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name: marketplace-analysis description: Use when reviewing plugin quality, auditing plugins, analyzing the marketplace, checking plugins against Anthropic standards, or evaluating plugin architecture - provides systematic analysis methodology with validation framework

Marketplace Analysis

Analyze Claude Code plugins to achieve Anthropic-level quality standards.

Core Philosophy

Anthropic Quality Bar: Same or more functionality with leaner, more efficient implementation.

Principles:

• Systems thinking over point fixes
• Elegant simplicity over feature accumulation
• Proven improvements over assumptions
• Deletion over addition

Analysis Process

1. Quick Scan

• Count plugins and components
• Note obvious issues (large files, naming inconsistencies)
• Flag files >500 lines

2. Deep Analysis (per plugin)

1. Read SKILL.md files - check trigger phrases, writing style
2. Read agent descriptions - check triggering examples
3. Read commands - check argument handling
4. Check hooks - validate event usage
5. Map interactions - how components work together

3. Cross-Plugin Analysis

• Find redundancy across plugins
• Check consistency (naming, patterns, styles)
• Identify gaps and conflicts

4. Reference Validation

For each skill, verify bundled references exist:

1. Extract paths from SKILL.md:

   • references/*.md mentions
   • scripts/*.sh or scripts/*.py mentions
   • Markdown links: [text](relative/path)

2. Validate each path:

   • Resolve relative to skill directory
   • Check file exists with Glob
   • Flag missing as "broken reference"

3. Report:

   • Missing references = Priority 1 errors
   • Orphaned files (exist but not referenced) = Priority 3 notes

Anti-Overengineering Checks

Before proposing ANY change:

1. Is this simpler than the original?
2. Does this solve a real problem?
3. Would a new user understand this?
4. Can I remove instead of add?

Red flags:

• Adding abstraction for one use case
• "Might need this later" reasoning
• Recommending deletion based on filename alone

Output Format

Copy## Priority 1: High Impact, Low Effort
- [ ] [Change] - [Why] - [Expected impact] - [How to validate]

## Priority 2: Medium Impact
...

## Priority 3: Consider Later
...

Each recommendation includes validation approach.

References

For detailed guidance:

• references/skill-design-standards.md - Official Anthropic skill-creator guide (authoritative source for skill structure, frontmatter, progressive disclosure)
• references/quality-standards.md - Quality criteria checklist, anti-patterns (includes summary of official standards)
• references/measuring-improvements.md - Metrics, user testing, validation templates
• references/output-patterns.md - Template and examples patterns for consistent output
• references/workflows.md - Sequential and conditional workflow patterns

Use scripts/analyze-metrics.sh for consistent metric collection.

Consulting Documentation

Verify best practices via claude-code-guide subagent before claiming something is "wrong."

Applying Changes

When implementing improvements:

1. Before any changes: Create TodoWrite items for each improvement
2. Apply changes: Use Edit tool, one logical change at a time
3. MANDATORY verification: Use core:verification skill before claiming complete
4. Evidence required: Run validation commands, report actual output

Never claim "improved" or "fixed" without verification evidence.