docguard-score

docguard-score is a skill for Claude Code, Codex from raccioly/docguard. It costs 39 tokens per session (1,525 once invoked), scanned A, original, MIT.

A documentation maturity assessment for projects using DocGuard, a tool for checking software documentation. It runs DocGuard's scoring and validation commands, then breaks the result into quality categories.

In plain words
What is it for?
Use it to score documentation structure, writing quality, testing, security, environment details, change tracking, and architecture. It also creates an improvement roadmap with estimated gains.
Why use it?
It shows which parts of the project's documentation need the most improvement instead of leaving quality problems as a general impression.

Skill for Claude CodeCodex

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 skills/raccioly/docguard/docguard-score
Any agent
npx skills add raccioly/docguard --skill docguard-score
Clone the repo
git clone --depth 1 https://github.com/raccioly/docguard

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for docguard-score

README.md
[![agentmods](https://agentmods.dev/badge/skills/raccioly/docguard/docguard-score.svg)](https://agentmods.dev/skills/raccioly/docguard/docguard-score)
Your own site
<a href="https://agentmods.dev/skills/raccioly/docguard/docguard-score"><img src="https://agentmods.dev/badge/skills/raccioly/docguard/docguard-score.svg" alt="Measured on agentmods" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,525 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.

ModelPer sessionOnce invoked
Fable 5 $0.00039 $0.01525
Opus 5 $0.00019 $0.00763
Sonnet 5 $0.00008 $0.00305
Haiku 4.5 $0.00004 $0.00153

Measured 5d ago against content hash 75aefb0bff91, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

docguard-score 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 5d 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.

.agent/skills/docguard-score/SKILL.md · 180 lines

How it starts

The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DocGuard Score Skill

User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Goal

Run DocGuard's CDD maturity scoring engine, analyze the category breakdown, identify highest-ROI improvements, and produce an actionable improvement roadmap showing projected score gains per fix.

Execution Flow

Step 1: Run Scoring Engine

Execute both tools for comprehensive data:

npx docguard-cli score 2>&1
npx docguard-cli guard 2>&1

If in DocGuard dev environment:

node cli/docguard.mjs score 2>&1
node cli/docguard.mjs guard 2>&1

Step 2: Parse Score Breakdown

Extract the category-level scores:

Category Score Weight Points Potential Gain
Structure X% ×25 N [25 - N]
Doc Quality X% ×20 N [20 - N]
Testing X% ×15 N [15 - N]
Security X% ×10 N [10 - N]
Environment X% ×10 N [10 - N]
Drift X% ×10 N [10 - N]
Changelog X% ×5 N [5 - N]
Architecture X% ×5 N [5 - N]

Calculate Potential Gain for each category = (weight - current points).

Step 3: Grade Classification

Grade Score Description
A+ 95-100 Exemplary — production-grade documentation, CDD fully adopted
A 85-94 Strong — minor improvements possible, CI-gate ready
B 70-84 Good — documentation covers essentials, some gaps exist
C 50-69 Fair — significant documentation debt, multiple gaps
D 30-49 Poor — major structural gaps, limited doc coverage
F 0-29 Critical — documentation infrastructure missing

Step 4: ROI-Based Improvement Roadmap

Sort categories by Potential Gain / Effort ratio:

For each category below 100%, calculate:

  • Gap: What checks are failing?
  • Effort: How hard is it to fix? (LOW = update metadata, MEDIUM = add content, HIGH = research + write)
  • Impact: Points gained if fixed to 100%
  • ROI: Impact / Effort ranking

Read the full file on GitHub · 180 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.

  1. 5d ago First seen · 180 lines · 39 tokens per session scan A 75aefb0bff91

Subscribe to this mod's changes

docguard-score is a skill published in the GitHub repository raccioly/docguard (27 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,525 once invoked, about $0.0002 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.