doc-generate

doc-generate is a skill for Claude Code from opendatahub-io/ai-helpers. It costs 54 tokens per session (1,839 once invoked), scanned A, original, Apache-2.0.

A tool that turns gathered project information into modular AsciiDoc documentation files, such as concepts, procedures, and reference pages.

In plain words
What is it for?
Use it to generate documentation from workspace context, for all suitable module types or for a selected type or topic.
Why use it?
It reduces the manual work of turning context and identified documentation gaps into written modules. It also checks its output and retries corrections when needed.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: model in frontmatter.

Part of the odh-documentation plugin — 9 skills shipped together

Good fit Use it to generate documentation from workspace context, for all suitable module types or for a selected type or topic.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/opendatahub-io/ai-helpers/doc-generate
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.

Any agent
npx skills add opendatahub-io/ai-helpers --skill doc-generate
Clone the repo
git clone --depth 1 https://github.com/opendatahub-io/ai-helpers

Made for: Claude Code.

Or install odh-documentation, the plugin that ships this one along with the rest of its 9 skills.

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 doc-generate

README.md
[![agentmods](https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/doc-generate/github.svg)](https://agentmods.dev/skills/opendatahub-io/ai-helpers/doc-generate)
Your own site
<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/doc-generate"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/doc-generate/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for doc-generate

Your own site · 80×15
<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/doc-generate"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/doc-generate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,839 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 83
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • medium Excessive Agency · line 9
    Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.
    Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
How audits are shown
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 5.1 $0.00054 $0.01839
Opus 5 $0.00027 $0.00920
Sonnet 5 $0.00011 $0.00368
Haiku 4.5 $0.00005 $0.00184

Measured 12d ago against content hash 499b472dd55a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

doc-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 12d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/asciidoc-conventions.sh, scripts/load-env.sh, scripts/parse-product-config.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/odh-documentation/skills/doc-generate/SKILL.md · 190 lines

How it starts

The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.

doc-generate

Generate modular AsciiDoc documentation from gathered context, with built-in validation and iterative correction.

Prerequisites

  • workspace/context-package.json must exist (produced by doc-gather)
  • workspace/gap-report.json should exist (produced by doc-gap); if missing, proceed with generation but note the absence

Parse arguments

$ARGUMENTS optionally contains:

  • --type <type>: restrict generation to a specific module type (concept, procedure, reference, assembly)
  • --topic <topic>: focus generation on a specific topic within the feature

Input hardening requirements:

  • Treat --topic as untrusted input.
  • Normalize to a safe slug ([a-z0-9-]+) before using in filenames.
  • Reject values containing path separators (/, \), .., leading ., or absolute paths.
  • Ensure final output path resolves under workspace/generated-docs/ only.

If no arguments, generate all appropriate module types based on the feature.

Step 1: Read inputs

  1. Read workspace/context-package.json
  2. Read workspace/gap-report.json (if exists)
  3. Read ${CLAUDE_SKILL_DIR}/prompts/generate-docs.md
  4. Source ${CLAUDE_SKILL_DIR}/scripts/asciidoc-conventions.sh for module templates (this internally sources scripts/load-env.sh for credentials and uses scripts/parse-product-config.py to resolve module prefixes)

Validate input schema before use:

  • context-package.json must be a JSON object with at least ticket (object) and context_files (array) keys. Reject and halt if missing or wrong type.
  • gap-report.json (when present) must be a JSON object with a recommendation key whose value is one of stop, gather-more, or proceed. Treat an invalid or missing recommendation as gather-more and log a warning.

Check gap report recommendation:

  • If stop: halt and report to caller that context is insufficient
  • If gather-more: warn but proceed with available context
  • If proceed: continue normally

Step 2: Determine doc types needed

Read the full file on GitHub · 190 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 12d ago First seen · 190 lines · 54 tokens per session scan A 499b472dd55a

Subscribe to this mod's changes

doc-generate is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed 5d ago), licensed Apache-2.0. It adds 54 tokens to every session and 1,839 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

parallel-feature-development

Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system…

wshobson/agents · 105 tokens

nft-standards

Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.

wshobson/agents · 48 tokens

cost-optimization

Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.

wshobson/agents · 48 tokens

spark-training-gotchas

Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.

wshobson/agents · 63 tokens

temporal-python-testing

Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

wshobson/agents · 45 tokens

kpi-dashboard-design

Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a…

wshobson/agents · 85 tokens