全部能力

Use this skill when the user wants a systematic literature review, survey, or synthesis across multiple academic papers on a topic. Also covers annotated…

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,…

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

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.

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…

Visualizes Azure infrastructure from ARM templates, Azure CLI, or descriptions. Use when user has Azure resources to diagram.

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

App Store Optimization (ASO) 工具包，用于研究关键词、分析竞争对手排名、生成元数据建议，以及提升应用在…上的可见性

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…

Use when auditing the codebase through the evaluation platform with mandatory research, coordinated domain audit workers, and structured summaries.

Generate comprehensive hierarchical codemaps for UNFAMILIAR repositories. Expensive operation - only use when explicitly asked for codebase documentation or…

Research agent for external documentation, best practices, and library APIs via MCP tools

Configure PostgreSQL with pgvector for GrepAI. Use this skill for team environments and large codebases.

Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends,…

Fetch structured stock sentiment across Reddit, X.com, news, and Polymarket using the Adanos Finance API. Use this skill whenever the user asks how much people are talking about a stock, how hot a ticker is on social platforms, how many Polymarket bets exist for a company, whether sources are aligned, or to compare stock sentiment across multiple tickers. Triggers include: "social sentiment on TSLA", "how hot is NVDA on X.com", "how many Reddit mentions does AAPL have", "compare sentiment on AMD vs NVDA", "how many Polymarket bets on Microsoft", "is Reddit aligned with X on META", "stock buzz", "bullish percentage", and any mention of cross-source stock sentiment research. This skill is READ-ONLY and does not place trades or modify anything.

Creates complete project documentation system (project docs, reference, tasks, tests). Use when bootstrapping docs from scratch or regenerating all.