deepen

name	deepen
description	Enhance an existing plan with parallel research agents for depth, best practices, and implementation details. Use when the user says 'deepen the plan', 'research more', or when a plan has high-risk dimensions that need more investigation.
argument-hint	Path to plan file (or reads from docs/plans/.latest)

Deepen Plan

Take an existing plan (from /cplan) and enhance 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.

Subagents

This skill dispatches these research subagents in parallel:

• cexplore — codebase patterns and conventions
• cdocs — external documentation via Context7 MCP
• clearnings — past solutions from docs/solutions/
• cbestpractices — industry standards and community patterns

It also launches all available *-reviewer agents against the plan content.

Workflow

Step 0: Locate the Plan

1. If a plan path was provided as argument, use it
2. Otherwise, read the path from docs/plans/.latest
3. If neither exists, list docs/plans/ and ask the user which plan to deepen

Do not proceed until you have a valid plan file.

Step 1: Parse and Analyze 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
• Any UI/UX components mentioned
• Technologies/frameworks mentioned

Create a section manifest:

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

Step 2: Research (Parallel)

Launch research sub-agents in parallel for each major section:

For each section, spawn a cexplore agent:

• Research codebase patterns and conventions for the section topic
• Find existing implementations and architecture decisions

Spawn cbestpractices for each major technology or pattern:

• Research industry standards and community conventions
• Find recommended patterns, anti-patterns, and production guidance
• Identify performance considerations and common pitfalls

For each technology/framework mentioned, spawn a cdocs agent:

• Fetch current documentation via Context7 MCP
• Find version-specific patterns and constraints
• Get concrete code examples

Spawn clearnings for documented solutions:

• Check docs/solutions/ for relevant past solutions
• Surface institutional knowledge that applies to this plan

Step 3: Discover and Run Review Agents

Launch ALL available review agents in parallel against the plan content. Do not filter by relevance — let each reviewer decide what applies. The goal is maximum coverage.

Step 4: Synthesize Findings

Collect outputs from all agents and:

• Extract concrete recommendations (actionable items)
• Extract code patterns and examples
• Identify anti-patterns to avoid
• Note performance and security considerations
• Surface edge cases
• Deduplicate and prioritize by impact
• Flag conflicting advice for human review
• Group findings by plan section

Step 5: Enhance Plan Sections

For each section, add research insights below the original content:

## [Original Section Title]

[Original content preserved]

### Research Insights

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

**Performance Considerations:**
- [Optimization opportunity]

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

Edge Cases:

• [Edge case and how to handle]

References:

• [Documentation URL]

### Step 6: Add Enhancement Summary

At the top of the plan, add:

```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]

Step 7: Handover

1. Update the plan file in place
2. Present a brief summary of what was added

Use #askQuestions to ask what the user wants to do next:

Option	When to show
Start Implementation (Recommended) — load the /work skill	Always (default)
Write Tests First (TDD) — load the /test skill	Always
Deepen further — re-run research on specific sections	Always
Review and refine — iterate on specific sections	Always

After the user picks a next skill, announce the handover and load the chosen skill.

Response Rules

• Never echo full file contents into chat — reference by path
• Keep chat responses under 500 words
• Preserve ALL original plan content — only add, never remove
• Mark all additions clearly as "Research Insights"

Guidelines

• Never write code in the plan — only research and enhance
• Include specific file paths and documentation URLs as references
• Make recommendations concrete and actionable
• When agents disagree, present both perspectives and flag for human decision