claude-engineering-plugin/plugins/compound-engineering/commands/deepen-plan.md

name, description, argument-hint

name	description	argument-hint
deepen-plan	Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details	[path to plan file]

Deepen Plan - Power Enhancement Mode

Introduction

Note: The current year is 2025. Use this when searching for recent documentation and best practices.

This command takes an existing plan (from /workflows:plan) and enhances each section with parallel research agents. Each major element gets its own dedicated research sub-agent to find:

• Best practices and industry patterns
• Performance optimizations
• UI/UX improvements (if applicable)
• Quality enhancements and edge cases
• Real-world implementation examples

The result is a deeply grounded, production-ready plan with concrete implementation details.

Plan File

<plan_path> #$ARGUMENTS </plan_path>

If the plan path above is empty:

1. Check for recent plans: ls -la plans/
2. Ask the user: "Which plan would you like to deepen? Please provide the path (e.g., plans/my-feature.md)."

Do not proceed until you have a valid plan file path.

Main Tasks

1. Parse and Analyze Plan Structure

First, read and parse the plan to identify each major section that can be enhanced with research.

Read the plan file and extract:

• Overview/Problem Statement
• Proposed Solution sections
• Technical Approach/Architecture
• Implementation phases/steps
• Code examples and file references
• Acceptance criteria
• Any UI/UX components mentioned

Create a section manifest:

Section 1: [Title] - [Brief description of what to research]
Section 2: [Title] - [Brief description of what to research]
...

2. Launch Parallel Research Agents

For each major section, spawn a dedicated sub-agent to research improvements. Run all agents in parallel for maximum efficiency.

For each identified section, launch a parallel Task agent:

Launch these agents simultaneously using Task tool with appropriate subagent_type:

Agent Categories by Section Type:

For Architecture/Technical sections:

• Task best-practices-researcher: "Research best practices for [section topic]. Find industry patterns, performance considerations, and common pitfalls."
• Task framework-docs-researcher: "Find official documentation and examples for [technologies mentioned]."

For Implementation/Code sections:

• Task pattern-recognition-specialist: "Analyze patterns for [implementation approach]. Find optimal code structures and anti-patterns to avoid."
• Task performance-oracle: "Research performance implications of [approach]. Find optimization strategies and benchmarks."

For UI/UX sections:

• Task best-practices-researcher: "Research UI/UX best practices for [component type]. Find accessibility standards, responsive patterns, and user experience improvements."

For Data/Models sections:

• Task data-integrity-guardian: "Research data modeling best practices for [model type]. Find validation patterns, indexing strategies, and migration safety."

For Security-sensitive sections:

• Task security-sentinel: "Research security considerations for [feature]. Find OWASP patterns, authentication best practices, and vulnerability prevention."

3. Collect and Synthesize Research

Wait for all parallel agents to complete, then synthesize their findings into actionable enhancements for each section.

For each agent's findings:

• Extract concrete recommendations
• Note specific code patterns or examples
• Identify performance metrics or benchmarks
• List relevant documentation links
• Capture edge cases discovered

4. Enhance Plan Sections

Merge research findings back into the plan, adding depth without changing the original structure.

Enhancement format for each section:

## [Original Section Title]

[Original content preserved]

### Research Insights

**Best Practices:**
- [Concrete recommendation 1]
- [Concrete recommendation 2]

**Performance Considerations:**
- [Optimization opportunity]
- [Benchmark or metric to target]

**Implementation Details:**
```[language]
// Concrete code example from research

Edge Cases:

• [Edge case 1 and how to handle]
• [Edge case 2 and how to handle]

References:

• [Documentation URL 1]
• [Documentation URL 2]

### 5. Add Enhancement Summary

At the top of the plan, add a summary section:

```markdown
## Enhancement Summary

**Deepened on:** [Date]
**Sections enhanced:** [Count]
**Research agents used:** [List]

### Key Improvements
1. [Major improvement 1]
2. [Major improvement 2]
3. [Major improvement 3]

### New Considerations Discovered
- [Important finding 1]
- [Important finding 2]

6. Update Plan File

Write the enhanced plan:

• Preserve original filename
• Add -deepened suffix if user prefers a new file
• Update any timestamps or metadata

Output Format

Update the plan file in place (or create plans/<original-name>-deepened.md if requested).

Quality Checks

Before finalizing:

• All original content preserved
• Research insights clearly marked and attributed
• Code examples are syntactically correct
• Links are valid and relevant
• No contradictions between sections
• Enhancement summary accurately reflects changes

Post-Enhancement Options

After writing the enhanced plan, use the AskUserQuestion tool to present these options:

Question: "Plan deepened at [plan_path]. What would you like to do next?"

Options:

1. View diff - Show what was added/changed
2. Run /plan_review - Get feedback from reviewers on enhanced plan
3. Start /workflows:work - Begin implementing this enhanced plan
4. Deepen further - Run another round of research on specific sections
5. Revert - Restore original plan (if backup exists)

Based on selection:

• View diff → Run git diff [plan_path] or show before/after
• /plan_review → Call the /plan_review command with the plan file path
• /workflows:work → Call the /workflows:work command with the plan file path
• Deepen further → Ask which sections need more research, then re-run those agents
• Revert → Restore from git or backup

Example Enhancement

Before (from /workflows:plan):

## Technical Approach

Use React Query for data fetching with optimistic updates.

After (from /workflows:deepen-plan):

## Technical Approach

Use React Query for data fetching with optimistic updates.

### Research Insights

**Best Practices:**
- Configure `staleTime` and `cacheTime` based on data freshness requirements
- Use `queryKey` factories for consistent cache invalidation
- Implement error boundaries around query-dependent components

**Performance Considerations:**
- Enable `refetchOnWindowFocus: false` for stable data to reduce unnecessary requests
- Use `select` option to transform and memoize data at query level
- Consider `placeholderData` for instant perceived loading

**Implementation Details:**
```typescript
// Recommended query configuration
const queryClient = new QueryClient({
  defaultOptions: {
    queries: {
      staleTime: 5 * 60 * 1000, // 5 minutes
      retry: 2,
      refetchOnWindowFocus: false,
    },
  },
});

Edge Cases:

• Handle race conditions with cancelQueries on component unmount
• Implement retry logic for transient network failures
• Consider offline support with persistQueryClient

References:

• https://tanstack.com/query/latest/docs/react/guides/optimistic-updates
• https://tkdodo.eu/blog/practical-react-query

NEVER CODE! Just research and enhance the plan.