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Auto Loop

Organization
claude-world
auto-loop

TDD-based autonomous development loop with checkpoint recovery and observability changelog

Overview

Publisherclaude-world
Repositorydirector-mode-lite
Skill nameauto-loop
Stars
80
Forks
10
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 claude-world on GitHub. Read the source before you install it.

Installation

Install the Auto Loop 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/claude-world/director-mode-lite/tree/main/skills/auto-loop
  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/claude-world/director-mode-lite.git /tmp/director-mode-lite
mkdir -p .claude/skills
cp -r /tmp/director-mode-lite/skills/auto-loop .claude/skills/auto-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auto Loop 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 Auto Loop 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 Auto Loop 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.

Auto-Loop

Execute a TDD-based autonomous development loop with full observability.


Usage

bash
# Start new task
/auto-loop "Implement user login"

# With acceptance criteria
/auto-loop "Implement authentication

Acceptance Criteria:
- [ ] Login form (email + password)
- [ ] JWT token generation
- [ ] Error handling
"

# Resume interrupted session
/auto-loop --resume

# Force restart (clear old state)
/auto-loop --force "New task"

# Check status
/auto-loop --status

# With iteration limit
/auto-loop "Task" --max-iterations 15

How It Works

┌───────────────────────────────────────────────────────────────┐
│                      TDD Iteration                            │
├───────────┬───────────────────────────────────────────────────┤
│  RED      │ Write failing test for next AC                    │
│           │ → Auto-logged: file_write, test_fail              │
├───────────┼───────────────────────────────────────────────────┤
│  GREEN    │ Write implementation to make test pass            │
│           │ → Auto-logged: file_write/edit, test_pass         │
├───────────┼───────────────────────────────────────────────────┤
│  REFACTOR │ Improve code quality (no behavior change)         │
│           │ → Use code-reviewer agent for suggestions         │
├───────────┼───────────────────────────────────────────────────┤
│  VALIDATE │ Run full test suite + linter                      │
│           │ → Auto-logged: test_pass/fail                     │
├───────────┼───────────────────────────────────────────────────┤
│  COMMIT   │ Commit changes with descriptive message           │
│           │ → Auto-logged: commit                             │
├───────────┼───────────────────────────────────────────────────┤
│  DECIDE   │ Check AC completion → continue or complete        │
└───────────┴───────────────────────────────────────────────────┘

Execution

When user runs /auto-loop "<request>":

1. State Detection (Conflict Prevention)

bash
STATE_DIR=".auto-loop"
CHECKPOINT="$STATE_DIR/checkpoint.json"

# Check for existing in-progress session
if [ -f "$CHECKPOINT" ]; then
    status=$(jq -r '.status // "unknown"' "$CHECKPOINT" 2>/dev/null || echo "unknown")
    iteration=$(jq -r '.current_iteration // 0' "$CHECKPOINT" 2>/dev/null || echo "0")

    if [ "$status" == "in_progress" ]; then
        echo "⚠️  Found interrupted session at iteration #$iteration"
        echo "Options:"
        echo "  /auto-loop --resume        → Continue"
        echo "  /auto-loop --force \"...\"  → Start fresh"
        exit 1
    fi
fi

Behavior Matrix:

Existing StateCommandAction
None/auto-loop "task"Start new
completed/auto-loop "task"Archive & start new
in_progress/auto-loop "task"Block - prompt user
in_progress/auto-loop --resumeContinue
in_progress/auto-loop --force "task"Archive & start new

2. Initialize

bash
# Archive old changelog if > 100 lines
CHANGELOG_DIR=".director-mode"
CHANGELOG="$CHANGELOG_DIR/changelog.jsonl"

if [ -f "$CHANGELOG" ] && [ $(wc -l < "$CHANGELOG") -gt 100 ]; then
    mv "$CHANGELOG" "$CHANGELOG_DIR/changelog.$(date +%Y%m%d_%H%M%S).jsonl"
fi

# Create state directories
mkdir -p "$STATE_DIR" "$CHANGELOG_DIR"

# Initialize checkpoint
# Build with jq so multiline requests or embedded double quotes stay valid JSON
# (a raw heredoc would break on the quoted acceptance-criteria usage above).
jq -n --arg req "$ARGUMENTS" \
  '{request: $req, current_iteration: 0, max_iterations: 20, status: "in_progress", started_at: (now | todate), acceptance_criteria: [], last_test_result: null, files_changed: []}' \
  > "$CHECKPOINT"

# acceptance_criteria stays empty here; the parse step below fills it in.

3. Parse Acceptance Criteria

Input:
  "Implement authentication

  Acceptance Criteria:
  - [ ] Login form
  - [ ] JWT token
  "

Parsed:
{
  "acceptance_criteria": [
    { "id": 1, "description": "Login form", "done": false },
    { "id": 2, "description": "JWT token", "done": false }
  ]
}

4. DECIDE - Completion Check

┌─────────────────────────────────────────────────────────────┐
│  DECIDE - Iteration #3                                      │
├─────────────────────────────────────────────────────────────┤
│  [x] 1. Login form            ← test passing               │
│  [x] 2. JWT token             ← test passing               │
│  [ ] 3. Error handling        ← NO TEST YET                │
├─────────────────────────────────────────────────────────────┤
│  Decision: 2/3 complete → CONTINUE                          │
└─────────────────────────────────────────────────────────────┘

Complete when:

  • All AC marked done: true
  • All tests passing

Stop when:

  • max_iterations reached
  • .auto-loop/stop file exists

Flags

FlagDescription
--resumeContinue interrupted session
--forceClear old state, start fresh
--statusShow current session status
--max-iterations NSet iteration limit (default: 20)

Observability

All events are automatically logged via PostToolUse hooks:

EventTriggerHook
file_writeWrite toollog-file-change.sh
file_editEdit toollog-file-change.sh
test_pass/failBash (test)log-bash-event.sh
commitBash (git commit)log-bash-event.sh

Query with /changelog:

bash
/changelog              # Recent events
/changelog --summary    # Statistics
/changelog --type test  # Filter by type

Stop / Resume

bash
# Interrupt (stop after current iteration)
touch .auto-loop/stop

# Check status
/auto-loop --status

# Resume
/auto-loop --resume

# Force restart
/auto-loop --force "New task"

Related

Frequently asked questions

What does the Auto Loop AI skill do?

TDD-based autonomous development loop with checkpoint recovery and observability changelog

Why use Auto Loop on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/claude-world/director-mode-lite/tree/main/skills/auto-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Auto Loop?

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 Auto Loop?

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

Is the Auto Loop AI skill free?

Yes. It is published on GitHub by claude-world 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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