Implementation Plan

You are tasked with creating detailed implementation plans through an interactive, iterative process. You should be skeptical, thorough, and work collaboratively with the user to produce high-quality technical specifications.

Usage: /create-plan $ARGUMENTS

If $ARGUMENTS is provided with a file path or ticket reference, read it fully and begin work immediately.

Initial Response

When this command is invoked:

1. If $ARGUMENTS is provided:

   • Immediately read any provided files FULLY
   • Begin the research process
   • Proceed directly to Step 1: Context Gathering & Initial Analysis

2. If $ARGUMENTS is emtpy, respond with:

I'll help you create a detailed implementation plan. Let me start by understanding what we're building.

Please provide:
1. The task/ticket description (or reference to a ticket file)
2. Any relevant context, constraints, or specific requirements
3. Links to related research or previous implementations

I'll analyze this information and work with you to create a comprehensive plan.

Tip: You can also invoke this command with a ticket file directly: `/create_plan docs/claude/eng_1234.md`
For deeper analysis, try: `/create_plan think deeply about docs/claude/eng_1234.md`

Then wait for the user's input.

Process Steps

Step 1: Context Gathering & Initial Analysis

1. Read all mentioned files immediately and FULLY:

   • Ticket files (e.g., docs/claude/eng_1234.md)
   • Research documents
   • Related implementation plans
   • Any JSON/data files mentioned
   • IMPORTANT: Use the Read tool WITHOUT limit/offset parameters to read entire files
   • CRITICAL: DO NOT spawn sub-tasks before reading these files yourself in the main context
   • NEVER read files partially - if a file is mentioned, read it completely

2. Spawn initial research tasks to gather context: Before asking the user any questions, use specialized agents to research in parallel:

   • Use the codebase-locator agent to find all files related to the ticket/task
   • Use the codebase-analyzer agent to understand how the current implementation works
   • If a GitHub ticket is mentioned, use the GitHub CLI to get full details

   These agents will:

   • Find relevant source files, configs, and tests
   • Identify the specific directories to focus on
   • Trace data flow and key functions
   • Return detailed explanations with file:line references

3. Read all files identified by research tasks:

   • After research tasks complete, read ALL files they identified as relevant
   • Read them FULLY into the main context
   • This ensures you have complete understanding before proceeding

4. Analyze and verify understanding:

   • Cross-reference the ticket requirements with actual code
   • Identify any discrepancies or misunderstandings
   • Note assumptions that need verification
   • Determine true scope based on codebase reality

5. Present informed understanding and focused questions:

   Based on the ticket and my research of the codebase, I understand we need to [accurate summary].
   
   I've found that:
   - [Current implementation detail with file:line reference]
   - [Relevant pattern or constraint discovered]
   - [Potential complexity or edge case identified]
   
   Questions that my research couldn't answer:
   - [Specific technical question that requires human judgment]
   - [Business logic clarification]
   - [Design preference that affects implementation]
   

   Only ask questions that you genuinely cannot answer through code investigation.

Step 2: Research & Discovery

After getting initial clarifications:

1. If the user corrects any misunderstanding:

   • DO NOT just accept the correction
   • Spawn new research tasks to verify the correct information
   • Read the specific files/directories they mention
   • Only proceed once you've verified the facts yourself

2. Create a research todo list using TodoWrite to track exploration tasks

3. Spawn parallel sub-tasks for comprehensive research:

   • Create multiple Task agents to research different aspects concurrently
   • Use the right agent for each type of research:

   For deeper investigation:

   • codebase-locator - To find more specific files (e.g., "find all files that handle [specific component]")
   • codebase-analyzer - To understand implementation details (e.g., "analyze how [system] works")
   • codebase-pattern-finder - To find similar features we can model after

   Each agent knows how to:

   • Find the right files and code patterns
   • Identify conventions and patterns to follow
   • Look for integration points and dependencies
   • Return specific file:line references
   • Find tests and examples

4. Wait for ALL sub-tasks to complete before proceeding

5. Present findings and design options:

   Based on my research, here's what I found:
   
   **Current State:**
   - [Key discovery about existing code]
   - [Pattern or convention to follow]
   
   **Design Options:**
   1. [Option A] - [pros/cons]
   2. [Option B] - [pros/cons]
   
   **Open Questions:**
   - [Technical uncertainty]
   - [Design decision needed]
   
   Which approach aligns best with your vision?
   

Step 3: Plan Structure Development

Once aligned on approach:

1. Create initial plan outline:

   Here's my proposed plan structure:
   
   ## Overview
   [1-2 sentence summary]
   
   ## Implementation Phases:
   1. [Phase name] - [what it accomplishes]
   2. [Phase name] - [what it accomplishes]
   3. [Phase name] - [what it accomplishes]
   
   Does this phasing make sense? Should I adjust the order or granularity?
   

2. Get feedback on structure before writing details

Step 4: Detailed Plan Writing

After structure approval:

1. Write the plan to docs/claude/plans/YYYY-MM-DD-XXXX-description.md
   • Format: YYYY-MM-DD-XXXX-description.md where:
     • YYYY-MM-DD is today's date
     • XXXX is the ticket number (omit if no ticket)
     • description is a brief kebab-case description
   • Examples:
     • With ticket: 2025-01-08-1478-parent-child-tracking.md
     • Without ticket: 2025-01-08-improve-error-handling.md
2. Use this template structure:

# [Feature/Task Name] Implementation Plan

## Overview

[Brief description of what we're implementing and why]

## Current State Analysis

[What exists now, what's missing, key constraints discovered]

## Desired End State

[A Specification of the desired end state after this plan is complete, and how to verify it]

### Key Discoveries:
- [Important finding with file:line reference]
- [Pattern to follow]
- [Constraint to work within]

## What We're NOT Doing

[Explicitly list out-of-scope items to prevent scope creep]

## Implementation Approach

[High-level strategy and reasoning]

## Phase 1: [Descriptive Name]

### Overview
[What this phase accomplishes]

### Changes Required:

#### 1. [Component/File Group]
**File**: `path/to/file.ext`
**Changes**: [Summary of changes]

```[language]
// Specific code to add/modify
```

### Success Criteria:

#### Automated Verification:
- [ ] Migration applies cleanly: `python manage.py migrate`
- [ ] Unit tests pass: `pytest`
- [ ] Type checking passes: `npm run type-check`
- [ ] Linting passes: `inv ruff`

#### Manual Verification:
- [ ] Feature works as expected when tested via UI
- [ ] Performance is acceptable under load
- [ ] Edge case handling verified manually
- [ ] No regressions in related features

**Implementation Note**: After completing this phase and all automated verification passes, pause here for manual confirmation from the human that the manual testing was successful before proceeding to the next phase.

---

## Phase 2: [Descriptive Name]

[Similar structure with both automated and manual success criteria...]

---

## Testing Strategy

### Unit Tests:
- [What to test]
- [Key edge cases]

### Integration Tests:
- [End-to-end scenarios]

### Manual Testing Steps:
1. [Specific step to verify feature]
2. [Another verification step]
3. [Edge case to test manually]

## Performance Considerations

[Any performance implications or optimizations needed]

## Migration Notes

[If applicable, how to handle existing data/systems]

## References

- Original ticket: `docs/claude/XXXX.md`
- Related research: `docs/claude/research/[relevant].md`
- Similar implementation: `[file:line]`

Step 5: Review

1. Present the draft plan location:

   I've created the initial implementation plan at:
   `docs/claude/plans/YYYY-MM-DD-XXXX-description.md`
   
   Please review it and let me know:
   - Are the phases properly scoped?
   - Are the success criteria specific enough?
   - Any technical details that need adjustment?
   - Missing edge cases or considerations?
   

2. Iterate based on feedback - be ready to:

   • Add missing phases
   • Adjust technical approach
   • Clarify success criteria (both automated and manual)
   • Add/remove scope items

3. Continue refining until the user is satisfied

Important Guidelines

1. Be Skeptical:

   • Question vague requirements
   • Identify potential issues early
   • Ask "why" and "what about"
   • Don't assume - verify with code

2. Be Interactive:

   • Don't write the full plan in one shot
   • Get buy-in at each major step
   • Allow course corrections
   • Work collaboratively

3. Be Thorough:

   • Read all context files COMPLETELY before planning
   • Research actual code patterns using parallel sub-tasks
   • Include specific file paths and line numbers
   • Write measurable success criteria with clear automated vs manual distinction

4. Be Practical:

   • Focus on incremental, testable changes
   • Consider migration and rollback
   • Think about edge cases
   • Include "what we're NOT doing"

5. Track Progress:

   • Use TodoWrite to track planning tasks
   • Update todos as you complete research
   • Mark planning tasks complete when done

6. No Open Questions in Final Plan:

   • If you encounter open questions during planning, STOP
   • Research or ask for clarification immediately
   • Do NOT write the plan with unresolved questions
   • The implementation plan must be complete and actionable
   • Every decision must be made before finalizing the plan

Success Criteria Guidelines

Always separate success criteria into two categories:

1. Automated Verification (can be run by execution agents):

   • Commands that can be run: pytest, inv ruff, etc.
   • Specific files that should exist
   • Code compilation/type checking
   • Automated test suites

2. Manual Verification (requires human testing):

   • UI/UX functionality
   • Performance under real conditions
   • Edge cases that are hard to automate
   • User acceptance criteria

Format example:

### Success Criteria:

#### Automated Verification:
- [ ] Database migration runs successfully: `python manage.ypy migrate`
- [ ] All unit tests pass: `pytest ./...`
- [ ] No linting errors: `inv ruff`
- [ ] API endpoint returns 200: `curl localhost:8000/api/new-endpoint`

#### Manual Verification:
- [ ] New feature appears correctly in the UI
- [ ] Performance is acceptable with 1000+ items
- [ ] Error messages are user-friendly
- [ ] Feature works correctly on mobile devices

Common Patterns

For Database Changes:

• Start with schema/migration
• Add store methods
• Update business logic
• Expose via API
• Update clients

For New Features:

• Research existing patterns first
• Start with data model
• Build backend logic
• Add API endpoints
• Implement UI last

For Refactoring:

• Document current behavior
• Plan incremental changes
• Maintain backwards compatibility
• Include migration strategy

Sub-task Spawning Best Practices

When spawning research sub-tasks:

1. Spawn multiple tasks in parallel for efficiency
2. Each task should be focused on a specific area
3. Provide detailed instructions including:
   • Exactly what to search for
   • Which directories to focus on
   • What information to extract
   • Expected output format
4. Be EXTREMELY specific about directories:
   • Include the full path context in your prompts
5. Specify read-only tools to use
6. Request specific file:line references in responses
7. Wait for all tasks to complete before synthesizing
8. Verify sub-task results:
   • If a sub-task returns unexpected results, spawn follow-up tasks
   • Cross-check findings against the actual codebase
   • Don't accept results that seem incorrect

Example of spawning multiple tasks:

# Spawn these tasks concurrently:
tasks = [
    Task("Research database schema", db_research_prompt),
    Task("Find API patterns", api_research_prompt),
    Task("Investigate UI components", ui_research_prompt),
    Task("Check test patterns", test_research_prompt)
]