optimizing-large-skills
Systematic methodology to reduce skill file size through externalization, consolidation, and progressive loading patterns. Use when skills exceed 300 lines, multiple code blocks (10+) with similar functionality, heavy Python inline with markdown, functions >20 lines embedded. Do not use when skill is under 300 lines and well-organized. creating new skills - use modular-skills instead. Consult this skill when skills-eval shows "Large skill file" warnings.
Loading State Tester
Verify loading indicators, skeleton screens, and progress bars appear correctly during async operations and disappear on completion or error.
large-scale-map-visualization
Master of high-performance web map implementations handling 5,000-100,000+ geographic data points. Specializes in Leaflet.js optimization, Supercluster algorithms, viewport-based loading, canvas rendering, and progressive disclosure UX patterns.
conditional-docs-setup
Set up conditional documentation loading to prevent context pollution. Use when organizing project docs, implementing progressive disclosure, or reducing CLAUDE.md token consumption with on-demand loading.
context-hierarchy-design
Design memory hierarchy with progressive loading for optimal context management. Use when organizing CLAUDE.md imports, implementing just-in-time context loading, or designing priming hierarchies for agents.
mcp-context-optimizer
Optimize MCP tool context loading through progressive disclosure and hierarchical indexing. Use when working with large MCP tool sets (e.g., Miyabi's 172 tools) to prevent context window bloat. Triggers include "find tool", "search mcp", "tool lookup", "which tool", "context optimization", or when dealing with many MCP tools.
skill-optimizer
Optimize Claude Code skills for token efficiency using progressive disclosure and content loading order. Use when optimizing skills, reducing token usage, restructuring skill content, improving skill performance, analyzing skill size, applying 500-line rule, implementing progressive disclosure, organizing reference files, optimizing YAML frontmatter, reducing context consumption, improving skill architecture, analyzing token costs, splitting large skills, or working with skill content loading. Covers Level 1 (metadata), Level 2 (instructions), Level 3 (resources) loading optimization.
Bundle Optimizer
Analyze and reduce bundle size, implement code splitting, optimize dependencies, and improve build performance. Use when bundle is too large, load times are slow, or implementing progressive loading.
test-skill-multifile
Test skill demonstrating progressive file loading with multiple resources
spring-boot-scanner
Smart code scanner that detects Spring Boot patterns and routes to appropriate skills. Use when editing Java or Kotlin files in Spring Boot projects, working with pom.xml/build.gradle containing spring-boot-starter, or when context suggests Spring Boot development. Detects annotations (@RestController, @Entity, @EnableWebSecurity, @SpringBootTest) to determine relevant skills and provides contextual guidance. Uses progressive automation - auto-invokes for low-risk patterns (web-api, data, DDD), confirms before loading high-risk skills (security, testing, verify).
30-00-refactor-index
Refactoring primer index. Read this first before any refactor — it points you to the right guide for your situation. Progressive exploration, not bulk loading.
40-00-architecture-index
Architecture primer index. Read this first before any structural work — it points you to the specific principle you need. Progressive exploration, not bulk loading.
skill-optimizer
Optimizes existing Claude Code skills for token efficiency using layered loading, content classification, and progressive disclosure. Use when optimizing skills, reducing token usage, reviewing skill efficiency, refactoring large/bloated SKILL.md files, auditing a skill repo for optimization opportunities, or when user mentions skill optimization, skill review, reduce tokens, skill efficiency, or bloated skill. Invoked during skill creation (post-create optimization), skill intake (post-adapt/add optimization), or standalone skill review.