gepetto
Gepetto orchestrates multi-phase planning for complex features by combining research, stakeholder interviews, specification synthesis, implementation planning, and external review cycles. Use this skill when scoping substantial features that require thorough pre-implementation analysis before development begins, as it generates detailed sectionized implementation plans and documentation artifacts to guide the development process.
git clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/gepetto && cp -r /tmp/gepetto/cli-tool/components/skills/ai-research/gepetto ~/.claude/skills/gepettoSKILL.md
# Gepetto
Orchestrates a multi-step planning process: Research → Interview → Spec Synthesis → Plan → External Review → Sections
## CRITICAL: First Actions
**BEFORE anything else**, do these in order:
### 1. Print Intro
Print intro banner immediately:
```
═══════════════════════════════════════════════════════════════
GEPETTO: AI-Assisted Implementation Planning
═══════════════════════════════════════════════════════════════
Research → Interview → Spec Synthesis → Plan → External Review → Sections
Note: GEPETTO will write many .md files to the planning directory you pass it
```
### 2. Validate Spec File Input
**Check if user provided @file at invocation AND it's a spec file (ends with `.md`).**
If NO @file was provided OR the path doesn't end with `.md`, output this and STOP:
```
═══════════════════════════════════════════════════════════════
GEPETTO: Spec File Required
═══════════════════════════════════════════════════════════════
This skill requires a markdown spec file path (must end with .md).
The planning directory is inferred from the spec file's parent directory.
To start a NEW plan:
1. Create a markdown spec file describing what you want to build
2. It can be as detailed or as vague as you like
3. Place it in a directory where gepetto can save planning files
4. Run: /gepetto @path/to/your-spec.md
To RESUME an existing plan:
1. Run: /gepetto @path/to/your-spec.md
Example: /gepetto @planning/my-feature-spec.md
═══════════════════════════════════════════════════════════════
```
**Do not continue. Wait for user to re-invoke with a .md file path.**
### 3. Setup Planning Session
Determine session state by checking existing files:
1. Set `planning_dir` = parent directory of the spec file
2. Set `initial_file` = the spec file path
3. Scan for existing planning files:
- `claude-research.md`
- `claude-interview.md`
- `claude-spec.md`
- `claude-plan.md`
- `claude-integration-notes.md`
- `claude-ralph-loop-prompt.md`
- `claude-ralphy-prd.md`
- `reviews/` directory
- `sections/` directory
4. Determine mode and resume point:
| Files Found | Mode | Resume From |
|-------------|------|-------------|
| None | new | Step 4 |
| research only | resume | Step 6 (interview) |
| research + interview | resume | Step 8 (spec synthesis) |
| + spec | resume | Step 9 (plan) |
| + plan | resume | Step 10 (external review) |
| + reviews | resume | Step 11 (integrate) |
| + integration-notes | resume | Step 12 (user review) |
| + sections/index.md | resume | Step 14 (write sections) |
| all sections complete | resume | Step 15 (execution files) |
| + claude-ralph-loop-prompt.md + claude-ralphy-prd.md | complete | Done |
5. Create TODO list with TodoWrite based on current state
Print status:
```
Planning directory: {planning_dir}
Mode: {mode}
```
If resuming:
```
Resuming from step {N}
To start fresh, delete the planning directory files.
```
---
## Logging Format
```
═══════════════════════════════════════════════════════════════
STEP {N}/17: {STEP_NAME}
═══════════════════════════════════════════════════════════════
{details}
Step {N} complete: {summary}
───────────────────────────────────────────────────────────────
```
---
## Workflow
### 4. Research Decision
See [research-protocol.md](references/research-protocol.md).
1. Read the spec file
2. Extract potential research topics (technologies, patterns, integrations)
3. Ask user about codebase research needs
4. Ask user about web research needs (present derived topics as multi-select)
5. Record which research types to perform in step 5
### 5. Execute Research
See [research-protocol.md](references/research-protocol.md).
Based on decisions from step 4, launch research subagents:
- **Codebase research:** `Task(subagent_type=Explore)`
- **Web research:** `Task(subagent_type=Explore)` with WebSearch
If both are needed, launch both Task tools in parallel (single message with multiple tool calls).
**Important:** Subagents return their findings - they do NOT write files directly. After collecting results from all subagents, combine them and write to `<planning_dir>/claude-research.md`.
Skip this step entirely if user chose no research in step 4.
### 6. Detailed Interview
See [interview-protocol.md](references/interview-protocol.md)
Run in main context (AskUserQuestion requires it). The interview should be informed by:
- The initial spec
- Research findings (if any)
### 7. Save Interview Transcript
Write Q&A to `<planning_dir>/claude-interview.md`
### 8. Write Initial Spec (Spec Synthesis)
Combine into `<planning_dir>/claude-spec.md`:
- **Initial input** (the spec file)
- **Research findings** (if step 5 was done)
- **Interview answers** (from step 6)
This synthesizes the user's raw requirements into a complete specification.
### 9. Generate Implementation Plan
Create detailed plan → `<planning_dir>/claude-plan.md`
**IMPORTANT**: Write for an unfamiliar reader. The plan must be fully self-contained - an engineer or LLM with no prior context should understand *what* we're building, *why*, and *how* just from reading this document.
### 10. External Review
See [external-review.md](references/external-review.md)
Launch TWO subagents in parallel to review the plan:
1. **Gemini** via Bash
2. **Codex** via Bash
Both receive the plan content and return their analysis. Write results to `<planning_dir>/reviews/`.
### 11. Integrate External Feedback
Analyze the suggestions in `<planning_dir>/reviews/`.
You are the authority on what to integrate or not. It's OK if you decide to not integrate anything.
**Step 1:** Write `<planning_dir>/claude-integration-notes.md` documenting:
- What suggestions you're integrating and why
- What suggestions you're NOT integrating and why
**Step 2:** Update `<planning_dir>/claude-plan.md` with the integrated changes.
### 12. User Review of Integrated Plan
Use AskUserQuestion:
```
The plan has been updated with external feedback. You can now review and editTesting and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Use this agent when creating specialized Claude Code agents for the claude-code-templates components system. Specializes in agent design, prompt engineering, domain expertise modeling, and agent best practices. Examples: <example>Context: User wants to create a new specialized agent. user: 'I need to create an agent that specializes in React performance optimization' assistant: 'I'll use the agent-expert agent to create a comprehensive React performance agent with proper domain expertise and practical examples' <commentary>Since the user needs to create a specialized agent, use the agent-expert agent for proper agent structure and implementation.</commentary></example> <example>Context: User needs help with agent prompt design. user: 'How do I create an agent that can handle both frontend and backend security?' assistant: 'Let me use the agent-expert agent to design a full-stack security agent with proper domain boundaries and expertise areas' <commentary>The user needs agent development help, so use the agent-expert agent.</commentary></example>
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa