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Autopilot

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Yeachan-Heo
autopilot

Full autonomous execution from idea to working code

Overview

PublisherYeachan-Heo
Repositoryoh-my-claudecode
Skill nameautopilot
Stars
39.2K
Forks
3.5K
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 Yeachan-Heo on GitHub. Read the source before you install it.

Installation

Install the Autopilot 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/Yeachan-Heo/oh-my-claudecode.git /tmp/oh-my-claudecode
mkdir -p .claude/skills
cp -r /tmp/oh-my-claudecode/skills/autopilot .claude/skills/autopilot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autopilot 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 Autopilot 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 Autopilot 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.

<Use_When>

  • User wants end-to-end autonomous execution from an idea to working code
  • User says "autopilot", "auto pilot", "autonomous", "build me", "create me", "make me", "full auto", "handle it all", or "I want a/an..."
  • Task requires multiple phases: planning, coding, testing, and validation
  • User wants hands-off execution and is willing to let the system run to completion </Use_When>

<Do_Not_Use_When>

  • User wants to explore options or brainstorm -- use plan skill instead
  • User says "just explain", "draft only", or "what would you suggest" -- respond conversationally
  • User wants a single focused code change -- use ralph or delegate to an executor agent
  • User wants to review or critique an existing plan -- use plan --review
  • Task is a quick fix or small bug -- use direct executor delegation </Do_Not_Use_When>

<Why_This_Exists> Most non-trivial software tasks require coordinated phases: understanding requirements, designing a solution, implementing in parallel, testing, and validating quality. Autopilot orchestrates all of these phases automatically so the user can describe what they want and receive working code without managing each step. </Why_This_Exists>

<Execution_Policy>

  • Each phase must complete before the next begins
  • Parallel execution is used within phases where possible (Phase 2 and Phase 4)
  • QA cycles repeat up to 5 times; if the same error persists 3 times, stop and report the fundamental issue
  • Validation requires approval from all reviewers; rejected items get fixed and re-validated
  • Cancel with /oh-my-claudecode:cancel at any time; progress is preserved for resume </Execution_Policy>

<Workflow_Profiles>

Named stage profiles (v1)

Select a configured profile only with /autopilot --workflow <name> <task>. A profile is an autopilot-owned stage schedule, not a command, mode, plugin, filename, or separate state identity. Without --workflow, autopilot retains its legacy lifecycle and behavior.

Named workflow profiles require Linux with the flock utility in v1 because their transcript evidence boundary uses Linux no-follow file-descriptor traversal and their recoverable mutation lock uses kernel advisory locking. Unsupported environments reject explicit --workflow activation before state mutation; use legacy autopilot instead.

Profiles are configured in project or user JSONC as autopilot.workflows.<slug>. Every v1 profile has exactly version: 1 and stages; no other profile keys are accepted. The only admitted stage sequences are:

jsonc
{
  "autopilot": {
    "workflows": {
      "plan-build-qa": {
        "version": 1,
        "stages": ["ralplan", "execution", "qa"]
      }
    }
  }
}
text
[ralplan, execution]
[ralplan, execution, ralph]
[ralplan, execution, qa]
[ralplan, execution, ralph, qa]

ralplan creates the plan consumed by execution; execution creates the implemented workspace required by ralph and qa. Thus omitted or reordered prerequisites, duplicate stages, and non-built-in stages are invalid. Profile names use ^[a-z][a-z0-9-]{0,62}$, are validated metadata only, and cannot collide with built-in stages, autopilot/mode names, or deprecated aliases.

User and project configuration sources are each validated before composition. Different names coexist; a project profile with the same name replaces the complete user profile rather than deep-merging it. Environment configuration cannot define or replace profiles.

On successful selection, autopilot atomically creates its existing session-scoped state with an immutable normalized descriptor and selected-only pipeline tracking. The descriptor contains the workflow name, profile version, canonical stages, and a deterministic SHA-256 profile hash; it excludes task text and mutable progress. Resume and Stop verify that hash and refuse a mismatch without reloading configuration or emitting a stage prompt. Cancel, resume, cleanup, state inspection, HUD, and Stop continuation remain owned by autopilot.

The installed plugin and standalone-installed Stop hooks advance only after an authorized assistant completion record for the active stage appears after that stage's persisted activation transcript boundary. They bind evidence to the owner session and bounded, non-symlink transcript; reject user/tool/local-command output and stale or wrong-stage evidence; and use compare-before-write tracking updates so duplicate or concurrent Stop events advance exactly once. Public state, HUD, and Stop output show only safe workflow metadata and progress, never the task, descriptor internals, transcript references, offsets, or record hashes.

V1 deferrals

V1 does not support stageModels, model routing, provider or role selection; inline/no-spawn execution; dynamic commands, modes, or state files; arbitrary stages, prompts, plugins, branches, loops, DAGs, or callbacks; or environment-defined profile definitions. The separate custom-skill inline-array frontmatter parser mismatch is also deferred. </Workflow_Profiles>

  1. Phase 1 - Planning: Create an implementation plan from the spec

    • If ralplan consensus plan exists: Skip — already done in the 3-stage pipeline
    • Architect (Opus): Create plan (direct mode, no interview)
    • Critic (Opus): Validate plan
    • Output: .omc/plans/autopilot-impl.md
  2. Phase 2 - Execution: Implement the plan using executor agents with Ralph persistence when needed

    • Executor (Haiku): Simple tasks
    • Executor (Sonnet): Standard tasks
    • Executor (Opus): Complex tasks
    • Run independent tasks in parallel
  3. Phase 3 - QA: Cycle until all tests pass

    • Build, lint, test, fix failures
    • Repeat up to 5 cycles
    • Stop early if the same error repeats 3 times (indicates a fundamental issue)
  4. Phase 4 - Validation: Multi-perspective review in parallel

    • Architect: Functional completeness
    • Security-reviewer: Vulnerability check
    • Code-reviewer: Quality review
    • All must approve; fix and re-validate on rejection
  5. Phase 5 - Cleanup: Delete all state files on successful completion

    • Remove .omc/state/autopilot-state.json, ralph-state.json (plus stale retired ultraqa-state.json/ultrawork-state.json if legacy copies exist)
    • Run /oh-my-claudecode:cancel for clean exit

<Tool_Usage>

  • Use Task(subagent_type="oh-my-claudecode:architect", ...) for Phase 4 architecture validation
  • Use Task(subagent_type="oh-my-claudecode:security-reviewer", ...) for Phase 4 security review
  • Use Task(subagent_type="oh-my-claudecode:code-reviewer", ...) for Phase 4 quality review
  • Agents form their own analysis and return it; the LEAD then spawns any cross-validation agents itself. Do not rely on a subagent spawning further subagents without checking the Claude Code depth setting: Claude Code 2.1.217–2.1.218 defaulted CLAUDE_CODE_MAX_SUBAGENT_SPAWN_DEPTH to 1, while 2.1.219+ defaults to 3. Keep cross-validation at the LEAD level unless nested delegation is deliberate and supported by the active runtime.
  • Never block on external tools; proceed with available agents if delegation fails </Tool_Usage>

<Escalation_And_Stop_Conditions>

  • Stop and report when the same QA error persists across 3 cycles (fundamental issue requiring human input)
  • Stop and report when validation keeps failing after 3 re-validation rounds
  • Stop when the user says "stop", "cancel", or "abort"
  • If requirements were too vague and expansion produces an unclear spec, offer redirect to /deep-interview for Socratic clarification, or pause and ask the user for clarification before proceeding </Escalation_And_Stop_Conditions>

<Final_Checklist>

  • All 5 phases completed (Expansion, Planning, Execution, QA, Validation)
  • All validators approved in Phase 4
  • Tests pass (verified with fresh test run output)
  • Build succeeds (verified with fresh build output)
  • State files cleaned up
  • User informed of completion with summary of what was built </Final_Checklist>

Parallel session caveats

  • Multi-repo workspace anchor: drop a .omc-workspace marker at the parent directory so multiple sessions across sub-repos share one .omc/. Resolution order: OMC_STATE_DIR > .omc-workspace > git > cwd. See docs/REFERENCE.md.
  • Session id source: OMC_SESSION_ID env var wins in CLI contexts; hook payload data.session_id wins in hook contexts.
  • Plan id (when applicable): Autopilot state is session-scoped. Two autopilots in the same workspace require distinct session IDs.
  • Parallel verdict: supported (session-scoped state)

Optional settings in .claude/omc.jsonc (project) or ~/.config/claude-omc/config.jsonc (user):

jsonc
{
  "autopilot": {
    "maxIterations": 10,
    "maxQaCycles": 5,
    "maxValidationRounds": 3,
    "pauseAfterExpansion": false,
    "pauseAfterPlanning": false,
    "skipQa": false,
    "skipValidation": false,
    "execution": "solo"
  }
}

To run autopilot implementation through the tmux CLI team runtime and prefer Cursor executor workers:

jsonc
{
  "autopilot": {
    "execution": "team",
    "team": { "agentTypes": ["cursor"] }
  }
}

With that config, the execution stage must launch executor-style work through:

sh
omc team 1:cursor "<implementation task>"

or the Claude Code slash compatibility surface:

text
/omc-teams 1:cursor "<implementation task>"

Limitations:

  • Cursor workers support implementation and reviewer-style team roles. critic, code-reviewer, security-reviewer, and test-engineer workers must emit the structured verdict file consumed by the team leader; final approval remains a lead-session responsibility.
  • Cursor requires the cursor-agent CLI to be installed and authenticated. If cursor-agent is unavailable, report that setup requirement instead of silently falling back to Claude-only execution.

Resume

If autopilot was cancelled or failed, run /oh-my-claudecode:autopilot again to resume from where it stopped.

Best Practices for Input

  1. Be specific about the domain -- "bookstore" not "store"
  2. Mention key features -- "with CRUD", "with authentication"
  3. Specify constraints -- "using TypeScript", "with PostgreSQL"
  4. Let it run -- avoid interrupting unless truly needed

Troubleshooting

Stuck in a phase? Check TODO list for blocked tasks, review .omc/autopilot-state.json, or cancel and resume.

QA cycles exhausted? The same error 3 times indicates a fundamental issue. Review the error pattern; manual intervention may be needed.

Validation keeps failing? Review the specific issues. Requirements may have been too vague -- cancel and provide more detail.

Deep Interview Integration

When autopilot is invoked with a vague input, Phase 0 can redirect to /deep-interview for Socratic clarification:

User: "autopilot build me something cool"
Autopilot: "Your request is open-ended. Would you like to run a deep interview first?"
  [Yes, interview first (Recommended)] [No, expand directly]

If a deep-interview spec already exists at .omc/specs/deep-interview-*.md, autopilot uses it directly as Phase 0 output (the spec has already been mathematically validated for clarity).

3-Stage Pipeline: deep-interview → ralplan → autopilot

The recommended full pipeline chains three quality gates:

/deep-interview "vague idea"
  → Socratic Q&A → spec (ambiguity ≤ 20%)
  → /ralplan --direct → consensus plan (Planner/Architect/Critic approved)
  → /autopilot → skips Phase 0+1, starts at Phase 2 (Execution)

When autopilot detects a ralplan consensus plan (.omc/plans/ralplan-*.md or .omc/plans/consensus-*.md), it skips both Phase 0 (Expansion) and Phase 1 (Planning) because the plan has already been:

  • Requirements-validated (deep-interview ambiguity gate)
  • Architecture-reviewed (ralplan Architect agent)
  • Quality-checked (ralplan Critic agent)

Autopilot starts directly at Phase 2 using executor agents and Ralph persistence.

Frequently asked questions

What does the Autopilot AI skill do?

Full autonomous execution from idea to working code

Why use Autopilot on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yeachan-Heo/oh-my-claudecode/tree/main/skills/autopilot. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Autopilot?

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 Autopilot?

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

Is the Autopilot AI skill free?

Yes. It is published on GitHub by Yeachan-Heo 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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