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Found 2,706 Skills
Alibaba Cloud MaxCompute Cost Analysis Skill. Analyze MaxCompute pay-as-you-go costs including billing, storage metrics, and compute metrics. Triggers: "maxcompute cost", "odps cost", "maxcompute billing", "maxcompute费用", "成本分析", "费用分析", "存储用量", "计算用量", "费用突增", "SQL签名", "SQL signature", "重复SQL", "扫描量最大", "daily billing details", "每日账单明细", "按计费项", "billing by fee item".
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
Logic coherence pass for per-H3 section files: enforce a clear paragraph-1 thesis and surface paragraph-island risks (connector stats are diagnostic, not a quota) before merging. **Trigger**: logic polisher, section logic, thesis statement, connectors, 段落逻辑, 连接词, 论证主线, 润色逻辑. **Use when**: `sections/S*.md` exist but read like paragraph islands; you want a targeted, debuggable self-loop before `section-merger`. **Skip if**: sections are missing/thin (fix `subsection-writer` first) or evidence packs/briefs are scaffolded (fix C3/C4 first). **Network**: none. **Guardrail**: do not add new citations; do not invent facts; do not change citation keys; do not move citations across subsections.
Guides the agent through running and writing Python tests with pytest. Triggered when users say "run tests", "write a test", "test this function", "add unit tests", "run pytest", "check test coverage", "debug failing test", "create test fixtures", "mock a dependency", or mention pytest, pytest-asyncio, pytest-cov, testing, unit tests, integration tests, test coverage, or test-driven development.
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
dontbesilent Folder Knowledge Base. Transform users' existing local folders into a knowledge base that Agents can reliably search, archive, and maintain; build a minimal knowledge base when users have no materials, generate a knowledge base navigation when materials exist, and support subsequent functions such as adding new materials, searching for answers, identifying current versions, and checking the health status of the knowledge base. Use this whenever users mention phrases like "build a knowledge base", "my folder is the knowledge base", "let AI understand these files", "put materials into the knowledge base", "find things from the knowledge base", "which file is the latest version", "materials are too messy", "establish a Source of Truth". Users don't need to understand Source of Truth, RAG, or Agent configurations. Folder-based knowledge base for AI agents. Use whenever the user wants to build, populate, query, organize, audit, or connect a local folder as a knowledge base, including source-of-truth navigation and version resolution.
Discover how to leverage SQLite's JSON support to build a NoSQL-like document store, complete with TTL-based expiration, within this powerful embedded database.
Builds custom drag-and-drop tools for the Unlayer editor — registering tools, adding property editors, creating custom widgets, head CSS/JS injection, tool configuration, and the custom# prefix convention.
Use when needing point-in-time recovery, version control for object storage, or creating isolated bucket copies for testing/experimentation
Generic test writing discipline: test quality, real assertions, anti-patterns, and rationalization resistance. Use when writing tests, adding test coverage, or fixing failing tests for any language or framework. Complements language-specific skills.
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.