A documentation-generation command that scans source code and creates a fact-based set of project documents, including architecture, data, tests, security, environment, and API information.

Use it to preview captured code facts, create documentation skeletons, or export a machine-readable plan for an AI agent.

It reduces the gap between what the code actually does and what the documentation says, including in projects using several programming languages or a monorepo, a repository containing multiple related projects.

generate — Command by raccioly

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add commands/raccioly/docguard/generate

Clone the repo

git clone --depth 1 https://github.com/raccioly/docguard

Per session 9 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 660 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00009	$0.00660
Opus 5	$0.00005	$0.00330
Sonnet 5	$0.00002	$0.00132
Haiku 4.5	$0.00001	$0.00066

Measured 2d ago against content hash ec61ee12fba8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

generate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

extensions/spec-kit-docguard/commands/generate.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DocGuard Generate

Scans your codebase (JS/TS, Python, Rust, Go, Java/Kotlin, Ruby, PHP, C# — polyglot/monorepo-aware) and generates the canonical documentation memory: ARCHITECTURE.md, DATA-MODEL.md, TEST-SPEC.md, SECURITY.md, ENVIRONMENT.md, API-REFERENCE.md, SCREENS.md.

Two modes:

--plan (AI-powered, recommended) — emits a structured agent task manifest + writes the code-truth skeleton inside  markers. The AI agent then writes the prose grounded in scanned facts. Human prose is preserved.
default — purely deterministic generation: writes templated docs with TODO placeholders. Use when no AI agent is available.

User Input

$ARGUMENTS

Steps

Preview the plan — what code-truth facts were captured + what the agent will write:

npx --yes docguard-cli@latest generate --plan $ARGUMENTS

Scaffold the skeleton docs (marked sections filled with code-truth, prose sections as agent-task placeholders):

npx --yes docguard-cli@latest generate --plan --write $ARGUMENTS

Or get the machine-readable manifest to drive an agent:

npx --yes docguard-cli@latest generate --plan --format json $ARGUMENTS

Fallback (no AI, deterministic generation):

npx --yes docguard-cli@latest generate $ARGUMENTS

Review the generated docs in docs-canonical/. Each document includes:
- Structured sections based on industry standards
- Data extracted from your actual codebase (routes, schemas, configs)
- Standards citation footer referencing relevant specifications
- DocGuard metadata headers for freshness tracking
Customize with --doc <name> to generate a specific document only.

Generated Documents

Document	Source	Standard
ARCHITECTURE.md	Routes, configs, dependencies	arc42 / C4 Model
DATA-MODEL.md	Schema files, type definitions	C4 Component / ER
TEST-SPEC.md	Test files, test configs	ISO/IEC/IEEE 29119-3
SECURITY.md	Auth modules, .gitignore, secrets	OWASP ASVS v4.0
ENVIRONMENT.md	.env files, Docker, CI/CD configs	12-Factor App
API-REFERENCE.md	Route handlers, OpenAPI specs	OpenAPI 3.1

Read the full file on GitHub · 74 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 74 lines · 9 tokens per session scan A ec61ee12fba8

Subscribe to this mod's changes

generate is a command published in the GitHub repository raccioly/docguard (27 stars, last pushed 4d ago), licensed MIT. It adds 9 tokens to every session and 660 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Backend and APIs Security Testing ai-agents cdd cli documentation drift-detection eu-ai-act

generate

Getting it into your agent

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

DocGuard Generate

User Input

Steps

Generated Documents

What this file has done since we first saw it

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