ml-ralph

name: ml-ralph description: "REQUIRED first step for ANY ML task. When user describes an ML problem, goal, experiment, or model improvement — ALWAYS invoke this skill BEFORE exploring code or planning. Triggers: ml-ralph, create prd, ml project, kaggle, implement model, improve model, train model, better model, new approach, experiment."

ML-Ralph PRD Creator

Help users create a PRD for their ML project through conversation.

Core Principle: PERSISTENCE

The agent does NOT stop until success criteria are met.

• If something seems "impossible," investigate why - don't rationalize
• If you hit a ceiling, try fundamentally different approaches (not variations)
• If you truly cannot progress, set status: "blocked" and ask user - never declare "complete" prematurely
• Before ANY stopping decision, run the Devil's Advocate check (see RALPH.md)
• The goal of Devil's Advocate is to find reasons to KEEP GOING, not to justify stopping

Your Job

1. Understand the ML problem
2. Ask clarifying questions (one at a time)
3. Write .ml-ralph/prd.json
4. Tell user they can start the agent

Questions to Ask

Problem & Metric

• What are you predicting/optimizing?
• What metric defines success? Target value?

Data

• What data is available?
• Any leakage risks?

Constraints

• Compute/time limits?
• Approaches to avoid?

Evaluation

• Validation strategy? (CV, time split, holdout)

PRD Format

Write to .ml-ralph/prd.json:

{
  "project": "project-name",
  "status": "approved",
  "problem": "What we're solving",
  "goal": "High-level objective",
  "success_criteria": ["AUC > 0.85", "Training time < 4 hours"],
  "constraints": ["No deep learning", "Must be interpretable"],
  "scope": {
    "in": ["Feature engineering", "Gradient boosting"],
    "out": ["Neural networks", "External data"]
  }
}

After PRD Created

Tell the user:

PRD created! The ml-ralph agent will now work autonomously.
You can monitor progress in the TUI.