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

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**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

**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:

**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:**

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

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

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**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"
---

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**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:

**Example filtering:**

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

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

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."

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

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**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:

**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.

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// Concrete code example from research

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

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

NEVER CODE! Just research and enhance the plan.