Deepen Plan - Power Enhancement Mode

Introduction

Note: The current year is 2026. Use this when searching for recent documentation and best practices.

This skill takes an existing plan (from the soleur:plan skill) 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 knowledge-base/plans/
2. Ask the user: "Which plan would you like to deepen? Please provide the path (e.g., knowledge-base/plans/2026-01-15-feat-my-feature-plan.md)."

Do not proceed until a valid plan file path is provided.

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

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

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

# 3. soleur plugin skills
ls ~/.claude/plugins/cache/*/soleur/*/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 soleur 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

# 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 the `soleur:compound` skill. 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:

knowledge-base/learnings/           <-- PRIMARY: Project-level learnings (created by soleur: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:

# 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:

---
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:

# Read first 20 lines of each learning (frontmatter + summary)
head -20 knowledge-base/learnings/**/*.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]'
"

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_soleur_context7__resolve-library-id: Find library ID for [framework]
mcp__plugin_soleur_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

# 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. soleur plugin agents (all subdirectories)
find ~/.claude/plugins/cache/*/soleur/*/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/
• soleur plugin (but SKIP engineering/workflow/ agents - only use review, research, and design)
• ALL other installed plugins (agent-sdk-dev, frontend-design, etc.)
• Any local plugins

For soleur plugin specifically:

• USE: agents/engineering/review/* (all reviewers)
• USE: agents/engineering/research/* (all researchers)
• USE: agents/engineering/design/* (design agents)
• SKIP: agents/engineering/workflow/* (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 soleur: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:

## [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 the 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 soleur: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
• soleur:work -> Use skill: soleur:work 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 soleur:plan):

## Technical Approach

Use React Query for data fetching with optimistic updates.

After (from /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