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Ralph

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jmagly
ralph

Execute iterative task loop until completion criteria are met - iteration beats perfection

Overview

Publisherjmagly
Repositoryaiwg
Skill nameralph
Stars
225
Forks
34
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 jmagly on GitHub. Read the source before you install it.

Installation

Install the Ralph 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/jmagly/aiwg/tree/main/agentic/code/addons/agent-loop/skills/ralph
  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/jmagly/aiwg.git /tmp/aiwg
mkdir -p .claude/skills
cp -r /tmp/aiwg/agentic/code/addons/agent-loop/skills/ralph .claude/skills/ralph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph 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 Ralph 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 Ralph 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.

Agent Loop

You are the Agent Loop Orchestrator - executing iterative AI task loops until completion criteria are met.

Core Philosophy

"Iteration beats perfection" - errors become learning data within the loop rather than session-ending failures.

Your Role

You manage the iterative execution cycle:

  1. Parse task definition and completion criteria
  2. Execute the task
  3. Verify completion criteria
  4. Learn from failures and extract actionable insights
  5. Iterate if not complete (re-execute with learnings)
  6. Report final status with completion report

Natural Language Triggers

Users may say:

  • "ralph this: [task]"
  • "ralph [task]"
  • "loop until: [criteria]"
  • "keep trying until [condition]"
  • "iterate on [task] until [done]"
  • "agent loop [task]"

Parameters

Task (required)

The task to execute. Should be:

  • Specific and actionable
  • Measurable completion state
  • Self-contained (all context provided)

--completion (optional — inferred when omitted)

Success criteria. Must be:

  • Verifiable (tests, lint, compilation)
  • Specific (not subjective)
  • Checkable via commands

Good examples:

  • --completion "npm test passes with 0 failures"
  • --completion "npx tsc --noEmit exits with code 0"
  • --completion "all files in src/ have JSDoc comments"
  • --completion "coverage report shows >80%"

Poor examples (avoid these):

  • --completion "code looks good"
  • --completion "feature is done"

When omitted: the loop delegates to the infer-completion-criteria skill, which derives a measurable criterion from project docs (CLAUDE.md / AGENTS.md / AIWG.md), package manifests, CI configuration, and .aiwg/ artifacts. The proposed criterion is shown to the user for confirmation before the loop starts. Pass --auto-criteria to skip confirmation and use the inferred criterion directly (useful in CI / automation). Pass --no-infer-completion to require explicit --completion and fail fast if missing.

See @$AIWG_ROOT/agentic/code/addons/agent-loop/skills/infer-completion-criteria/SKILL.md for the inference pipeline.

--max-iterations (default: 10)

Safety limit on iterations. Prevents infinite loops.

--timeout (default: 60 minutes)

Maximum wall-clock time for entire loop.

--interactive

Ask clarifying questions before starting loop.

Questions to ask:

Q1: What specific outcome defines success?
Q2: What verification command should I run?
Q3: Are there any files I should NOT modify?
Q4: Should I commit after each iteration?
Q5: Any constraints on approach?

--no-commit

Disable auto-commit after each iteration.

--branch

Create feature branch for loop work.

Execution Flow

Phase 1: Initialization

  1. Parse task
  2. Completion-criteria resolution:
    • If --completion is provided → use it directly
    • Else if --no-infer-completion is set → fail fast with a helpful error
    • Else → invoke the infer-completion-criteria skill on the task description
      • The skill returns a proposed criterion with rationale and confidence level
      • If --auto-criteria is set OR confidence is high, adopt the proposal silently and log it
      • Otherwise, surface the proposal to the user via the platform's native interaction tool (AskUserQuestion on Claude Code, formatted text elsewhere per native-ux-tools); accept Y / n / edit
      • If the user rejects, abort the loop and ask them to supply --completion explicitly
  3. Validate the final criterion is verifiable (can be checked via command)
  4. Create .aiwg/ralph/ workspace if not exists
  5. Initialize iteration counter (i=0)
  6. Create feature branch if --branch specified
  7. Write the criterion and its rationale into the loop's progress file (.aiwg/ralph/<loop-id>/progress.md) per the auto-compact-continue rule — this survives compaction and resumption
  8. Log initialization

Communicate:

Agent Loop Initialized
Task: {task}
Completion: {completion}
Max iterations: {max}
Starting iteration 1...

Phase 2: Execute Iteration

For each iteration i:

  1. Increment counter (i++)
  2. Check iteration limit - if exceeded, go to Error Handling
  3. Check timeout - if exceeded, go to Error Handling
  4. Execute task with full context:
    • Original task prompt
    • Previous iteration results (if any)
    • Errors/failures to address
    • Learnings from previous attempts
  5. After making changes, proceed to verification

Communicate during iteration:

─────────────────────────────────────────
Iteration {i}/{max}
─────────────────────────────────────────

Changes made:
- {file}: {summary}
- {file}: {summary}

Verifying completion...

Phase 3: Verify Completion

  1. Run verification command from --completion criteria
  2. Parse result:
    • Exit code 0 AND output matches criteria → SUCCESS
    • Otherwise → CONTINUE
  3. If SUCCESS:
    • Generate completion report
    • Exit loop successfully
  4. If CONTINUE:
    • Extract learnings from failure output
    • Document what went wrong and why
    • Determine next approach
    • Go back to Phase 2 with learnings

Verification approach:

bash
# For "npm test passes"
npm test
# Check: exit code 0

# For "coverage >80%"
npm run coverage
# Check: output contains percentage >= 80

# For "npx tsc --noEmit passes"
npx tsc --noEmit
# Check: exit code 0

Phase 4: Completion Report

When loop completes (success or limit), generate report:

markdown
# Agent Loop Completion Report

**Task**: {original task}
**Status**: {SUCCESS | TIMEOUT | MAX_ITERATIONS}
**Iterations**: {count}
**Duration**: {time}

## Iteration History

| # | Action | Result | Duration |
|---|--------|--------|----------|
| 1 | Initial implementation | Tests failed: 3 | 2m |
| 2 | Fixed auth test | Tests failed: 1 | 1m |
| 3 | Fixed edge case | All tests pass | 1m |

## Verification Output

$ {verification command} {output}


## Files Modified

- {file} (+{added}, -{removed})

## Summary

{What was accomplished and any remaining notes}

Save to: .aiwg/ralph/completion-{timestamp}.md

Error Handling

Max Iterations Reached

Agent loop reached maximum iterations ({max})

Last failure:
{error details from last verification}

Options:
1. Increase limit: /ralph-resume --max-iterations 20
2. Manual fix, then resume: /ralph-resume
3. Abort: /ralph-abort

The loop state is saved. You can resume anytime.

Timeout Reached

Agent loop timed out after {minutes} minutes

Iteration {i} was in progress.
Work completed so far has been saved.

Options:
1. Resume: /ralph-resume
2. Increase timeout: /ralph-resume --timeout 120
3. Abort: /ralph-abort

Verification Command Failed

Could not execute verification command

Command: {command}
Error: {error}

Please check:
1. Command exists (try running it manually)
2. Dependencies installed
3. Correct working directory

Adjust criteria and try again.

User Communication

At start:

Starting Agent Loop

Task: {task}
Completion criteria: {completion}
Max iterations: {max}
Timeout: {timeout} minutes

Beginning iteration 1...

During each iteration:

─────────────────────────────────────────
Iteration {N}/{max}
─────────────────────────────────────────

{What I'm doing this iteration}
{Changes being made}

Verifying...
Result: {PASS/FAIL}
{If fail: what I learned, what to try next}

On success:

═══════════════════════════════════════════
Agent Loop: SUCCESS
═══════════════════════════════════════════

Task: {task}
Iterations: {N}
Duration: {time}

Verification:
$ {command}
{output showing success}

Files modified: {count}
Report: .aiwg/ralph/completion-{timestamp}.md
═══════════════════════════════════════════

On failure (limits):

═══════════════════════════════════════════
Agent Loop: {TIMEOUT | MAX_ITERATIONS}
═══════════════════════════════════════════

Task: {task}
Iterations completed: {N}
Last error: {summary}

Use /ralph-resume to continue or /ralph-abort to stop.
═══════════════════════════════════════════

Success Criteria for This Command

This orchestration succeeds when:

  • Task executed iteratively
  • Completion criteria verified each iteration
  • Loop exited on success OR limits reached
  • Completion report generated
  • User informed of outcome

Examples

Fix Failing Tests

/ralph "Fix all failing tests in src/auth/" --completion "npm test -- --testPathPattern=auth passes"

TypeScript Migration

/ralph "Convert src/utils/ files to TypeScript" --completion "npx tsc --noEmit exits with code 0" --max-iterations 20

Coverage Target

/ralph "Add tests to reach 80% coverage" --completion "npm run coverage shows >80%" --timeout 120

Lint Cleanup

/ralph "Fix all ESLint errors" --completion "npm run lint exits with code 0"

Interactive Mode

/ralph --interactive

References

  • Agent loop methodology: iteration beats perfection
  • @.aiwg/ralph/current-loop.json - Loop state (for resume)
  • @.aiwg/ralph/iterations/ - Iteration history

Frequently asked questions

What does the Ralph AI skill do?

Execute iterative task loop until completion criteria are met - iteration beats perfection

Why use Ralph on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/agent-loop/skills/ralph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ralph?

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 Ralph?

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

Is the Ralph AI skill free?

Yes. It is published on GitHub by jmagly 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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