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Omh Ralplan

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witt3rd
omh-ralplan

Planner+Architect+Critic→consensus impl plan (≤3 rounds)

Overview

Publisherwitt3rd
Repositoryoh-my-hermes
Skill nameomh-ralplan
Stars
371
Forks
33
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by witt3rd on GitHub. Read the source before you install it.

Installation

Install the Omh Ralplan 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/witt3rd/oh-my-hermes/tree/master/plugins/omh/skills/omh-ralplan
  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/witt3rd/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/plugins/omh/skills/omh-ralplan .claude/skills/omh-ralplan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Ralplan 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 Omh Ralplan 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 Omh Ralplan 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.

OMH Ralplan — Consensus Planning

When to Use

  • Before implementing any feature that touches multiple files or components
  • When architectural decisions need validation from multiple perspectives
  • When you need a plan that's been stress-tested against adversarial critique
  • When the user says: "plan this", "consensus plan", "ralplan", "let's think this through"

When NOT to Use

  • Trivial single-file changes (just do them)
  • Tasks where the approach is obvious and low-risk
  • When the user explicitly wants to skip planning

Prerequisites

  • A clear goal or specification (if ambiguous, use omh-deep-interview first)
  • The delegate_task tool must be available

Procedure

Phase 0: Context Gathering

Before planning, gather project context:

  1. Read relevant files to understand the codebase structure
  2. Identify existing patterns, conventions, and constraints
  3. Summarize context into a brief (~500 words) that all agents will receive

Phase 1: Planning Loop (max 3 rounds)

Round 1 — All Sequential (Planner → Architect → Critic):

Step 1 — Planner (single delegate_task)

delegate_task(
    goal="[omh-role:planner] Create an implementation plan for: {goal}\n\n{detailed_requirements}",
    context="# Project Context\n\n{project_context}"
)

The goal should include the full specification — don't assume the subagent knows anything.

Step 2 — Architect Review (single delegate_task)

delegate_task(
    goal="[omh-role:architect] Review this implementation plan for architectural soundness:\n\nPLAN:\n{planner_output}",
    context="# Project Context\n\n{project_context}"
)

Step 3 — Critic Challenge (single delegate_task)

delegate_task(
    goal="[omh-role:critic] Critically challenge this plan and architect review:\n\nPLAN SUMMARY:\n{plan_summary}\n\nARCHITECT REVIEW:\n{architect_verdict_and_concerns}",
    context="# Project Context\n\n{project_context}"
)

Step 4 — Consensus Check Check all three verdicts:

  • If ALL three are APPROVE → consensus reached, proceed to output
  • If any is REQUEST_CHANGES → collect all feedback, proceed to Round 2
  • If any is REJECT → output concerns and ask user whether to continue

Round 2+ — Planner Revises, Architect + Critic Re-review in Parallel:

When looping, the Planner must receive ALL feedback (Architect concerns + Critic critical issues + warnings). Be explicit about what needs to change — include the specific concern IDs (A1, C1, W1, etc.).

For Round 2 re-reviews, Architect and Critic are independent — run them in parallel via batch delegate_task:

delegate_task(tasks=[
    {goal: "[omh-role:architect] Re-review revised plan:\n{revised_plan}\n\nPrior concerns: {architect_concerns}", context: "{project_context}"},
    {goal: "[omh-role:critic] Re-review revised plan:\n{revised_plan}\n\nPrior concerns: {critic_concerns}", context: "{project_context}"}
])

This saves significant time (Round 2 re-reviews ran 14 seconds parallel vs ~120 seconds sequential).

Phase 2: Output

Write the consensus plan to .omh/plans/ralplan-{slug}.md containing:

  1. Consensus status (rounds, verdicts per round)
  2. Revision summary (what changed from feedback)
  3. The final plan (tasks with dependencies, complexity, acceptance criteria)
  4. Risks and open questions
  5. Round count and consensus status

Use a descriptive slug, not a timestamp: ralplan-deep-interview-consensus.md not ralplan-20260407.md.

Also write a summary to the user with the key design decisions that emerged from the debate.

State Management

State is per-instance, keyed on the plan slug (instance_id="{slug}"). Multiple ralplan sessions on different goals can run concurrently.

If the omh plugin is available, use it for state:

omh_state(action="write", mode="ralplan", instance_id="{slug}", data={
    "goal": "...", "round": 1,
    "phase": "planner|architect|critic|complete",
    "consensus": false, "plan_file": ".omh/plans/ralplan-{slug}.md"
})

If the plugin is not available, write .omh/state/ralplan--{slug}.json manually.

If a ralplan session is interrupted, list active instances and resume from the last completed phase:

listed = omh_state(action="list_instances", mode="ralplan")
state  = omh_state(action="read", mode="ralplan", instance_id="{slug}")

Singleton fallback (legacy). Omitting instance_id writes .omh/state/ralplan-state.json. Acceptable only when running one ralplan at a time.

Deliberate Mode (--deliberate)

When the user requests deliberate mode or uses "deliberate", "ADR", or "decision record", the Architect's output should follow Architecture Decision Record (ADR) format. Load references/adr-template.md for the template.

Pitfalls

  • Use [omh-role:NAME] markers in the goal field — the OMH plugin automatically injects the role prompt into the subagent's system prompt. Never inline role prompt text manually. Available roles: planner, architect, critic, executor, verifier, analyst, security-reviewer, code-reviewer, test-engineer, debugger. Fallback without plugin: omh_state(action="load_role", role="NAME") and pass returned prompt in context.
  • Include full specifications in the goal — subagents start with zero context. The goal + context must be self-contained.
  • Run Round 2+ reviews in parallel — Architect and Critic are independent in re-review rounds. Use batch delegate_task to save time.
  • Summarize feedback with IDs for the Planner — when looping, label feedback as A1/A2/C1/C2/W1 etc. so the Planner can address each point explicitly and the reviewers can check each one off.
  • Don't let the loop run more than 3 rounds — if no consensus by round 3, output with caveats
  • Each subagent must receive the project context, not just the plan — they need to evaluate against reality
  • The Critic should challenge, not block — if issues are minor, APPROVE with reservations
  • If the goal is ambiguous, stop and suggest omh-deep-interview instead of planning with unclear requirements
  • Use todo tracking — update a todo list with round/phase status so the user sees progress during what can be a 5-10 minute process
  • When porting/adapting from a reference system, feed actual source to subagents — if designing a skill inspired by another system (e.g., OMC → Hermes), the Planner, Architect, and Critic must read the actual source implementation, not just briefs or summaries. Extract reference docs from the source repo first, pass file paths in the delegate_task goal so subagents read them. Summaries lose critical field names, state schemas, edge cases, and design patterns.
  • The Critic's simplicity test can change architecture — don't dismiss it. In the ralph consensus, the Critic proposed one-task-per-invocation (instead of an in-session loop) which both reviewers then approved as fundamentally better. The consensus process finds the right architecture, not just validates the proposed one.

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 Omh Ralplan AI skill do?

Planner+Architect+Critic→consensus impl plan (≤3 rounds)

Why use Omh Ralplan on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/witt3rd/oh-my-hermes/tree/master/plugins/omh/skills/omh-ralplan. 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 Omh Ralplan?

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 Omh Ralplan?

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

Is the Omh Ralplan AI skill free?

Yes. It is published on GitHub by witt3rd 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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