan Generator using Multi-Agent Debate and RAG (Lopez et al., IEEE TSE 2026)
• TRACE — Telecom Root Cause Analysis through Calibrated Explainability (Lopez et al., IEEE TMLCN 2026)

Lead researcher: Martin Manuel Lopez · ORCID 0009-0002-7652-2385

See CONTRIBUTING.md for full citations.

What the labels measure. DocGuard borrows TRACE's HIGH/MEDIUM/LOW vocabulary as deterministic strata (a validator's check pass-ratio). Detector precision is the quantity DocGuard actually measures: on a labelled, deliberately balanced benchmark corpus, published with sample sizes and Wilson 95% bounds in benchmarks/baseline.json (contract: schemas/docguard-benchmark-baseline.schema.json). Every number there carries a caveat explaining that benchmark precision on a balanced corpus differs from the base rate of stale claims in your repository. See VALIDATION.md.

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⭐ Star History

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🔒 Privacy & Supply Chain

DocGuard is local-first: no telemetry, no analytics, no phone-home — the full (short) policy is in PRIVACY.md. npm releases are published with provenance attestation, so you can verify each tarball was built by GitHub Actions from this repository.

📄 License

MIT — Free to use, modify, and distribute.

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Made with ❤️ by Ricardo Accioly