code-assist
Guides implementation of code tasks using test-driven development in an Explore, Plan, Code, Commit workflow. Acts as a Technical Implementation Partner and…
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
$ npx -y skills add mikeyobrien/ralph-orchestrator --skill ralph-docs --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/ralph-docsContext preview
The summary Claude sees to decide when to auto-load this skill.
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
name: ralph-docs description: 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.
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.
memories, tasks, presets, backends, CLI, TUI, diagnostics, waves, or API.
max_iterations?", "why didn't my hat fire?") from first principles in the docs, not pattern-matching.
locating the right crate, the relevant concept doc, and the existing test surface before writing code.
concept + reference docs for that subsystem, identify the probable file path in the repo.
Getting Started pages instead of the agent's stale training data.
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.
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:
mkdir -p ~/.cache/ralph-docs
curl -sSfL https://mikeyobrien.github.io/ralph-orchestrator/llms.txt \
-o ~/.cache/ralph-docs/llms.txt3. 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.
**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.
not surface an answer, say so and suggest checking the source tree at <https://github.com/mikeyobrien/ralph-orchestrator>.
CLI flags, preset names). Ralph's CLI evolves; always verify against `guide/cli-reference.md` or `reference/changelog.md`.
threshold. Refetch llms.txt before any other doc to detect renames/moves.
installed ralph version), note the mismatch and suggest `ralph --version` so the user can decide which to trust.
`concepts/tenets/index.md` first. Ralph's six tenets are load-bearing; changes that fight them usually belong somewhere else.
`references/contributing.md` for the crate map), the concept doc that justifies the change, and the test file that should cover it.
`references/llms-txt-map.md`
`references/common-questions.md`
`references/contributing.md`
A hat-based orchestration framework that keeps AI agents in a loop until the task is done. "Me fail English? That's unpossible!" - Ralph Wiggum
Repo: mikeyobrien/ralph-orchestrator
Guides implementation of code tasks using test-driven development in an Explore, Plan, Code, Commit workflow. Acts as a Technical Implementation Partner and…
Generates structured .code-task.md files from descriptions or PDD implementation plans. Auto-detects input type, creates properly formatted tasks with…
Use when testing Ralph's hat collection presets, validating preset configurations, or auditing the preset library for bugs and UX issues.
Lists all code tasks in the repository with their status, dates, and metadata. Useful for getting an overview of pending work or finding specific tasks.
Transforms a rough idea into a detailed design document with implementation plan. Follows Prompt-Driven Development — iterative requirements clarification,…
Browser automation via Playwriter (remorses) using persistent Chrome sessions and the full Playwright Page API.