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Ralph

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Q00
ralph

MCP-owned Ralph loop around background evolve_step jobs

Overview

PublisherQ00
Repositoryouroboros
Skill nameralph
Stars
6K
Forks
605
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 Q00 on GitHub. Read the source before you install it.

Installation

Install the Ralph 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/Q00/ouroboros.git /tmp/ouroboros
mkdir -p .claude/skills
cp -r /tmp/ouroboros/skills/ralph .claude/skills/ralph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph 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 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 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.

/ouroboros:ralph

MCP-owned Ralph loop around background evolve_step jobs. "The boulder never stops."

Usage

ooo ralph --lineage-id <lineage_id>
/ouroboros:ralph --lineage-id <lineage_id>

# For a plain natural-language request, run `ooo interview` + `ooo seed` first,
# then call the MCP tool with a fresh lineage_id and the validated Seed YAML.

Trigger keywords: "ralph", "don't stop", "must complete", "until it works", "keep going"

How It Works

Ralph is owned by the ouroboros_ralph MCP tool. In non-plugin runtimes, the tool starts one background Ralph job, runs repeated evolve_step generations inside that job, and stops only when QA passes, convergence is reached, a terminal evolution action occurs, cancellation is requested, or max_generations is reached. In OpenCode plugin mode, the MCP tool returns a delegated_to_plugin envelope with job_id=None; the bridge plugin dispatches a child Task session that owns the loop instead of creating a local JobManager job.

The client skill should not reimplement the loop. Deterministic frontmatter dispatch is limited to the router's named --lineage-id option so raw trailing text is never treated as lineage identity. Raw natural-language ooo ralph "<request>" input must flow through the validated Seed path before any mutating Ralph loop starts. Until a lineage id and optional Seed YAML are prepared, ouroboros_ralph returns structured input guidance instead of starting a job. Once the inputs are prepared, start the MCP-owned Ralph surface once, then follow either the returned job tools path or the OpenCode Task widget path.

Instructions

When the user invokes this skill:

Load MCP Tools (Required first)

The Ouroboros MCP tools are often registered as deferred tools that must be explicitly loaded before use. Do this before preparing input or calling Ralph:

  1. Use the active runtime's tool-discovery capability to find and load the Ralph/job MCP tools:
    tool discovery query: "+ouroboros ralph job"
  2. The loaded tools may be exposed under plugin-prefixed names such as mcp__plugin_ouroboros_ouroboros__ouroboros_ralph. Use the actual tool names returned by runtime tool discovery; the bare names below are the canonical MCP tool names for documentation.
  3. Confirm that ouroboros_ralph and the job tools (ouroboros_job_wait, ouroboros_job_status, ouroboros_job_result, and ouroboros_cancel_job) are callable. If the tools are unavailable, stop and tell the user that Ralph requires the Ouroboros MCP runtime.

Ralph Flow

  1. Prepare lineage input:

    • If the user provides an existing lineage_id and explicitly wants to continue it, reuse that lineage_id and omit seed_content unless they explicitly provide an updated Seed.
    • If the user provides Seed YAML for a new Ralph run, use it as seed_content and generate a fresh lineage_id for this run. Keep lineage_id separate from Seed, interview, and session IDs so separate Ralph runs over the same Seed do not collide.
    • If the user provides only a plain natural-language request, do not treat it as a direct ooo ralph "<request>" command, do not freehand Seed YAML, and do not pass raw text as seed_content. Route through the authoritative Seed path first: ooo interview to capture requirements, then ooo seed / ouroboros_generate_seed to produce validated Seed YAML with the normal ambiguity gate. After Seed generation, call the MCP tool with a fresh lineage_id and that validated Seed YAML as seed_content; do not use the raw request text. If an interview/seed session already exists in context, reuse that validated Seed output instead of regenerating it.
  2. Start Ralph by calling ouroboros_ralph with:

    • lineage_id: existing lineage id for an explicit continuation, otherwise a freshly generated stable id for this Ralph run, such as ralph-<short-slug>-<uuid>; do not use a Seed/interview id by itself
    • seed_content: valid Seed YAML for generation 1 when starting a new lineage
    • execute: default true
    • parallel: default true
    • skip_qa: default false
    • project_dir: explicit target project directory when known
    • max_generations: default 10 unless the user requests a tighter bound
  3. Handle the start response:

    • If response.meta.job_id is present, report it concisely and retain the job cursor from response.meta.cursor:

      [Ralph] Started background loop: <job_id>
      Lineage: <lineage_id>
      Live view: <dashboard_url, or `ouroboros tui open`>
      
      A read-only observer will post meaningful progress, attention, and terminal
      events here. This conversation remains available for other safe work.
    • If response.meta.job_observer is unavailable, recover it from the final <!-- ouroboros-job-observer-v1 base64 ... --> content sentinel. Fail closed unless the bounded payload passes canonical v1 validation and its job identity matches the visible start receipt. Use that ID only as an identity anchor, never to reconstruct tools or arguments. Reject validation failure or any mismatch between structured and inline surfaces.

    • If the structured or recovered job_observer is present and the host supports an independent child session, spawn exactly one read-only observer and pass that contract unchanged. The observer exclusively owns job wait/result calls and the cursor. The main session retains only user conversation, explicit on-demand status, and cancellation when the user requests it. The main session must not poll the same job while the observer is active. It may refine requirements, perform read-only review, or work in an unrelated isolated worktree; check active-worker conflicts before writing to Ralph's workspace. On Codex, call spawn_agent exactly once with task_name="run_observer"; wait is not a spawn, and do not claim an observer until a live child ID/path is returned. Once acknowledged, keep the parent turn open with wait_agent calls of at most 60 seconds while the observer is active. Child send_message calls only enqueue mailbox events and cannot revive an ended parent turn. Relay meaningful updates and wait again until terminal. On OMP, submit exactly one native Task child named RunObserver, require its live agent/job ID, and use the host wait/inbox relay until terminal. User input may interrupt the wait; handle it and resume waiting while observation remains active unless the user asks to stop live observation or replaces the active request. Then end only the relay loop, keep the durable job running, and offer next-turn or explicit-status catch- up. If the observer child fails, is cancelled, or exits before a terminal summary, use that same fallback instead of waiting indefinitely. This relay loop must not poll the Ouroboros job or take cursor ownership. If spawn fails, do not promise live proactive relays: the detached worker continues after the stdio turn, and the main session catches up from durable events on the next interaction or explicit status request. Keep the main turn open in the fallback polling loop only when the user asked for live watching.

    • If response.meta.status == "delegated_to_plugin" and response.meta.job_id is None, report that OpenCode plugin mode delegated the loop to a child Task session. Do not call ouroboros_job_wait, ouroboros_job_result, or ouroboros_cancel_job without a job id; follow the host Task widget/session lifecycle instead.

  4. Monitor non-plugin job progress in the polling owner when a job_id exists.

    The delegated observer is the default owner. Use the main-session loop below only when no independent child session exists and the user explicitly asked for live watching; otherwise catch up on the next parent turn. Never run both loops:

    • ouroboros_job_wait(job_id, cursor, timeout_seconds=120, stream="linked", wait_for="attention_or_ac_change") for long polling; after every wait/status response, update cursor = response.meta.cursor
    • ouroboros_job_status(job_id) for a quick status check
    • ouroboros_job_result(job_id) when the job is terminal
    • ouroboros_cancel_job(job_id) if the user says stop/cancel

    Observer events are concise: relay phase/progress changes in 1-2 lines, surface attention_required immediately, present terminal as the final result, distinguish Synapse queued from runtime-proven applied, surface rejected/uncertain delivery immediately, and suppress unchanged heartbeats or raw tool output. Render every relay in the user's current conversation language; preserve raw event codes only when exact diagnostics help. Interpret structured subtypes: report run configuration, total ACs and dependency/parallel levels, first scheduled ACs, bounded Discover targets, current model/harness changes, level transitions, and verified AC completion. Say "currently running with" because later generations may escalate or switch harnesses. Never forward raw commands or model reasoning.

    When a new generation starts, do not just report the generation number — lineage.generation.started carries an ac_focus block (active_ac_indices, frozen_ac_indices, active_ac_descriptions, reason). Report WHAT the generation is redoing, e.g. "Gen 7: 2/5 AC 재작업 — 'CSV export writes summary.csv', 'CLI exits 0 on --help' (3 AC는 이전 PASS 증거로 frozen)". When ac_focus is absent or every AC is active with reason "initial/full generation", say the full AC graph is being executed. Never quote verify commands or expected outputs — descriptions only.

    When the user asks a live AC a read-only question or provides additive intent, reload +ouroboros session signal, call ouroboros_session_signal_targets for the observed execution, and select the semantically relevant AC without asking for internal IDs. Use mode="inform" for assurance/questions and omit fallback_mode in that mode. For implementation refinement use contract_effect="additive", source="user", mode="redirect", and explicit fallback_mode="after_turn" with the exact discovered guards. Shared goal/AC/ constraint/non-goal changes require an approved shared successor.

  5. On non-plugin job termination, the polling owner fetches ouroboros_job_result(job_id) and summarize the final job result and next step:

    • Success / convergence: summarize the final generation output, QA verdict, and any worktree_path / worktree_branch returned in job metadata. Do not present ooo evaluate as an automatic next step for Ralph results: the Ralph job contract preserves the evolution lineage_id, but it does not reliably preserve a separate execution session_id for the evaluate workflow. If a valid execution session_id is explicitly available from a separate run result, keep it distinct from the Ralph lineage_id and follow the ooo evaluate <session_id> contract; otherwise state that formal evaluation needs a real execution session and should not be invoked from the Ralph lineage id alone.
    • Max generations / failure: summarize the stop reason and suggest ooo unstuck, ooo interview, or a narrower Ralph retry
    • Cancelled: confirm cancellation and preserve the job id for later inspection
  6. On OpenCode plugin delegation, rely on the child Task result as the terminal surface. Summarize the Task completion/error state and lineage id; do not claim a local Ralph job can be polled or cancelled.

Active Conductor decision policy

For attention_required, use at most one short-lived read-only verifier. If the host has no verifier primitive, surface the evidence and do not mutate. Otherwise VERIFY → DECIDE from recommended_host_actions → LOG selected with ouroboros_record_conductor_decision → ACT only a menu-listed registered tool → LOG exactly one completed, failed, or declined outcome. Ralph may apply a conductor directive only to the first and sole bounded successor generation (max_generations=1), and only when it is deterministic and non-relaxing. Never silently retry or weaken the approved shared contract.

These are English canonical host instructions. Render them naturally in the user's conversation language.

Tool Mapping

Skill actionMCP tool
Start Ralph loopouroboros_ralph
Wait for progressouroboros_job_wait
Fetch final resultouroboros_job_result
Cancel loopouroboros_cancel_job
Inspect current statusouroboros_job_status

The Boulder Never Stops

This is the key phrase. Ralph does not give up:

  • Each failure is data for the next attempt.
  • Verification drives the loop.
  • Only success, convergence, terminal failure, cancellation, or max-generation limits stop it.

RFC #1392 State Breadcrumb Footer

Your final response MUST end with exactly one breadcrumb footer line:

◆ <current state> → next: <recommended action>

Derive <current state> from live session state via ouroboros_session_status when that MCP projection is available; otherwise derive it from this skill's actual outcome. Never use a linear Step N of M footer because Ouroboros is an evolutionary loop. When the next action is genuinely a choice, list 2-3 honest options in the next: clause. The breadcrumb line must be the last line of the response.

Frequently asked questions

What does the Ralph AI skill do?

MCP-owned Ralph loop around background evolve_step jobs

Why use Ralph on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Q00/ouroboros/tree/main/skills/ralph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ralph?

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?

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

Is the Ralph AI skill free?

Yes. It is published on GitHub by Q00 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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