Meta-Pattern Recognition
Meta-Pattern Recognition helps identify universal principles by detecting when the same structural pattern appears across three or more distinct domains. Use this skill when solving complex problems, designing systems, or learning new fields to uncover abstract patterns like caching, layering, queuing, and pooling that can reveal proven solutions applicable to seemingly unique challenges.
git clone --depth 1 https://github.com/mrgoonie/claudekit-skills /tmp/meta-pattern-recognition && cp -r /tmp/meta-pattern-recognition/.claude/skills/problem-solving/meta-pattern-recognition ~/.claude/skills/meta-pattern-recognitionSKILL.md
# Meta-Pattern Recognition ## Overview When the same pattern appears in 3+ domains, it's probably a universal principle worth extracting. **Core principle:** Find patterns in how patterns emerge. ## Quick Reference | Pattern Appears In | Abstract Form | Where Else? | |-------------------|---------------|-------------| | CPU/DB/HTTP/DNS caching | Store frequently-accessed data closer | LLM prompt caching, CDN | | Layering (network/storage/compute) | Separate concerns into abstraction levels | Architecture, organization | | Queuing (message/task/request) | Decouple producer from consumer with buffer | Event systems, async processing | | Pooling (connection/thread/object) | Reuse expensive resources | Memory management, resource governance | ## Process 1. **Spot repetition** - See same shape in 3+ places 2. **Extract abstract form** - Describe independent of any domain 3. **Identify variations** - How does it adapt per domain? 4. **Check applicability** - Where else might this help? ## Example **Pattern spotted:** Rate limiting in API throttling, traffic shaping, circuit breakers, admission control **Abstract form:** Bound resource consumption to prevent exhaustion **Variation points:** What resource, what limit, what happens when exceeded **New application:** LLM token budgets (same pattern - prevent context window exhaustion) ## Red Flags You're Missing Meta-Patterns - "This problem is unique" (probably not) - Multiple teams independently solving "different" problems identically - Reinventing wheels across domains - "Haven't we done something like this?" (yes, find it) ## Remember - 3+ domains = likely universal - Abstract form reveals new applications - Variations show adaptation points - Universal patterns are battle-tested
Manage MCP (Model Context Protocol) server integrations - discover tools/prompts/resources, analyze relevance for tasks, and execute MCP capabilities. Use when need to work with MCP servers, discover available MCP tools, filter MCP capabilities for specific tasks, execute MCP tools programmatically, or implement MCP client functionality. Keeps main context clean by handling MCP discovery in subagent context.
Stage all files and create a commit.
Stage, commit and push all code in the current branch
Create a pull request
Create a new agent skill
Utilize tools of Model Context Protocol (MCP) servers
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.