ralph-deep-init
Build a comprehensive project backlog through multi-stage architectural analysis. Creates 6 functional groups and generates detailed tasks for each. Use when you need a large, well-organized backlog for complex projects.
Best use case
ralph-deep-init is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Build a comprehensive project backlog through multi-stage architectural analysis. Creates 6 functional groups and generates detailed tasks for each. Use when you need a large, well-organized backlog for complex projects.
Teams using ralph-deep-init should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ralph-deep-init/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ralph-deep-init Compares
| Feature / Agent | ralph-deep-init | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Build a comprehensive project backlog through multi-stage architectural analysis. Creates 6 functional groups and generates detailed tasks for each. Use when you need a large, well-organized backlog for complex projects.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Ralph Deep Initialization This skill performs a comprehensive, multi-stage project initialization that generates a large, well-organized backlog by breaking down the project into functional architectural groups. ## When to Use This Skill Use ralph-deep-init instead of ralph-initialize when: - Building a complex project with many features - Need 20-40+ detailed tasks in the backlog - Want tasks organized by architectural concerns - Project has multiple distinct functional areas ## Prerequisites - Git repository initialized - No existing `prd.json` (or user approves overwrite) - Clear understanding of project requirements ## Multi-Stage Process ### Phase 1: Architecture **Goal:** Initialize the project structure and define the architectural map. 1. **Analyze Request** - Ask: "What do you want to build?" if not already provided - Gather comprehensive project requirements 2. **Create Foundation Files** - `progress.md` - Standard Ralph progress log - `.windsurf/rules/tech-stack.md` - Tech stack & conventions - `init_progress.txt` - Temporary initialization tracking 3. **Generate Architecture** - Identify **6 distinct functional groups** needed for this project - Examples: Auth, Database, API, Frontend, Testing, DevOps - Create `groups.json` containing ONLY a JSON array of these string names - Format: `["Group1", "Group2", "Group3", "Group4", "Group5", "Group6"]` ### Phase 2: Expansion (6 Iterations) **Goal:** Generate detailed tasks for each functional group. For each of the 6 groups (iterate 1 through 6): 1. **Select Target:** Pick group N from `groups.json` 2. **Generate Tasks:** Create 3-5 detailed implementation tasks for this group 3. **Write to File:** Save to `partial_N.json` **Task Template:** Use `.windsurf/skills/ralph-deep-init/task-examples.json` as the reference schema for each `partial_N.json`. ### Phase 3: Assembly **Goal:** Compile the final Product Requirements Document. 1. **Create PRD** - Create `prd.json` (use `.windsurf/skills/ralph-initialize/prd-template.json` as reference) - Read all `partial_*.json` files - Merge all `tasks` arrays into the `backlog` field - Ensure valid JSON structure 2. **Cleanup** - Delete `groups.json` - Delete all `partial_*.json` files - Keep `init_progress.txt` for reference 3. **Report** - Announce: "Initialization Complete. Backlog created with [X] tasks." - Summarize task breakdown by group ## Supporting Resources Reference these files: - `architecture-examples.md` - Sample functional group breakdowns - `task-examples.json` - Well-formed task examples - `groups-template.json` - Template for groups array ## Success Criteria Deep initialization is complete when: - ✅ `prd.json` exists with 20+ tasks - ✅ Tasks are organized by functional groups - ✅ All tasks have `passes: false` - ✅ Each task has clear acceptance criteria - ✅ `progress.md` exists - ✅ Temporary files are cleaned up ## Tips - Choose groups that represent distinct technical concerns - Ensure tasks within a group are cohesive - Order tasks to respect dependencies - Include infrastructure/testing as separate groups - Aim for 3-5 tasks per group (total: 18-30 tasks)