smithery.ai

deepen-plan

Enhance an existing plan with parallel research agents for depth, best practices, and implementation details.

First seen Apr 12, 2026

Installation

$ npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
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GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 6,979 B
  • docs SUMMARY.md 128 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Deepen Plan

Takes an existing plan and enhances each section with parallel research. Each major element gets dedicated research to find best practices, performance optimizations, quality enhancements, and real-world examples.

When This Skill Applies

  • User has an existing plan file they want to enhance
  • User asks to "deepen" or "research" a plan
  • User wants implementation details for a plan

Plan File

The user should provide a path to the plan file. If not provided:

  1. Check Claude Code's plan mode location first (most recent plans):

``bash ls -lt ~/.claude/plans/*.md 2>/dev/null | head -5 ``

  1. Check for local plans:

``bash ls -la plans/ 2>/dev/null || ls -la *.md ``

  1. If multiple plans found, show the user and ask which one to deepen
  2. If no plans found, ask the user: "Which plan would you like to deepen? Please provide the path."

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

Workflow

1. Parse Plan Structure

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
  • UI/UX components mentioned
  • Technologies/frameworks mentioned
  • 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 Available Skills

Check all skill sources and match to plan content:

# Project-local skills
ls .claude/skills/ 2>/dev/null

# User's global skills
ls ~/.claude/skills/ 2>/dev/null

# Plugin skills
find ~/.claude/plugins -type d -name "skills" 2>/dev/null

For each discovered skill:

  1. Read its SKILL.md to understand what it does
  2. Check if any plan sections match the skill's domain
  3. If matched, spawn a sub-agent to apply that skill's knowledge

3. Discover Documented Learnings

Check for previously solved problems:

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

# Alternative locations
find .claude/docs -name "*.md" -type f 2>/dev/null

For each learning file:

  1. Read frontmatter (title, category, tags, module)
  2. Filter by relevance to plan technologies/domains
  3. Spawn sub-agents for relevant learnings

4. Launch Parallel Research Agents

Launch all independent research agents in parallel using Task (subagent_type: Explore).

Based on the plan's technologies and sections, spawn these agents IN PARALLEL:

Task (subagent_type: Explore): "Research best practices for: [technology 1]
Find: industry standards, performance tips, common pitfalls, documentation.
Return concrete, actionable recommendations."

Task (subagent_type: Explore): "Research best practices for: [technology 2]
..."

Task (subagent_type: Explore): "Research implementation patterns for: [section topic]
..."

Spawn one agent per:

  • Each major technology mentioned (React, Rails, PostgreSQL, etc.)
  • Each architectural concern (caching, auth, API design, etc.)
  • Each domain area (data models, UI components, etc.)

Also use WebSearch for recent documentation on each technology.

5. Run Review Agents

Discover available review agents:

# Find all agent definitions
find ~/.claude -path "*/agents/*.md" 2>/dev/null
find .claude/agents -name "*.md" 2>/dev/null

Launch all review agents in parallel using Task (subagent_type: Explore) — they are read-only.

Task (subagent_type: Explore): "ARCHITECTURE REVIEW
Review this plan for architectural concerns:
- Scalability issues
- Coupling problems
- Missing components
Plan: [content]"

Task (subagent_type: Explore): "SECURITY REVIEW
Review this plan for security concerns:
- Auth/authz gaps
- Data exposure risks
- Input validation
Plan: [content]"

Task (subagent_type: Explore): "SIMPLICITY REVIEW
Review this plan for over-engineering:
- Unnecessary complexity
- Simpler alternatives
- YAGNI violations
Plan: [content]"

Task (subagent_type: Explore): "TESTABILITY REVIEW
Review this plan for testing concerns:
- Hard-to-test patterns
- Missing test strategies
- Edge cases to cover
Plan: [content]"

Rules:

  • Launch independent agents in one parallel wave, then wait once
  • Each agent catches different issues
  • Don't filter by "relevance" - run them all
  • Parent agent owns synthesis and final plan updates

6. Synthesize Findings

Wait for ALL parallel agents to complete, then collect:

From skill agents:

  • Code patterns and examples
  • Framework-specific recommendations

From research agents:

  • Best practices and documentation
  • Performance considerations
  • Real-world examples

From review agents:

  • Architecture feedback
  • Security considerations
  • Simplicity recommendations

From learnings:

  • Past solutions that apply
  • Mistakes to avoid

Deduplicate and prioritize:

  • Merge similar recommendations
  • Prioritize by impact
  • Flag conflicting advice
  • Group by plan section

7. Enhance Plan Sections

For each section, add research insights:

````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 ```

Edge Cases:

  • [Edge case 1 and handling]
  • [Edge case 2 and handling]

References:

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

````

8. Add Enhancement Summary

At the top of the enhanced plan:

## Enhancement Summary

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

### Key Improvements

1. [Major improvement 1]
2. [Major improvement 2]
3. [Major improvement 3]

### New Considerations Discovered

- [Important finding 1]
- [Important finding 2]
````

### 9. Write Enhanced Plan

Update the plan file in place, or create `[original-name]-deepened.md` if user prefers.

## Quality Checks

Before finalizing:

- [ ] All original content preserved
- [ ] Research insights clearly marked
- [ ] Code examples are syntactically correct
- [ ] Links are valid and relevant
- [ ] No contradictions between sections

## Post-Enhancement

After writing the enhanced plan, ask the user:

**"Plan deepened. What next?"**

Options:

1. **View diff** - Show what was added
2. **Review** - Get feedback from `/review`
3. **Implement** - Start working on the plan
4. **Deepen further** - Research specific sections more

## Important

**NEVER write code during this skill.** Only research and enhance the plan with findings.