ML-Ralph PRD Converter

Converts ML PRDs into the prd.json format used by ML-Ralph.

The Job

Take a PRD (markdown or text) and convert it to prd.json in the ML-Ralph directory.

Output Format

{
  "project": "[Project Name]",
  "branchName": "ml-ralph/[feature-name-kebab-case]",
  "description": "[Short description]",
  "userStories": [
    {
      "id": "US-001",
      "title": "[Story title]",
      "description": "As a [role], I want [outcome] so that [benefit].",
      "type": "discovery | experiment | evaluation | implementation | ops",
      "hypothesis": "[Optional hypothesis]",
      "evidenceRequired": "[Required evidence to log]",
      "acceptanceCriteria": [
        "Criterion 1",
        "Criterion 2",
        "Ruff check passes",
        "Ruff format passes",
        "Mypy passes",
        "Pytest passes (if tests exist)",
        "Evidence logged in progress.jsonl"
      ],
      "priority": 1,
      "passes": false,
      "notes": "",
      "supersededBy": "",
      "risk": ""
    }
  ]
}

Story Size: The Number One Rule

Each story must be completable in one iteration. If you cannot describe the change in 2-3 sentences, split it.

Story Ordering: Dependencies First

Order stories so earlier ones unlock later ones:

1. •Discovery/evaluation scaffolding
2. •Baseline experiments
3. •Improvements and analysis
4. •Operationalization

Acceptance Criteria Rules

• •Criteria must be verifiable.
• •Always include Ruff/Mypy/Pytest checks and evidence logging.
• •For experiment stories, include the specific metric and logging requirement, including W&B run URL/ID.

Conversion Rules

1. •Each user story becomes one JSON entry
2. •IDs sequential: US-001, US-002, ...
3. •priority orders execution
4. •passes: false for all
5. •supersededBy empty string for all
6. •branchName must start with ml-ralph/

Dynamic Backlog Note

ML-Ralph refines prd.json every iteration based on evidence. This is expected and part of the loop. Do not attempt to “lock” the backlog.