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.
npx agentmods add commands/raccioly/docguard/docguard.fixgit clone --depth 1 https://github.com/raccioly/docguardWhat 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 | $0.00012 | $0.00740 |
| Opus 5 | $0.00006 | $0.00370 |
| Sonnet 5 | $0.00002 | $0.00148 |
| Haiku 4.5 | $0.00001 | $0.00074 |
Grade A, and why
docguard.fix 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.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/docguard.fix — Find and Fix CDD Documentation Issues
You are an AI agent responsible for maintaining documentation quality using DocGuard.
Step 1: Mechanical fixes first (no AI judgment needed)
npx docguard-cli fix --write
This deterministically applies the safe fix class: broken doc anchors, stale counts bound to code collections, stale version references. Each fix is provenance-checked and fail-closed — it never rewrites content whose source of truth it cannot verify. Doing this first shrinks the issue list you research.
Step 2: Assess what remains
npx docguard-cli diagnose
Parse the output — issues are categorized with AI-ready fix prompts. Every
finding carries a stable code; run npx docguard-cli explain <CODE> whenever
the right remediation isn't obvious from the message.
If no issues remain, report "All CDD documentation is up to date" and stop.
Step 3: Fix each issue
| Issue Type | Action |
|---|---|
missing-file |
Run npx docguard-cli fix --doc <name> to generate |
empty-doc / partial-doc |
Proceed to Step 4 for codebase research |
missing-config |
Create .docguard.json based on project type |
stale-doc |
Update docguard:last-reviewed date and content |
quality-issue |
Fix negation language, add missing sections |
| false positive | Suppress at the site: // docguard:ignore <CODE> (with a reason comment), and report it: npx docguard-cli feedback |
Doc wrong vs code wrong: a doc/code mismatch does not automatically mean
the doc is stale. Canonical docs are the spec — if the code drifted from a
documented decision, flag the code (or record the deviation with a
// DRIFT: reason comment + DRIFT-LOG.md entry) instead of silently rewriting
the doc to match the regression.
Step 4: Write real content
For each document that needs content:
npx docguard-cli fix --doc <name>
Where <name> is: architecture, data-model, security, test-spec, environment
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 · 85 lines · 12 tokens per session scan A d6fbce2b193d
docguard.fix is a command published in the GitHub repository raccioly/docguard (27 stars, last pushed 4d ago), licensed MIT. It adds 12 tokens to every session and 740 once invoked, about $0.0001 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.
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