Deepen Plan

Enhance an existing plan with dynamic skill/agent discovery and targeted research.

Plan Folder

<plan_path> #$ARGUMENTS </plan_path>

If empty: Check ls docs/plans/ and ask user which plan to deepen.

Workflow

1. Load Plan

Read the plan folder contents:

• spec.md - Human-readable plan
• prd.json - Machine-executable stories
• brainstorm.md - Optional context

Parse and identify:

• Technologies mentioned (React, Next.js, Node.js, TypeScript, etc.)
• Domain areas (UI, API, data, auth, payments, etc.)
• Story categories from prd.json
• Keywords and triggers

2. Discover Available Skills

Scan all skill paths:

# User skills
find ~/.claude/skills -name "SKILL.md" 2>/dev/null

# Plugin skills
find ~/.claude/plugins/cache -name "SKILL.md" 2>/dev/null
find ~/.claude/plugins/marketplaces -name "SKILL.md" 2>/dev/null

Extract skill metadata from each SKILL.md:

for skill_file in $(find ~/.claude/skills ~/.claude/plugins -name "SKILL.md" 2>/dev/null); do
  # Extract frontmatter
  name=$(sed -n '/^---$/,/^---$/p' "$skill_file" | grep "^name:" | cut -d: -f2- | xargs)
  description=$(sed -n '/^---$/,/^---$/p' "$skill_file" | grep "^description:" | cut -d: -f2-)
  echo "SKILL|$name|$description"
done

Build skill registry:

Skill	Triggers On
(dynamically populated from SKILL.md descriptions)

3. Discover Available Agents

Scan agent paths:

# User agents (organized by category)
find ~/.claude/agents -name "*.md" 2>/dev/null

# Plugin agents
find ~/.claude/plugins -path "*/agents/*.md" 2>/dev/null

Extract agent metadata from each .md file:

for agent_file in $(find ~/.claude/agents ~/.claude/plugins -path "*/agents/*.md" -o -name "*.md" -path "*/.claude/agents/*" 2>/dev/null); do
  # Extract frontmatter
  name=$(sed -n '/^---$/,/^---$/p' "$agent_file" | grep "^name:" | cut -d: -f2- | xargs)
  description=$(sed -n '/^---$/,/^---$/p' "$agent_file" | grep "^description:" | cut -d: -f2-)
  category=$(dirname "$agent_file" | xargs basename)
  echo "AGENT|$name|$category|$description"
done

Agent paths:

~/.claude/agents/design/*.md      → design agents
~/.claude/agents/research/*.md    → research agents
~/.claude/agents/review/*.md      → review agents
~/.claude/agents/workflow/*.md    → workflow agents
~/.claude/plugins/**/agents/*.md  → plugin agents

Build agent registry from discovery:

Agent	Category	Use When
(dynamically populated from agent .md descriptions)

4. Match Skills to Stories

For each story in prd.json:

For story in prd.stories:
  matched_skills = []
  matched_agents = []

  # Match by category
  if story.category == "ui":
    matched_skills += ["frontend-design", "emil-design-engineering", "web-design-guidelines"]
    matched_agents += ["design-implementation-reviewer"]

  if story.category == "performance":
    matched_agents += ["performance-oracle"]

  if story.category == "integration":
    matched_agents += ["security-sentinel", "silent-failure-hunter"]

  if story.category == "edge-case":
    matched_agents += ["silent-failure-hunter"]

  # Match by breadboard presence
  if spec_has_breadboard:
    matched_agents += ["breadboard-reflection"]

  # Match by keywords in title/acceptance_criteria
  keywords = extract_keywords(story.title + story.acceptance_criteria)

  for skill in discovered_skills:
    if skill.triggers_match(keywords):
      matched_skills.append(skill.name)

  # Match by tech stack (detected from spec.md)
  if "react" in tech_stack or "next" in tech_stack:
    matched_skills += ["vercel-react-best-practices"]
    if "component" in keywords:
      matched_skills += ["vercel-composition-patterns"]

  if "animation" in keywords or "transition" in keywords:
    matched_skills += ["web-animation-design"]

  if "stripe" in keywords or "payment" in keywords:
    matched_skills += ["stripe-best-practices"]
    matched_agents += ["security-sentinel"]

  if "form" in keywords or "input" in keywords:
    matched_skills += ["emil-design-engineering"]

  # Update story
  story.skills = dedupe(matched_skills)
  story.validation_agents = dedupe(matched_agents)

5. Apply Relevant Skills

For each unique skill matched to any story:

skill: [skill-name]

Extract concrete recommendations for the plan.

5.5. Breadboard Validation (Conditional)

Gate: Only run if spec.md contains breadboard affordance tables (UI Affordances, Code Affordances).

Validation checks:

• Every UI affordance (U) has at least one prd.json story covering it
• Every Code affordance (N) is referenced or implied by a story
• Every prd.json story maps to at least one breadboard affordance
• Flag gaps: affordances with no story coverage (missing from plan)
• Flag horizontal stories: stories with no affordance mapping (story may be horizontal, not vertical)

Output: Add validation results to spec.md Enhancement Summary section.

Also: Add breadboard-reflection to the agent discovery registry so it can be matched to stories that reference breadboard affordances.

6. Query Framework Documentation

Use Context7 for frameworks/libraries detected:

mcp__plugin_context7_context7__resolve-library-id: Find ID for [framework]
mcp__plugin_context7_context7__query-docs: Query specific patterns

7. Run Targeted Review Agents

Only run 2-3 agents most relevant to plan content.

Select based on:

• Story categories (many ui → design agents, any security → security-sentinel)
• Risk level (payments, auth, data → security + architecture)
• Complexity (many stories → architecture-strategist)

Task [agent-name]: "Review this plan: [spec.md content]"

Run matched agents in parallel.

8. Enhance spec.md

For relevant sections, add:

### Research Insights

**Best Practices:**
- [Concrete recommendation from skill/agent]

**Implementation Details:**
```typescript
// Code example from framework docs

Edge Cases:

• [Case and handling]

References:

• [URL from Context7 or agent research]

### 9. Update prd.json

Update each story with discovered skills and agents:

```json
{
  "id": 1,
  "title": "User can create account form",
  "category": "ui",
  "skills": ["frontend-design", "emil-design-engineering", "vercel-react-best-practices"],
  "validation_agents": ["design-implementation-reviewer", "code-reviewer"],
  ...
}

10. Add Enhancement Summary

At top of spec.md:

## Enhancement Summary

**Deepened:** YYYY-MM-DD
**Skills discovered:** [count] available, [count] matched
**Agents consulted:** [list]

### Key Improvements
1. [Improvement]
2. [Improvement]

### Skills Applied to Stories
| Story | Skills | Validation Agents |
|-------|--------|-------------------|
| #1 Create account form | frontend-design, emil-design-engineering | design-implementation-reviewer |

11. Write Updates

• Update spec.md with research insights
• Update prd.json with skills and validation_agents

Discovery Reference

Skill Paths

~/.claude/skills/*/SKILL.md
~/.claude/plugins/cache/**/skills/*/SKILL.md
~/.claude/plugins/marketplaces/**/skills/*/SKILL.md

Agent Paths

~/.claude/agents/design/*.md        → design agents (figma-design-sync, design-iterator, etc.)
~/.claude/agents/research/*.md      → research agents (best-practices-researcher, etc.)
~/.claude/agents/review/*.md        → review agents (security-sentinel, performance-oracle, etc.)
~/.claude/agents/workflow/*.md      → workflow agents (spec-flow-analyzer)
~/.claude/plugins/cache/**/agents/*.md
~/.claude/plugins/marketplaces/**/agents/*.md

Plugin agents include:

• pr-review-toolkit: code-reviewer, silent-failure-hunter, code-simplifier, comment-analyzer, pr-test-analyzer, type-design-analyzer
• feature-dev: code-explorer, code-architect
• plugin-dev: agent-creator, skill-reviewer, plugin-validator
• hookify: conversation-analyzer

Frontmatter Format (Skills & Agents)

---
name: skill-or-agent-name
description: When to use this. Triggers on: keyword1, keyword2, ...
---

The description field contains trigger keywords - use these for matching.

Category → Default Mappings

Category	Default Skills	Default Agents
functional	(tech-stack based)	code-reviewer
ui	frontend-design, emil-design-engineering, web-design-guidelines	design-implementation-reviewer
integration	(service-specific)	security-sentinel, silent-failure-hunter
edge-case	-	silent-failure-hunter
performance	vercel-react-best-practices	performance-oracle

Tech Stack → Skill Mappings

Tech Detected	Skills
React, Next.js	vercel-react-best-practices
Component architecture	vercel-composition-patterns
Animation, transition, motion	web-animation-design
Stripe, payments	stripe-best-practices
Form, input, validation	emil-design-engineering
Browser automation	agent-browser
Code search, AST	ast-grep

Post-Enhancement Options

Ask user:

1. View changes - Show what was added to spec.md and prd.json
2. Start /workflows:work - Begin implementation
3. Deepen specific story - Run more research on one story
4. Re-run discovery - Scan for new skills/agents

NEVER CODE! Just research and enhance the plan.