deepen-plan skill

---
name: deepen-plan
description: Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details
---

## Arguments
[path to plan file]

# Deepen Plan - Power Enhancement Mode

## Introduction

**Note: The current year is 2026.** 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 docs/plans/`
2. Ask the user: "Which plan would you like to deepen? Please provide the path (e.g., `docs/plans/2026-01-15-feat-my-feature-plan.md`)."

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

## Main Tasks

### 1. Parse and Analyze Plan Structure

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

**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
- [ ] Technologies/frameworks mentioned (Rails, React, Python, TypeScript, etc.)
- [ ] Domain areas (data models, APIs, UI, security, performance, etc.)

**Create a section manifest:**
```
Section 1: [Title] - [Brief description of what to research]
Section 2: [Title] - [Brief description of what to research]
...
```

### 2. Discover and Apply Available Skills

<thinking>
Dynamically discover all available skills and match them to plan sections. Don't assume what skills exist - discover them at runtime.
</thinking>

**Step 1: Discover ALL available skills from ALL sources**

```bash
# 1. Project-local skills (highest priority - project-specific)
ls .claude/skills/

# 2. User's global skills (~/.claude/)
ls ~/.claude/skills/

# 3. compound-engineering plugin skills
ls ~/.claude/plugins/cache/*/compound-engineering/*/skills/

# 4. ALL other installed plugins - check every plugin for skills
find ~/.claude/plugins/cache -type d -name "skills" 2>/dev/null

# 5. Also check installed_plugins.json for all plugin locations
cat ~/.claude/plugins/installed_plugins.json
```

**Important:** Check EVERY source. Don't assume compound-engineering is the only plugin. Use skills from ANY installed plugin that's relevant.

**Step 2: For each discovered skill, read its SKILL.md to understand what it does**

```bash
# For each skill directory found, read its documentation
cat [skill-path]/SKILL.md
```

**Step 3: Match skills to plan content**

For each skill discovered:
- Read its SKILL.md description
- Check if any plan sections match the skill's domain
- If there's a match, spawn a sub-agent to apply that skill's knowledge

**Step 4: Spawn a sub-agent for EVERY matched skill**

**CRITICAL: For EACH skill that matches, spawn a separate sub-agent and instruct it to USE that skill.**

For each matched skill:
```
Task general-purpose: "You have the [skill-name] skill available at [skill-path].

YOUR JOB: Use this skill on the plan.

1. Read the skill: cat [skill-path]/SKILL.md
2. Follow the skill's instructions exactly
3. Apply the skill to this content:

[relevant plan section or full plan]

4. Return the skill's full output

The skill tells you what to do - follow it. Execute the skill completely."
```

**Spawn ALL skill sub-agents in PARALLEL:**
- 1 sub-agent per matched skill
- Each sub-agent reads and uses its assigned skill
- All run simultaneously
- 10, 20, 30 skill sub-agents is fine

**Each sub-agent:**
1. Reads its skill's SKILL.md
2. Follows the skill's workflow/instructions
3. Applies the skill to the plan
4. Returns whatever the skill produces (code, recommendations, patterns, reviews, etc.)

**Example spawns:**
```
Task general-purpose: "Use the dhh-rails-style skill at ~/.claude/plugins/.../dhh-rails-style. Read SKILL.md and apply it to: [Rails sections of plan]"

Task general-purpose: "Use the frontend-design skill at ~/.claude/plugins/.../frontend-design. Read SKILL.md and apply it to: [UI sections of plan]"

Task general-purpose: "Use the agent-native-architecture skill at ~/.claude/plugins/.../agent-native-architecture. Read SKILL.md and apply it to: [agent/tool sections of plan]"

Task general-purpose: "Use the security-patterns skill at ~/.claude/skills/security-patterns. Read SKILL.md and apply it to: [full plan]"
```

**No limit on skill sub-agents. Spawn one for every skill that could possibly be relevant.**

### 3. Discover and Apply Learnings/Solutions

<thinking>
Check for documented learnings from /workflows:compound. These are solved problems stored as markdown files. Spawn a sub-agent for each learning to check if it's relevant.
</thinking>

**LEARNINGS LOCATION - Check these exact folders:**

```
docs/solutions/           <-- PRIMARY: Project-level learnings (created by /workflows:compound)
├── performance-issues/
│   └── *.md
├── debugging-patterns/
│   └── *.md
├── configuration-fixes/
│   └── *.md
├── integration-issues/
│   └── *.md
├── deployment-issues/
│   └── *.md
└── [other-categories]/
    └── *.md
```

**Step 1: Find ALL learning markdown files**

Run these commands to get every learning file:

```bash
# PRIMARY LOCATION - Project learnings
find docs/solutions -name "*.md" -type f 2>/dev/null

# If docs/solutions doesn't exist, check alternate locations:
find .claude/docs -name "*.md" -type f 2>/dev/null
find ~/.claude/docs -name "*.md" -type f 2>/dev/null
```

**Step 2: Read frontmatter of each learning to filter**

Each learning file has YAML frontmatter with metadata. Read the first ~20 lines of each file to get:

```yaml
---
title: "N+1 Query Fix for Briefs"
category: performance-issues
tags: [activerecord, n-plus-one, includes, eager-loading]
module: Briefs
symptom: "Slow page load, multiple queries in logs"
root_cause: "Missing includes on association"
---
```

**For each .md file, quickly scan its frontmatter:**

```bash
# Read first 20 lines of each learning (frontmatter + summary)
head -20 docs/solutions/**/*.md
```

**Step 3: Filter - only spawn sub-agents for LIKELY relevant learnings**

Compare each learning's frontmatter against the plan:
- `tags:` - Do any tags match technologies/patterns in the plan?
- `category:` - Is this category relevant? (e.g., skip deployment-issues if plan is UI-only)
- `module:` - Does the plan touch this module?
- `symptom:` / `root_cause:` - Could this problem occur with the plan?

**SKIP learnings that are clearly not applicable:**
- Plan is frontend-only → skip `database-migrations/` learnings
- Plan is Python → skip `rails-specific/` learnings
- Plan has no auth → skip `authentication-issues/` learnings

**SPAWN sub-agents for learnings that MIGHT apply:**
- Any tag overlap with plan technologies
- Same category as plan domain
- Similar patterns or concerns

**Step 4: Spawn sub-agents for filtered learnings**

For each learning that passes the filter:

```
Task general-purpose: "
LEARNING FILE: [full path to .md file]

1. Read this learning file completely
2. This learning documents a previously solved problem

Check if this learning applies to this plan:

---
[full plan content]
---

If relevant:
- Explain specifically how it applies
- Quote the key insight or solution
- Suggest where/how to incorporate it

If NOT relevant after deeper analysis:
- Say 'Not applicable: [reason]'
"
```

**Example filtering:**
```
# Found 15 learning files, plan is about "Rails API caching"

# SPAWN (likely relevant):
docs/solutions/performance-issues/n-plus-one-queries.md      # tags: [activerecord] ✓
docs/solutions/performance-issues/redis-cache-stampede.md    # tags: [caching, redis] ✓
docs/solutions/configuration-fixes/redis-connection-pool.md  # tags: [redis] ✓

# SKIP (clearly not applicable):
docs/solutions/deployment-issues/heroku-memory-quota.md      # not about caching
docs/solutions/frontend-issues/stimulus-race-condition.md    # plan is API, not frontend
docs/solutions/authentication-issues/jwt-expiry.md           # plan has no auth
```

**Spawn sub-agents in PARALLEL for all filtered learnings.**

**These learnings are institutional knowledge - applying them prevents repeating past mistakes.**

### 4. Launch Per-Section Research Agents

<thinking>
For each major section in the plan, spawn dedicated sub-agents to research improvements. Use the Explore agent type for open-ended research.
</thinking>

**For each identified section, launch parallel research:**

```
Task Explore: "Research best practices, patterns, and real-world examples for: [section topic].
Find:
- Industry standards and conventions
- Performance considerations
- Common pitfalls and how to avoid them
- Documentation and tutorials
Return concrete, actionable recommendations."
```

**Also use Context7 MCP for framework documentation:**

For any technologies/frameworks mentioned in the plan, query Context7:
```
mcp__plugin_compound-engineering_context7__resolve-library-id: Find library ID for [framework]
mcp__plugin_compound-engineering_context7__query-docs: Query documentation for specific patterns
```

**Use WebSearch for current best practices:**

Search for recent (2024-2026) articles, blog posts, and documentation on topics in the plan.

### 5. Discover and Run ALL Review Agents

<thinking>
Dynamically discover every available agent and run them ALL against the plan. Don't filter, don't skip, don't assume relevance. 40+ parallel agents is fine. Use everything available.
</thinking>

**Step 1: Discover ALL available agents from ALL sources**

```bash
# 1. Project-local agents (highest priority - project-specific)
find .claude/agents -name "*.md" 2>/dev/null

# 2. User's global agents (~/.claude/)
find ~/.claude/agents -name "*.md" 2>/dev/null

# 3. compound-engineering plugin agents (all subdirectories)
find ~/.claude/plugins/cache/*/compound-engineering/*/agents -name "*.md" 2>/dev/null

# 4. ALL other installed plugins - check every plugin for agents
find ~/.claude/plugins/cache -path "*/agents/*.md" 2>/dev/null

# 5. Check installed_plugins.json to find all plugin locations
cat ~/.claude/plugins/installed_plugins.json

# 6. For local plugins (isLocal: true), check their source directories
# Parse installed_plugins.json and find local plugin paths
```

**Important:** Check EVERY source. Include agents from:
- Project `.claude/agents/`
- User's `~/.claude/agents/`
- compound-engineering plugin (but SKIP workflow/ agents - only use review/, research/, design/, docs/)
- ALL other installed plugins (agent-sdk-dev, frontend-design, etc.)
- Any local plugins

**For compound-engineering plugin specifically:**
- USE: `agents/review/*` (all reviewers)
- USE: `agents/research/*` (all researchers)
- USE: `agents/design/*` (design agents)
- USE: `agents/docs/*` (documentation agents)
- SKIP: `agents/workflow/*` (these are workflow orchestrators, not reviewers)

**Step 2: For each discovered agent, read its description**

Read the first few lines of each agent file to understand what it reviews/analyzes.

**Step 3: Launch ALL agents in parallel**

For EVERY agent discovered, launch a Task in parallel:

```
Task [agent-name]: "Review this plan using your expertise. Apply all your checks and patterns. Plan content: [full plan content]"
```

**CRITICAL RULES:**
- Do NOT filter agents by "relevance" - run them ALL
- Do NOT skip agents because they "might not apply" - let them decide
- Launch ALL agents in a SINGLE message with multiple Task tool calls
- 20, 30, 40 parallel agents is fine - use everything
- Each agent may catch something others miss
- The goal is MAXIMUM coverage, not efficiency

**Step 4: Also discover and run research agents**

Research agents (like `best-practices-researcher`, `framework-docs-researcher`, `git-history-analyzer`, `repo-research-analyst`) should also be run for relevant plan sections.

### 6. Wait for ALL Agents and Synthesize Everything

<thinking>
Wait for ALL parallel agents to complete - skills, research agents, review agents, everything. Then synthesize all findings into a comprehensive enhancement.
</thinking>

**Collect outputs from ALL sources:**

1. **Skill-based sub-agents** - Each skill's full output (code examples, patterns, recommendations)
2. **Learnings/Solutions sub-agents** - Relevant documented learnings from /workflows:compound
3. **Research agents** - Best practices, documentation, real-world examples
4. **Review agents** - All feedback from every reviewer (architecture, security, performance, simplicity, etc.)
5. **Context7 queries** - Framework documentation and patterns
6. **Web searches** - Current best practices and articles

**For each agent's findings, extract:**
- [ ] Concrete recommendations (actionable items)
- [ ] Code patterns and examples (copy-paste ready)
- [ ] Anti-patterns to avoid (warnings)
- [ ] Performance considerations (metrics, benchmarks)
- [ ] Security considerations (vulnerabilities, mitigations)
- [ ] Edge cases discovered (handling strategies)
- [ ] Documentation links (references)
- [ ] Skill-specific patterns (from matched skills)
- [ ] Relevant learnings (past solutions that apply - prevent repeating mistakes)

**Deduplicate and prioritize:**
- Merge similar recommendations from multiple agents
- Prioritize by impact (high-value improvements first)
- Flag conflicting advice for human review
- Group by plan section

### 7. Enhance Plan Sections

<thinking>
Merge research findings back into the plan, adding depth without changing the original structure.
</thinking>

**Enhancement format for each section:**

```markdown
## [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]
```

### 8. 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]
```

### 9. 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 if user requests a separate file, append `-deepened` after `-plan`, e.g., `2026-01-15-feat-auth-plan-deepened.md`).

## 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):**
```markdown
## Technical Approach

Use React Query for data fetching with optimistic updates.
```

**After (from /workflows:deepen-plan):**
```markdown
## 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.