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Omh Ralph

Community
witt3rd
omh-ralph

execute plan: 1 task/call, verify evidence, iron law

Overview

Publisherwitt3rd
Repositoryoh-my-hermes
Skill nameomh-ralph
Stars
371
Forks
33
Bundled files
1
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.

  • 1 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 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.
    https://github.com/witt3rd/oh-my-hermes/tree/master/plugins/omh/skills/omh-ralph
  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-ralph .claude/skills/omh-ralph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh 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 Omh 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 Omh 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.

OMH Ralph — Verified Execution (v2)

Requires the OMH plugin. Install plugins/omh/ from this repo to ~/.hermes/plugins/omh/.

When to Use

  • You have a plan (from omh-ralplan or manual) and need verified execution
  • The user says: "ralph", "don't stop", "until done", "must complete", "keep going"
  • You need guaranteed verification — not just "looks done" but evidence-backed completion
  • Multi-step implementation where each task must be independently verified

When NOT to Use

  • No plan or spec exists (use omh-deep-interview and/or omh-ralplan first)
  • Trivial single-file changes (just do them directly)
  • The user explicitly wants to skip verification

Architecture: One Task Per Invocation

Each ralph invocation does ONE unit of work and exits. The caller re-invokes for the next task. This eliminates context window exhaustion and makes every invocation a clean checkpoint.

Invocation N:  read state → pick task → execute → verify → update state → EXIT
Invocation N+1: read state → pick next task → execute → verify → update state → EXIT
...
Final invocation: all tasks pass → architect review → mark complete → EXIT

Procedure

Step 0: Resolve Instance and Acquire Lock

Ralph and autopilot mutate a shared .omh/plans/ plan. Two sessions running ralph against the same plan would race on ralph-tasks state and produce non-deterministic outcomes. Use per-instance state + advisory lock to make concurrent plans safe.

  1. Resolve instance_id from the plan path:
    • Default plan source: .omh/plans/ralplan-*.md or .omh/plans/ralph-plan.md.
    • instance_id = basename(plan_path) without ".md" (engine slugifies).
    • If no plan exists yet (Step 2 will choose), use instance_id="default".
  2. Acquire the lock before reading/writing state:
    lock = omh_state(action="lock", mode="ralph",
                     lock_key="{instance_id}",
                     session_id="{HERMES_SESSION_ID or uuid}",
                     holder_note="ralph executing {plan_path}")
    • acquired=true: continue.
    • acquired=false: report held_by to the user (pid + session_id + started_at). Offer: wait / cancel the holder (omh_state(action="cancel", mode="ralph", instance_id="{instance_id}"), then on next invocation the dead holder's lock will be released automatically by stale-pid detection) / pick a different plan.
  3. Pass instance_id to every subsequent omh_state call in this invocation — both the ralph mode and the ralph-tasks mode.
  4. Release the lock at every exit point (success, blocked, cancel, max-iterations, exception):
    omh_state(action="unlock", mode="ralph",
              lock_key="{instance_id}",
              session_id="{HERMES_SESSION_ID or uuid}")
    Wrap the body of the procedure so the unlock fires even on error.

Singleton fallback (legacy). If a caller omits instance_id, the engine writes the legacy .omh/state/ralph-state.json and skips locking. Only use this for one-plan-at-a-time setups.

Step 1: Read State

state = omh_state(action="read", mode="ralph", instance_id="{instance_id}")
  • state.exists=false: Fresh start — go to Step 2 (Planning Gate).
  • state.data.active=true: Resume — go to Step 3.
  • state.data.phase="complete": Report completion. Ask if user wants fresh start.
  • state.data.phase="blocked": Report blockers. Ask if issues are resolved.
  • state.data.active=false, phase="cancelled": Report cancellation. Offer resume.

Check for cancel signal:

cancel = omh_state(action="cancel_check", mode="ralph", instance_id="{instance_id}")

If cancel.cancelled=true: set phase="cancelled", write state, exit.

Check staleness: if state.stale=true, warn the user and offer to continue or fresh start.

Increment state.data.iteration. If iteration > max_iterations (default 100): write phase="blocked", report "Max iterations reached", exit.

Step 2: Planning Gate

Ralph MUST NOT execute without a plan. Check sources in order:

  1. omh_state(action="check", mode="ralph-tasks", instance_id="{instance_id}") → exists=true — already parsed, skip to Step 3
  2. .omh/plans/ralplan-*.md — parse into ralph-tasks state: omh_state(action="write", mode="ralph-tasks", instance_id="{instance_id}", data={...})
  3. .omh/plans/ralph-plan.md — parse into ralph-tasks state
  4. Nothing found → tell user: "No plan found. Run omh-ralplan first."

Plan parsing rules:

  • Extract numbered tasks with titles, descriptions, and acceptance criteria
  • Reject generic criteria like "implementation is complete" — must be testable
  • Enforce atomicity: split multi-part tasks into separate entries
  • Assign priorities by dependency order (no-dependency tasks first)
  • Set all tasks to passes: false

Write initial state:

omh_state(action="write", mode="ralph", instance_id="{instance_id}", data={
  "active": true, "phase": "execute", "iteration": 0,
  "session_id": "<uuid>", "max_iterations": 100,
  "task_prompt": "<original user request>",
  "current_task_id": null,
  "started_at": "<ISO 8601 timestamp>",
  "project_path": "<absolute path to project root>",
  "files_modified": [],
  "error_history": [],
  "completed_task_learnings": []
})

Step 3: Pick Next Task

Read task list:

tasks = omh_state(action="read", mode="ralph-tasks", instance_id="{instance_id}")
  1. If ALL tasks have passes: true → go to Step 7 (Final Review)
  2. Find eligible tasks: passes=false AND all dependencies met
  3. If no eligible task but incomplete tasks remain → dependency deadlock → set phase="blocked", write state, exit
  4. Among eligible tasks, pick by priority (lowest number first)

Parallel execution: if 2-3 independent tasks are eligible (no shared file footprint, no dependency between them), batch them into one delegate_task call.

Step 4: Execute

Delegate to an executor subagent:

delegate_task(
    goal="[omh-role:executor] Implement this task:\n\n{task.title}\n{task.description}\n\nAcceptance Criteria:\n{task.acceptance_criteria}",
    context="Project Context:\n{tech stack, conventions, relevant paths}\n\nPrevious Feedback (if retry):\n{task.verifier_verdict}\n\nLearnings from prior tasks:\n{state.data.completed_task_learnings}"
)

The [omh-role:executor] marker in the goal causes the OMH plugin to automatically inject the executor role prompt into the subagent's system prompt — no inlining needed. (Fallback without plugin: replace marker with omh_state(action="load_role", role="executor") and pass the returned prompt text in context.)

Parse the executor's response: COMPLETE → Step 5, PARTIAL → Step 5, BLOCKED → record blocker, add discovered task if needed, update state, exit.

Step 5: Verify

Part A: Gather evidence

evidence = omh_gather_evidence(commands=[
    "{project build command}",
    "{project test command}",
    "{project lint command}"
])

Use the project's actual build/test/lint commands. The tool captures output, enforces timeouts, and returns {results, all_pass, summary}.

Part B: Delegate to verifier

delegate_task(
    goal="[omh-role:verifier] Verify whether this task's acceptance criteria are met:\n\n{task.title}\n{task.acceptance_criteria}\n\nExecutor Report:\n{task.executor_report}",
    context="Evidence:\n{evidence.results}"
)

Parse the verifier's response:

  • APPROVE / PASS: Set task.passes = true. Append to learnings: {task_id, summary, files_changed, gotchas}. Append to .omh/logs/ralph-progress.md.
  • REQUEST_CHANGES / FAIL: Record task.verifier_verdict. Check 3-strike rule (Step 6).

Step 6: Error Handling

3-Strike Circuit Breaker: Construct error fingerprint {task_id, category, error_key}. Add to task.error_fingerprints. If 3 fingerprints share the same category + error_key: mark task blocked, log the error, continue to next eligible task on next invocation. If ALL remaining tasks are blocked: write phase="blocked", report, exit.

Cancel detection (if user says "stop", "cancel", "abort"):

omh_state(action="cancel", mode="ralph", instance_id="{instance_id}", reason="user request")

Then write phase="cancelled" to state, exit with resume instructions.

Step 7: Final Review

When all tasks have passes: true:

evidence = omh_gather_evidence(commands=["{build command}", "{test command}"])

delegate_task(
    goal="[omh-role:architect] Review the complete implementation for architectural soundness.\n\nOriginal Plan:\n{source plan text}\n\nTasks Completed:\n{summary of all tasks + learnings}",
    context="Evidence:\n{evidence.results}\n\nFiles Changed Across All Tasks:\n{aggregate file list}"
)
  • APPROVE: Write {active: false, phase: "complete"}, clear state files, keep progress log.
  • REQUEST_CHANGES: Add new tasks with discovered: true, set phase="execute".

Step 8: Update State and Exit

After every action:

omh_state(action="write", mode="ralph", instance_id="{instance_id}", data={...updated state...})

Exit cleanly. The caller re-invokes for the next iteration.

Pitfalls

  • Never skip the planning gate. No plan = no execution.
  • Never trust executor claims without verifier evidence. The verifier must see omh_gather_evidence output.
  • Don't run evidence inside the verifier delegation. Gather evidence BEFORE delegating.
  • Don't conflate verifier and architect. Different jobs, different prompts, different phases.
  • Respect the 3-strike rule. Same error 3 times → surface the fundamental issue.
  • Feed learnings forward. Include completed_task_learnings in every executor delegation.
  • Use [omh-role:NAME] markers — the OMH plugin injects role prompts automatically into subagent sessions. Never inline role prompt text manually. Available roles: executor, verifier, architect, planner, critic, analyst, security-reviewer, code-reviewer, test-engineer, debugger. Fallback without plugin: omh_state(action="load_role", role="NAME") and pass returned prompt in context.

Sentinel Convention

Other skills detect ralph status:

  • omh_state(action="check", mode="ralph", instance_id="{instance_id}") → {exists, active, phase, stale}
  • phase="complete" → ralph finished successfully
  • phase="blocked" → ralph needs intervention

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 Ralph AI skill do?

execute plan: 1 task/call, verify evidence, iron law

Why use Omh Ralph on TypingMind?

Because you install it once and use it with any model. Omh 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 Omh Ralph 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-ralph. 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 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 Omh Ralph?

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

Is the Omh Ralph 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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