全部能力

RAG and search engineering — chunking, hybrid retrieval, reranking, and nDCG evaluation. Use when building retrieval-augmented generation pipelines.

Expert technical documentation specialist for developer docs, API references, and runbooks. Activate on: documentation, docs, README, API reference, technical…

在观察行为层面进行用户研究规划与综合——生成带有置信度评级的候选用户故事

Analyze earnings call transcripts to extract key insights about future guidance, strategic priorities, management commentary, and market signals.

Real-time web search and page reading using Aliyun IQS APIs. Use this skill FIRST when the user needs current information, news, facts verification, URL…

Search and retrieve academic papers from arXiv.org using WebFetch and Exa. No MCP server required - uses existing tools to access arXiv API directly.

Expert knowledge for Azure Container Apps development including troubleshooting, best practices, decision making, architecture & design patterns, limits &…

Create research posters using HTML/CSS that can be exported to PDF or PPTX. Use this skill ONLY when the user explicitly requests PowerPoint/PPTX poster…

Documentation templates and structure guidelines. README, API docs, code comments, and AI-friendly documentation.

Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.

Generates comprehensive documentation templates for open-source and internal projects including README, CONTRIBUTING, SECURITY, CODE_OF_CONDUCT, LICENSE, and…

Comprehensive knowledge of GitHub Copilot SDK for embedding Copilot's agentic workflows in Python, TypeScript, Go, and .NET applications. Auto-activates for…

Implements knowledge graphs for AI-enhanced relational knowledge. Covers ontology design, graph database selection (Neo4j, Neptune, ArangoDB, TigerGraph), entity extraction, hybrid graph-vector architecture, query patterns, and AI integration. Use when implementing knowledge graphs, designing ontologies, extracting entities and relationships, selecting a graph database, or building hybrid graph-vector search. Use for knowledge graph, ontology design, entity resolution, graph RAG, hallucination detection. For architecture selection and governance, use the knowledge-base-manager skill. For document retrieval pipelines, use the rag-implementer skill.

Generate research questions from economic phenomena

Research and investigate business prospects and leads. Gathers company information, contact details, and qualification data for sales.

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering,…

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating…

Search and manage Confluence pages and spaces using CQL, read/create/update pages with Markdown support. Use when working with Confluence documentation.

Use when 需要在 Spec 级设计阶段执行 D1 research（产出 `{FEATURE_DIR}/design/research.md`），或面对关键不确定性/高风险点需要先验证而不是直接进入 D2；常见症状包括缺少证据支撑取舍、未知项被写成 TODO/待确认问题、在压力下想猜 FEATURE_DIR…

Generate a production-ready AbsolutelySkilled skill from any source: GitHub repos, documentation URLs, or domain topics (marketing, sales, TypeScript, etc.). Triggers on /skill-forge, "create a skill for X", "generate a skill from these docs", "make a skill for this repo", "build a skill about marketing", or "add X to the registry". For URLs: performs deep doc research (README, llms.txt, API references). For domains: runs a brainstorming discovery session with the user to define scope and content. Outputs a complete skill/ folder with SKILL.md, evals.json, and optionally sources.yaml, ready to PR into the AbsolutelySkilled registry.