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Autodev Parallel

Community
jh941213
autodev-parallel

Parallel version of the Ralph Loop. Multiple agents process PRD items simultaneously in worktrees. Triggers: "parallel experiments", "autodev parallel", "simultaneous experiments", "worktree experiments", "parallel ralph" Anti-triggers: "sequential experiments", "one at a time"

Overview

Publisherjh941213
Repositorymy-cc-harness
Skill nameautodev-parallel
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 Parallel 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-parallel .claude/skills/autodev-parallel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autodev Parallel 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 Parallel 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 Parallel 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 Parallel — Ralph Loop Parallel Orchestrator

Multiple agents process PRD items in parallel within worktree-isolated environments. Maximizes completion speed by working on independent items simultaneously.

Core Concept

main (or current branch)
 ├── worktree A ── Agent 1: PRD item 1
 ├── worktree B ── Agent 2: PRD item 2
 ├── worktree C ── Agent 3: PRD item 3
 └── Orchestrator (this skill)
      - Classify & assign independent items
      - Collect & verify results
      - Integrate via cherry-pick
      - Repeat for next round

Phase 0: Configuration Collection

yaml
goal: "What to achieve"
prd: "Path to PRD or checklist file"    # e.g., "PRD.md"
scope: ["Modifiable file patterns"]
verify: "Verification command"
parallel: 3                            # Number of concurrent agents (default 3)
rounds: 5                             # Number of rounds (default 5)
max_iterations: 100                    # Stop Hook maximum iterations (default 100)
completion_promise: "DONE"

Phase 1: Item Classification

Read the PRD and analyze item dependencies:

markdown
## Independent items (parallelizable)
- [ ] Item A: API endpoint — src/api/
- [ ] Item B: UI component — src/components/
- [ ] Item C: Write tests — tests/

## Dependent items (require sequencing)
- [ ] Item D: after A completes → integration tests

Phase 2: Round Loop

for round in 1..rounds:

  1. SELECT
     - Pick up to {parallel} independent items among the incomplete ones
     - Items with dependencies are selected only after their prerequisites complete

  2. LAUNCH (parallel)
     - Invoke {parallel} Agents simultaneously
     - Each Agent is isolated with isolation: "worktree"
     - Prompt passed to each Agent:

     """
     Implement the PRD item.

     Item: {specific_item}
     Scope: {scope}
     Verify: {verify}

     Procedure:
     1. Read files within scope and implement the item
     2. Verify by running {verify}
     3. On failure, attempt build-fix once
     4. On success, git commit -m "[autodev] {item_summary}"
     5. Return the result as the final message:
        AUTODEV_RESULT: status={success|fail}, commit={hash}, item="{desc}"
     """

  3. COLLECT
     - Wait for each Agent to complete
     - Parse results (status, commit hash)

  4. INTEGRATE
     - Cherry-pick the changes of successful Agents
     - On cherry-pick conflict:
       - Attempt conflict resolution (once)
       - On failure, defer the item to the next round
     - Check off completed items as [x] in the PRD

  5. VERIFY ALL
     - Full verification after integration: {verify}
     - On failure, revert the last cherry-pick and defer the item

  6. REPORT (per round)
     Round {round}/{rounds} complete:
     - Agent 1: {status} — {item}
     - Agent 2: {status} — {item}
     - Remaining incomplete items: {remaining}

  7. CHECK COMPLETION
     - All items complete? → <promise>DONE</promise>
     - Otherwise → next round

Phase 3: Completion Report

markdown
# AutoDev Parallel Completion Report

## Summary
- Total rounds: {rounds}
- Total items: {total} (completed: {done}, failed: {failed})
- Parallel agents: {parallel}

## Results by Round
| Round | Completed items | Failed | Cumulative completion |
|-------|-----------------|--------|-----------------------|
| 1 | A, B | - | 2/10 (20%) |
| 2 | C, D, E | F | 5/10 (50%) |

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

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

Parallelism Guidelines

SituationRecommended parallel
Independent files/modules5 (maximum)
Changes within the same file1-2 (conflict risk)
Includes performance benchmarks2-3 (shared resources)
Test-only judgment3-5

Safeguards

  1. Worktree isolation: use the Agent tool's isolation: "worktree"
  2. Cherry-pick only: force push is strictly forbidden
  3. Protect existing tests: roll back when full verification fails after integration
  4. Respect dependencies: process dependent items only after prerequisites complete
  5. Synchronize between rounds: start the next round only after the previous round's integration completes
  6. max_iterations: Stop Hook safety mechanism
  7. TTH mutual exclusion: do not use simultaneously with TTH (/tth) (Stop hook loop conflict)

TTH Team Member Utilization (optional)

yaml
# Assign specialty areas per team member
pichai: architecture changes, module separation
jensen: API optimization, DB query improvements
zuckerberg: frontend components
bezos: code deletion, removing unnecessary abstractions

Include the team role file in the prompt to grant expertise:

Read the team member's role file and implement from that perspective:
~/.claude/team-roles/{role}.md

Frequently asked questions

What does the Autodev Parallel AI skill do?

Parallel version of the Ralph Loop. Multiple agents process PRD items simultaneously in worktrees. Triggers: "parallel experiments", "autodev parallel", "simultaneous experiments", "worktree experiments", "parallel ralph" Anti-triggers: "sequential experiments", "one at a time"

Why use Autodev Parallel on TypingMind?

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

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

Which AI models can use Autodev Parallel?

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 Parallel?

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

Is the Autodev Parallel 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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