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Ralph Docs

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mikeyobrien
ralph-docs

Introspect, explain, and improve Ralph Orchestrator using its published llms.txt doc map. Use this skill whenever the user asks questions about Ralph's behavior, wants to understand how a Ralph internal works (event loop, hats, memories, tasks, backends, presets), debug an unfamiliar failure mode, or propose a code change to the ralph-orchestrator repo. The skill teaches the agent to discover authoritative answers from the live docs via llms.txt before guessing, and to scope improvements through the published architecture rather than the local checkout alone.

Overview

Publishermikeyobrien
Repositoryralph-orchestrator
Skill nameralph-docs
Stars
3.2K
Forks
299
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mikeyobrien on GitHub. Read the source before you install it.

Installation

Install the Ralph Docs 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.
    https://github.com/mikeyobrien/ralph-orchestrator/tree/main/skills/ralph-docs
  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/mikeyobrien/ralph-orchestrator.git /tmp/ralph-orchestrator
mkdir -p .claude/skills
cp -r /tmp/ralph-orchestrator/skills/ralph-docs .claude/skills/ralph-docs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph Docs 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 Ralph Docs 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 Ralph Docs 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.

Ralph Docs

Introspect Ralph Orchestrator the same way the framework expects a smart agent to — consult the published documentation map at https://mikeyobrien.github.io/ralph-orchestrator/llms.txt, fetch only the sections relevant to the question, and answer from authoritative sources.

Use this skill to behave like an internal Ralph contributor rather than a guess-first assistant.

Use This Skill For

  • Answering "how does Ralph do X?" questions about the event loop, hats, memories, tasks, presets, backends, CLI, TUI, diagnostics, waves, or API.
  • Explaining an observed behavior ("why did my loop terminate with max_iterations?", "why didn't my hat fire?") from first principles in the docs, not pattern-matching.
  • Proposing and scoping an improvement to the ralph-orchestrator codebase — locating the right crate, the relevant concept doc, and the existing test surface before writing code.
  • Triaging a ralph bug report: map symptoms to likely subsystem, pull in the concept + reference docs for that subsystem, identify the probable file path in the repo.
  • Onboarding: answer a new user's setup/quick-start questions from the official Getting Started pages instead of the agent's stale training data.

Core Principle: llms.txt Is The Router

Ralph's llms.txt is a curated map, not a full-text dump. The workflow is:

  1. Discover — fetch llms.txt to see the top-level sections (Getting Started / Concepts / User Guide / Advanced / API / Examples / Contributing / Reference).
  2. Narrow — pick the 1–3 pages that actually answer the question. The map links directly to .md versions — those are agent-optimized and should be preferred over scraping HTML.
  3. Fetch — pull only those pages. Do not fetch more than three pages speculatively; the budget should be spent on answering, not browsing.
  4. Cross-check — for code-level claims, confirm against the repo (crate paths are listed in AGENTS.md / CLAUDE.md inside the ralph-orchestrator checkout).
  5. Answer — cite the page you relied on. Include the URL so the user can verify.

Workflow

  1. Identify the question's subsystem using the taxonomy in references/llms-txt-map.md (hats, event loop, memories, tasks, backends, presets, CLI, TUI, diagnostics, waves, API).

  2. If ~/.cache/ralph-docs/llms.txt exists and is <7 days old, use it. Else refetch it:

    bash
    mkdir -p ~/.cache/ralph-docs
    curl -sSfL https://mikeyobrien.github.io/ralph-orchestrator/llms.txt \
      -o ~/.cache/ralph-docs/llms.txt
  3. Pick the 1–3 linked .md pages most relevant to the subsystem. The map entries are documented in references/llms-txt-map.md; use it to shortcut the grep.

  4. Fetch just those pages via curl -sSfL <url> -o ~/.cache/ralph-docs/<stem>.md and read them. Agents with web_fetch or an equivalent tool should use that instead.

  5. Answer the user's question grounded in what you just read. Quote the relevant sentence when the user asks "does Ralph do X?" so they can audit.

  6. If the answer requires a code change, switch to the ralph-orchestrator checkout and follow references/contributing.md for the propose-a-change workflow.

Scope Boundaries

  • This skill answers and explains. For creating/modifying user hats, defer to ralph-hats. For operating a live loop (running, resuming, merging, debugging), defer to ralph-loop. For code changes to ralph-orchestrator itself, this skill scopes the change; the actual editing uses the agent's native code-editing tools.
  • Do not invent features not present in the published docs. If llms.txt does not surface an answer, say so and suggest checking the source tree at https://github.com/mikeyobrien/ralph-orchestrator.
  • Do not rely on the agent's pretraining for version-sensitive claims (e.g. CLI flags, preset names). Ralph's CLI evolves; always verify against guide/cli-reference.md or reference/changelog.md.

Guardrails

  • Prefer .md URLs from llms.txt over scraping the rendered HTML.
  • Cache fetched pages under ~/.cache/ralph-docs/ with a 7-day staleness threshold. Refetch llms.txt before any other doc to detect renames/moves.
  • When cited docs contradict the local checkout (docs newer than the user's installed ralph version), note the mismatch and suggest ralph --version so the user can decide which to trust.
  • For "how to improve Ralph" requests, always read concepts/tenets/index.md first. Ralph's six tenets are load-bearing; changes that fight them usually belong somewhere else.

Output Expectations

  • Answer first, then link. Don't make the user wait for a verbose tour.
  • Include at least one source URL for non-trivial claims.
  • When proposing a code change, name the crate + file (see references/contributing.md for the crate map), the concept doc that justifies the change, and the test file that should cover it.

Read These References When Needed

  • For the llms.txt section map + which pages answer which question: references/llms-txt-map.md
  • For FAQ recipes (common introspection patterns): references/common-questions.md
  • For how to propose a code change, including crate layout and PR conventions: references/contributing.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ralph Docs AI skill do?

Introspect, explain, and improve Ralph Orchestrator using its published llms.txt doc map. Use this skill whenever the user asks questions about Ralph's behavior, wants to understand how a Ralph internal works (event loop, hats, memories, tasks, backends, presets), debug an unfamiliar failure mode, or propose a code change to the ralph-orchestrator repo. The skill teaches the agent to discover authoritative answers from the live docs via llms.txt before guessing, and to scope improvements through the published architecture rather than the local checkout alone.

Why use Ralph Docs on TypingMind?

Because you install it once and use it with any model. Ralph Docs 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 Ralph Docs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikeyobrien/ralph-orchestrator/tree/main/skills/ralph-docs. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ralph Docs?

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 Ralph Docs?

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

Is the Ralph Docs AI skill free?

Yes. It is published on GitHub by mikeyobrien 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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