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Establish a naming convention system for design elements, components, and tokens with clear rules and examples.

Craft portfolio-ready case studies that tell the story of a design project.

You are a documentation expert specializing in creating comprehensive, maintainable documentation from code. Generate API docs, architecture diagrams, user…

Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want…

B2B case study writing with STAR framework, data visualization, and research. Covers structure, customer quotes, metrics presentation, and distribution…

Comprehensive market research and analysis skill. Use when sizing markets, conducting competitive analysis, generating professional market research reports, analyzing consumer behavior, or identifying market opportunities. Activates for: market research, market analysis, market report, TAM SAM SOM, market sizing, industry analysis, market landscape, competitive landscape, market trends, consumer research, Porter's Five Forces, PESTLE, SWOT, BCG Matrix, McKinsey-style report, consulting report, market opportunity, industry report, due diligence, M&A analysis, GTM market analysis, product-market fit validation.

Write clear design rationale connecting decisions to user needs, business goals, and principles.

Define and organize design tokens (color, spacing, typography, elevation) with naming conventions and usage guidance.

Design system feedback for user actions including confirmations, status updates, and notifications.

Sports news via RSS/Atom feeds and Google News. Fetch headlines, search by query, filter by date. Covers football news, transfer rumors, match reports, and any sport via Google News. Use when: user asks for recent news, headlines, transfer rumors, or articles about any sport. Good for "what's the latest on [team/player]" questions. Supports any Google News query and curated RSS feeds (BBC Sport, ESPN, The Athletic, Sky Sports). Don't use when: user asks for structured data like standings, scores, statistics, or xG — use the sport-specific skill instead: football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), tennis-data (tennis), golf-data (golf), cfb-data (college football), cbb-data (college basketball), or fastf1 (F1). Don't use for prediction market odds — use polymarket or kalshi. News results are text articles, not structured data.

Create a holistic experience map showing the full ecosystem of user touchpoints, channels, and relationships.

Cubox CLI is a callable personal reading memory system that enables you to search, read, and use saved content, perform semantic (RAG-based) queries, access…

Create stakeholder alignment artifacts including responsibility matrices, decision frameworks, and communication plans.

Design team workflows covering task management, collaboration rituals, and tooling.

Identify, evaluate, and prioritize design opportunities using impact-effort frameworks and strategic criteria.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function…

Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search,…

Finds and recovers content from Claude Code session history files. This skill should be used when searching for deleted files, tracking changes across…

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.