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Hexdocs Fetcher

Community
oliver-kriska
hexdocs-fetcher

Fetch HexDocs for Elixir libraries with HTML-to-markdown conversion. Use when looking up docs on hexdocs.pm — modules, functions, guides, changelogs.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill namehexdocs-fetcher
Stars
555
Forks
40
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by oliver-kriska on GitHub. Read the source before you install it.

Installation

Install the Hexdocs Fetcher AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/oliver-kriska/claude-elixir-phoenix.git /tmp/claude-elixir-phoenix
mkdir -p .claude/skills
cp -r /tmp/claude-elixir-phoenix/plugins/elixir-phoenix/skills/hexdocs-fetcher .claude/skills/hexdocs-fetcher
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hexdocs Fetcher in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hexdocs Fetcher on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hexdocs Fetcher is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HexDocs Fetcher

Efficiently fetch Elixir library documentation from hexdocs.pm using Claude Code's native WebFetch tool.

Usage

When researching libraries, use WebFetch:

# Fetch library overview
WebFetch(
  url: "https://hexdocs.pm/oban",
  prompt: "Extract the main documentation, including module overview, installation instructions, and key functions. Format as clean markdown."
)

# Fetch specific module docs
WebFetch(
  url: "https://hexdocs.pm/phoenix_live_view/Phoenix.LiveView.html",
  prompt: "Extract the module documentation including all public functions, their specs, and examples."
)

# Fetch getting started guide
WebFetch(
  url: "https://hexdocs.pm/ecto/getting-started.html",
  prompt: "Extract the complete getting started guide content."
)

Token Efficiency

WebFetch automatically converts HTML to markdown and extracts relevant content:

SourceRaw HTMLWith WebFetchBenefit
HexDocs page~80k tokens~15k tokens80% reduction
Phoenix docs~120k tokens~25k tokens79% reduction
README~20k tokens~8k tokens60% reduction

Integration with hex-library-researcher

When evaluating libraries, fetch docs efficiently:

# Get library overview with focused extraction
WebFetch(
  url: "https://hexdocs.pm/oban",
  prompt: "Extract: 1) Installation instructions 2) Main features 3) Basic usage example"
)

Common HexDocs URLs

# Library overview
https://hexdocs.pm/{library}

# Module documentation
https://hexdocs.pm/{library}/{Module}.html
https://hexdocs.pm/{library}/{Module.Submodule}.html

# Guides
https://hexdocs.pm/{library}/guides.html
https://hexdocs.pm/{library}/{guide-name}.html

# API reference
https://hexdocs.pm/{library}/api-reference.html

Prompt Strategies

Use focused prompts for better extraction:

# For API docs
prompt: "Extract all public function docs with @spec and examples"

# For guides
prompt: "Extract the complete guide content preserving code examples"

# For troubleshooting
prompt: "Extract any troubleshooting sections, common errors, and FAQs"

# For configuration
prompt: "Extract configuration options and their defaults"

Caching

WebFetch includes automatic 15-minute caching. When fetching the same URL multiple times in a session, results are cached automatically.

For longer persistence, save to planning directory:

# After fetching, write the result to a file
Write(
  file_path: ".claude/plans/{slug}/research/docs/oban.md",
  content: "{extracted content}"
)

Tidewave Alternative

If Tidewave MCP is available, prefer mcp__tidewave__get_docs for exact version-matched documentation:

mcp__tidewave__get_docs(module: "Oban.Worker")

This fetches docs for the exact version in your mix.lock.

Iron Laws

  1. NEVER fetch entire HexDocs sites — always target specific modules or guides
  2. Use focused prompts — generic fetches waste tokens; specify what to extract
  3. Prefer Tidewave when available — exact version match beats generic hexdocs.pm

Frequently asked questions

What does the Hexdocs Fetcher AI skill do?

Fetch HexDocs for Elixir libraries with HTML-to-markdown conversion. Use when looking up docs on hexdocs.pm — modules, functions, guides, changelogs.

Why use Hexdocs Fetcher on TypingMind?

Because you install it once and use it with any model. Hexdocs Fetcher is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hexdocs Fetcher in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oliver-kriska/claude-elixir-phoenix/tree/main/plugins/elixir-phoenix/skills/hexdocs-fetcher. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hexdocs Fetcher?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hexdocs Fetcher?

As many as you like. As long as a model supports skills, you can use Hexdocs Fetcher with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hexdocs Fetcher AI skill free?

Yes. It is published on GitHub by oliver-kriska under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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