全部能力

掌握 OpenAPI 3.1 的 API 文档编写，结合 AI 驱动工具与现代开发者体验实践。创建交互式文档，生成 SDK，并构建…

Research competitors and compare positioning, messaging, content strategy, and market presence. Use when analyzing a competitor, building battlecards,…

Research Engineer

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…

Design a diary study plan with prompts, duration, participant criteria, and analysis framework. Use when you need to understand user behavior over time in…

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

Create developer handoff specifications with measurements, behaviors, assets, and edge cases.

优化 App Store 产品页面以提升搜索可见度和转化率。涵盖 App Store Optimization（ASO）策略、关键词研究及关键词字段优化…

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.

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…

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

Create structured plans for multi-step tasks -- software features, research workflows, events, study plans, or any goal that benefits from breakdown. Also…

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

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.

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

Read and send email via IMAP/SMTP. Check for new/unread messages, fetch content, search mailboxes, mark as read/unread, and send emails with attachments. Works…

NousResearch Hermes Agent 通用集成 Skill。通过 CLI 调用 Hermes Agent 的核心能力： - 🚀 自改进技能系统（从任务中自动创建可复用技能） - 🧠 持久化记忆（FTS5全文搜索 + LLM摘要） - 🤖 子代理委托（任务隔离和并行处理） - 🔌 MCP 双向集成 - 🌐 浏览器自动化 - 💻 代码执行 - 🔍 网页研究 - 📦 完全可移植，支持任意 Claw/WorkBuddy 实例一键安装

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.

Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or…