How does this skill differ from just asking an AI to check my documentation for errors?

Unlike generic LLM prompts that focus on typos or grammar, this skill specifically simulates code execution to identify functional errors, missing dependencies, and incorrect method names that would cause a developer's environment to fail.

What specific environment or AI agents do I need to run this skill?

This skill is designed for CLI-based agents like Claude Code or Gemini CLI and requires an environment with an MCP (Model Context Protocol) server equipped with browser automation tools like Playwright.

What exactly is included in the purchase of the 'doc audit contributor' skill?

You receive the specialized skill logic for documentation auditing, pre-configured workflows for PR generation, and a guide on connecting it to your browser-enabled MCP server.

Does this skill also check for broken links or website accessibility issues?

No, the skill focuses strictly on content accuracy and code usability; it does not check for broken hyperlinks, UI/UX bugs, or CSS styling issues.

Can I use this to audit an entire documentation site with hundreds of pages at once?

The skill is optimized for high-impact technical pages such as Quickstarts, Tutorials, and API References, crawling 5 to 8 pages per run to ensure deep, high-quality analysis.

How does the skill help me actually submit a contribution to GitHub?

The skill generates a structured report including suggested branch names, conventional commit messages, and a detailed description of the fix, allowing you to open a PR in seconds.

Doc Audit Contributor

Example session with this skill installed

Audit the OpenAI Agent SDK docs for any broken code examples or missing setup steps. I want to make a quick open-source contribution.

Read your context and instructions
Compiled the doc audit contributor

I have audited the OpenAI Agent SDK documentation (root: /docs/agent-sdk). After analyzing the Quickstart, Configuration, and Examples pages, I found one critical code error.

Documentation Audit Findings

Quickstart Guide (/docs/agent-sdk/quickstart)

Type: Broken code
Problem: The initialization example uses AgentClient(api_key=...) but the library actually requires AgentClient(auth_token=...).
Why: Internal consistency check; the API reference page for AgentClient confirms auth_token is the required parameter.
Fix: Update the parameter name in the initialization block to auth_token.

Branch: docs/fix-agent-client-init-param
Commit message: docs: fix incorrect initialization parameter in quickstart
PR title: docs: update AgentClient parameter to auth-token in Quickstart
PR description:


## What
The Quickstart guide uses an incorrect parameter name for the AgentClient constructor.

## Why
Running the example as-written results in a TypeError: __init__() got an unexpected keyword argument 'api_key'. The core library expects 'auth_token'.

## Fix
Changed `api_key` to `auth_token` in the Python code example.

## Verification
Cross-referenced with the API Reference documentation and validated against the SDK constructor signature.

Next steps

Create the branch docs/fix-agent-client-init-param in your local clone.
Apply the one-line fix to docs/quickstart.md.
Push and open the PR using the generated description.

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About this skill

Documentation bugs are the easiest way to start contributing to open source, but manually auditing pages for broken code is slow and tedious. This skill automates the discovery of high-value documentation errors that maintainers actually want to fix. It bypasses superficial UI bugs to focus strictly on content accuracy and code usability.

What it does

Broken code detection identifies syntax errors, wrong method names, or incorrect parameters in documentation examples.
Dependency verification flags missing imports, environment variables, or setup steps required to run examples.
Content clarity audits pinpoint misleading explanations or logical gaps that confuse new developers.
Automated PR packaging generates branch names, conventional commit messages, and structured PR descriptions for every finding.

How it works

Target Identification: Specify a documentation URL or project name to locate the docs root.
Page Selection: The skill crawls the navigation to pick 5 to 8 high-impact pages like Quickstarts and API references.
Deep Content Analysis: It extracts code blocks and prose, simulating a top-to-bottom execution of every instruction.
Validation: It cross-references findings against the source repo or internal consistency checks to ensure the bug is real.
Ready-to-Submit Output: You receive a formatted report with everything needed to open a GitHub PR immediately.

Frameworks & tools

This skill requires an MCP server with Playwright-style browser automation tools. It is optimized for CLI-based agents like Claude Code, Codex, or Gemini CLI that can navigate live documentation sites.

Why this beats prompting it yourself

Generic prompts often return "style suggestions" or "typo fixes" that maintainers ignore. This skill uses a strict verification logic to find functional errors in code examples, providing the exact PR metadata needed to contribute in seconds.

Use cases

Find "good first issues" in a new open-source library you want to support.
Audit your own project's docs before a major release to ensure examples work.
Quickly spot breaking changes in documentation after an API update.
Scale open-source contributions by automating the discovery and drafting phase.

Known limitations

Does not report UI bugs, broken links, accessibility issues, or styling errors. Requires a browser-enabled MCP environment to function.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Doc Audit Contributor