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Autodev

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jh941213
autodev

Ralph Loop based autonomous development loop. A Stop Hook intercepts session termination and completes PRD items one by one with automatic commits. Triggers: "autodev", "autonomous development", "run overnight", "ralph loop", "auto development" Anti-triggers: "implement it yourself", "do it once", "manual"

Overview

Publisherjh941213
Repositorymy-cc-harness
Skill nameautodev
Stars
125
Forks
35
Bundled files
Instructions only
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 jh941213 on GitHub. Read the source before you install it.

Installation

Install the Autodev 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/jh941213/my-cc-harness.git /tmp/my-cc-harness
mkdir -p .claude/skills
cp -r /tmp/my-cc-harness/skills_en/autodev .claude/skills/autodev
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autodev 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 Autodev 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 Autodev 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.

AutoDev — Ralph Loop Autonomous Development

Stop Hook based autonomous development loop. Completes PRD/checklist items one by one with automatic commits. Leave it running overnight and a PR is waiting when you arrive at work.

Note: this is a separate mechanism from Claude Code's built-in /goal (session-scoped completion-condition loop). For a single simple completion condition use the built-in /goal; when you need PRD-based multi-item work, quality gates, or team-member reuse, use this skill (autodev).

Core Principle

Session start → Read PRD → Process next item → Commit → Session end
                                            Stop Hook detects
                                        Done? → Yes → Exit
                                          ↓ No
                                   continuation prompt (reason) → new session starts
                                              Read PRD → ...

Phase 0: Configuration Collection

Confirm with the user (only ask about missing items):

yaml
goal: "What to achieve"                # e.g., "Complete all items in PRD.md"
prd: "Path to PRD or checklist file"   # e.g., "PRD.md" or "tasks/todo.md"
scope: ["Modifiable file patterns"]    # e.g., ["src/**", "tests/**"]
verify: "Verification command"         # e.g., "npm test" (can be auto-detected)
max_iterations: 100                    # Maximum iterations (default 100) — do not set above 50 without user confirmation
completion_promise: "DONE"             # Completion signal (default "DONE")
mode: "continue"                       # continue | reset (default continue)

verify auto-detection

If the user did not provide verify:

  1. package.jsonnpm test or vitest run
  2. pyproject.tomlpytest
  3. Makefilemake test
  4. None → echo "no verify command"

Phase 1: Loop Initialization

bash
# 1. Create autodev branch
git checkout -b autodev/$(date +%Y%m%d-%H%M)

# 2. Create .ralph-loop/ state directory
mkdir -p .ralph-loop

# 3. Initialize state file
cat > .ralph-loop/state.json << 'STATE'
{
  "active": true,
  "iteration": 0,
  "max_iterations": {max_iterations},
  "prompt": "{goal}",
  "completion_promise": "{completion_promise}",
  "prd_path": "{prd}",
  "verify_command": "{verify}",
  "started_at": "{ISO time}",
  "status": "running"
}
STATE

# 4. Add .ralph-loop/ to .gitignore
echo ".ralph-loop/" >> .gitignore

# 5. Baseline verification
{verify} 2>&1 | tee .ralph-loop/baseline.log

Phase 2: Iteration Execution (every session)

Procedure performed in each session (iteration):

1. READ PRD
   - Read the {prd} file
   - Select the first incomplete item ([ ])

2. PLAN
   - Minimal change plan to implement the selected item
   - Only files within scope may be modified

3. IMPLEMENT
   - Modify code according to the plan
   - Never modify files outside scope

4. VERIFY
   - Run {verify}
   - On failure, attempt build-fix once
   - After 2 failures, roll back changes (git checkout -- .)

5. COMMIT
   - On success:
     git add -A
     git commit -m "[autodev] {item summary}"
   - Check off the item as [x] in the PRD

6. CHECK COMPLETION
   - Are there incomplete items left in the PRD?
   - Yes → End the session naturally (Stop Hook starts the next iteration)
   - No → All items complete!
     Output <promise>{completion_promise}</promise>
     → Stop Hook detects it and terminates the loop

Phase 3: Completion Report

When the loop ends (completed or max_iterations reached):

markdown
# AutoDev Completion Report

## Summary
- Total iterations: {N}
- Completed items: {K}/{total}
- Baseline → Final: verification passing
- Status: {completed | max_iterations_reached}

## Completed Items
| # | Item | Commit |
|---|------|--------|
| 1 | Implement API endpoint | abc1234 |
| 2 | Add authentication | def5678 |

## Incomplete Items (if any)
- [ ] Item N: reason

## Branch
autodev/{tag} — ready to merge into main

Safeguards

  1. No modifications outside scope: only modify files/directories specified in scope
  2. Protect existing tests: roll back changes when verify fails
  3. Crash recovery limit: build-fix only once. Skip the item after 2 failures
  4. Git safety: work only on the autodev/ branch. Never touch main
  5. max_iterations: prevents infinite loops (default 100. Do not set above 50 without user confirmation)
  6. Cost awareness: each iteration incurs token cost. Set the iteration count reasonably

Stop Hook Behavior

~/.claude/hooks/ralph-loop.sh runs on session termination:

  • If active in .ralph-loop/state.json is true, start the next iteration
  • Terminate the loop when <promise>DONE</promise> is detected in the transcript
  • Terminate the loop when iteration >= max_iterations
  • Do nothing if there is no state or active: false

Manual Control

bash
# Stop the loop
python3 -c "import json; s=json.load(open('.ralph-loop/state.json')); s['active']=False; json.dump(s,open('.ralph-loop/state.json','w'))"

# Check state
cat .ralph-loop/state.json

# Resume the loop
python3 -c "import json; s=json.load(open('.ralph-loop/state.json')); s['active']=True; json.dump(s,open('.ralph-loop/state.json','w'))"

Leveraging Existing Skills

SituationSkill to use
Recovery on build failurebuild-fix
Code cleanup after commitsimplify
Test-driven implementationtdd
Item implementation planningplan
Final verificationverify

Frequently asked questions

What does the Autodev AI skill do?

Ralph Loop based autonomous development loop. A Stop Hook intercepts session termination and completes PRD items one by one with automatic commits. Triggers: "autodev", "autonomous development", "run overnight", "ralph loop", "auto development" Anti-triggers: "implement it yourself", "do it once", "manual"

Why use Autodev on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jh941213/my-cc-harness/tree/main/skills_en/autodev. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Autodev?

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 Autodev?

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

Is the Autodev AI skill free?

It is published on GitHub by jh941213. Check the repository for licensing terms. 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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