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 skills add h4vzz/awesome-ai-agent-skills --skill code-documentationgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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.
[](https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/code-documentation)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/code-documentation"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/code-documentation/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.
<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/code-documentation"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/code-documentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.1 | $0.00029 | $0.02012 |
| Opus 5.5 | $0.00012 | $0.00805 |
| Sonnet 5.5 | $0.00006 | $0.00402 |
| Haiku 4.5 | $0.00003 | $0.00201 |
Grade A, and why
code-documentation 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 1mo 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.
This is a copy
94% identical to code-documentation — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Documentation
This skill enables an AI agent to analyze source code and produce high-quality documentation in multiple formats. It covers everything from single-function docstrings to full project README files, ensuring that both human developers and downstream tooling (IDEs, doc generators) benefit from consistent, accurate descriptions.
Workflow
-
Inventory the Codebase: Walk the project tree and catalog public modules, classes, functions, constants, and type definitions. Note which symbols already have documentation and which are missing or stale.
-
Determine Documentation Scope: Based on the user's request, decide whether to generate inline docstrings, a standalone API reference, a project-level README, or a combination. Match the output format to the project's existing conventions (JSDoc, Google-style Python docstrings, TypeDoc, RDoc, etc.).
-
Analyze Signatures and Behavior: For each symbol, inspect parameter types, return types, default values, raised exceptions, and side effects. Read surrounding test files when available to understand intended usage and edge cases.
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Generate Documentation: Write documentation that includes a one-line summary, an extended description when the logic is non-trivial, parameter and return-value documentation with types, exception/error documentation, and at least one usage example for public API surfaces.
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Insert or Update In-Place: For inline documentation (docstrings, JSDoc comments), insert the generated text directly above or inside the relevant symbol. For standalone files (README, API reference), create or update the Markdown file at the project root or a
docs/directory. -
Validate and Cross-Reference: Verify that documented parameter names match the actual signature, that referenced types exist, and that examples are syntactically valid. Flag any inconsistencies for the user to review.
Supported Formats
- Python: Google-style docstrings, NumPy-style docstrings, Sphinx reStructuredText
- JavaScript / TypeScript: JSDoc (
@param,@returns,@throws), TypeDoc annotations - Java: Javadoc (
@param,@return,@throws) - Go: Godoc comment conventions (comment block immediately above the declaration)
- Rust:
///doc comments with Markdown,#[doc]attributes - Ruby: YARD (
@param,@return,@example) - Markdown: README files, CHANGELOG entries, architecture decision records (ADRs)
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.
- 1mo ago First seen · 215 lines · 29 tokens per session scan A ce7294dcf5e4
code-documentation is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 20d ago), licensed MIT. It adds 29 tokens to every session and 2,012 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to code-documentation, differing in 2 lines, and is treated as a copy.
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