Generating Prd JSON From Prd Md

PRD to JSON Conversion

Hybrid approach: Python script parses, AI validates and corrects.

Workflow

1. Dry-run parser (Bash tool) — catch parser issues before writing

python ralph/scripts/generate_prd_json.py --dry-run

Check output for: declared vs parsed story count mismatch, missing stories, empty acceptance/files. If issues found, fix PRD markdown or note for manual correction in step 3.

2. Run parser (Bash tool)

python ralph/scripts/generate_prd_json.py

Script handles: PRD.md parsing, (depends: ...) extraction, content hashing, state preservation.

3. Validate (Read tool)

   • Read ralph/docs/prd.json (script output)
   • Read the sprint PRD (docs/PRD.md symlink or docs/sprints/ source)
   • Check against Validation Checklist

4. Correct errors (Write tool, if needed)

   • Fix issues found
   • Recompute content_hash if title/description/acceptance changed
   • Write corrected ralph/docs/prd.json

5. Report

   • Story count and status
   • Corrections made
   • Suggest: make ralph_run

Validation Checklist

For each story, verify:

• id follows STORY-XXX format
• title is 3-7 words, matches PRD.md feature
• description is non-empty
• acceptance array is non-empty
• files array contains valid paths (if specified in PRD.md)
• content_hash is 64-char hex string
• depends_on references valid STORY-XXX IDs (no circular deps, no self-refs)

Cross-reference with PRD.md:

• All #### Feature N: headings have corresponding stories
• Story order matches PRD.md feature order
• (depends: STORY-XXX) syntax correctly parsed

Common Issues to Correct

prd.json Schema

See ralph/docs/templates/prd.json.template for structure and fields.

Usage

make ralph_prd_json

Next Steps

make ralph_init    # Validate environment
make ralph_run     # Start Ralph loop