Autonomous Loop

Q: Why use Autonomous Loop on TypingMind?

Because you install it once and use it with any model. Autonomous 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.

Q: How do I install Autonomous Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WellApp-ai/Well/tree/main/cursor-rules/skills/autonomous-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Q: Which AI models can use Autonomous 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.

Q: How many AI models can I use with Autonomous Loop?

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

Q: Is the Autonomous Loop AI skill free?

Yes. It is published on GitHub by WellApp-ai under the MIT license. You only pay your own AI provider for the tokens you use.

Q: 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.

Q: 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.

Organization

WellApp-ai

autonomous-loop

Iterate until success or limit, composing existing skills with Jidoka integration

Overview

Publisher	WellApp-ai
Repository	`Well`
Skill name	`autonomous-loop`
Stars	342
Forks	48
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 WellApp-ai on GitHub. Read the source before you install it.

Installation

Install the Autonomous Loop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/WellApp-ai/Well.git /tmp/Well
mkdir -p .claude/skills
cp -r /tmp/Well/cursor-rules/skills/autonomous-loop .claude/skills/autonomous-loop

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autonomous 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 Autonomous 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 Autonomous 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.

Autonomous Loop Skill (Ralph Wiggum)

Keep iterating until success without human intervention per attempt. Composes existing skills and respects Jidoka escalation.

When to Use

User requests hands-off iteration
Explicitly triggered with keywords

Trigger Phrases

"ralph mode"
"keep going"
"iterate until done"
"autonomous"
"don't stop"

Parameters

Param	Default	Description
max_iterations	5	Max attempts before pause
success_criteria	typecheck + lint + ReadLints clean	What defines success

Phase 1: Initialize

Acknowledge activation
Invoke session-status skill for current state
Initialize counters:
- iteration = 0
- error_history = [] (shared with debug skill)

Output:


Entering autonomous loop. Max 5 iterations.
Current: [session-status header]

Phase 2: Execute Loop


FOR iteration IN 1..max_iterations:
    
    2.1: Execute current task action
         - Implement changes per commit plan
         - Run npm run typecheck
         - Run npm run lint
    
    2.2: Invoke pr-review skill
         - If BLOCK: proceed to 2.4
         - If PASS: proceed to 2.3
    
    2.3: Invoke qa-commit skill
         - If GREEN: proceed to 2.5
         - If RED: proceed to 2.4
    
    2.4: Invoke debug skill
         - Debug skill checks Jidoka tier internally
         - If Tier 2/3 escalation: EXIT loop, return JIDOKA
         - If Tier 1 fix attempted: CONTINUE loop
    
    2.5: Success checkpoint
         - Log iteration as SUCCESS
         - Proceed to next commit or EXIT if done

Phase 3: Iteration Report

After each iteration, output:

markdown
## Iteration [N]/[max]

| Step | Skill | Result |
|------|-------|--------|
| Execute | (implementation) | [files changed] |
| Review | pr-review | PASS/BLOCK |
| Verify | qa-commit | GREEN/RED |
| Debug | debug | [if invoked] |

**Outcome:** [SUCCESS / CONTINUE / JIDOKA / MAX]

Phase 4: Exit and Report

On loop exit, invoke session-status skill and output:

markdown
## Autonomous Loop Complete

**Exit reason:** [SUCCESS / JIDOKA / MAX / INTERRUPT]
**Iterations:** [N] of [max]

| Iter | Action | Result |
|------|--------|--------|
| 1 | [description] | [outcome] |
| 2 | [description] | [outcome] |

[session-status footer with next steps]

Exit Conditions

Condition	Trigger	Result
SUCCESS	All checks pass, commit complete	Normal exit, proceed to next commit
JIDOKA	Tier 2/3 escalation triggered	Exit loop, present options to human
MAX	iteration >= max_iterations	Exit loop, report and wait for guidance
INTERRUPT	User sends message	Exit loop, respond to user

Safety Guardrails

NEVER auto-commit without GREEN from qa-commit
NEVER auto-push to remote
NEVER delete files without explicit approval
PAUSE on any destructive operation
EXIT on user message (treat as INTERRUPT)

Integration with Jidoka

Ralph Wiggum respects Jidoka escalation tiers:

Tier	Ralph Behavior
Tier 1 (error_count < 3)	Continue autonomously, debug skill handles
Tier 2 (same error 3x)	EXIT loop, Jidoka presents options
Tier 3 (any error 5x)	EXIT loop, Jidoka requires human triage

Integration

This skill composes:

session-status - Progress tracking
pr-review - Technical validation
qa-commit - QA contract verification
debug - Error fixing with Jidoka

This skill is invoked by:

User trigger phrases
agent.mdc when user requests autonomous mode

Invocation

Trigger with: "ralph mode", "keep going", "iterate until done"

Tools Used

Tool	Purpose
Shell	npm run typecheck, npm run lint
ReadLints	IDE diagnostic errors
Grep	Pattern search in code
SemanticSearch	Find implementations
Read	Read file contents
StrReplace	Make code changes
Browser MCP	Visual verification (via qa-commit)

Frequently asked questions

What does the Autonomous Loop AI skill do?

Iterate until success or limit, composing existing skills with Jidoka integration

Why use Autonomous Loop on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WellApp-ai/Well/tree/main/cursor-rules/skills/autonomous-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Autonomous 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 Autonomous Loop?

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

Is the Autonomous Loop AI skill free?

Yes. It is published on GitHub by WellApp-ai 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Autonomous Loop Skill (Ralph Wiggum)

When to Use

Trigger Phrases

Parameters

Phase 1: Initialize

Phase 2: Execute Loop

Phase 3: Iteration Report

Phase 4: Exit and Report

Exit Conditions

Safety Guardrails

Integration with Jidoka

Integration

Invocation

Tools Used

Frequently asked questions

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