「openai」の検索結果

Braintrust is a hosted platform for evaluating and tracing LLM applications: it runs a task over a dataset, scores the outputs, stores each run as an experiment and logs production traces. Use when a user asks to write or run a Braintrust eval, score LLM outputs with autoevals, compare prompt or model versions as experiments, run evals in CI and fail on regressions, trace OpenAI or Anthropic calls with Braintrust, manage evaluation datasets, or use the bt CLI.

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Cerebras Inference is a hosted LLM API that runs open models on Cerebras wafer-scale chips at thousands of tokens per second, through an OpenAI-compatible endpoint. Use when a user wants the lowest-latency chat, code completion or agent loop, asks how to call the Cerebras API or SDK, needs streaming, tool calling, strict JSON schema output or reasoning control, or hits a "model not found" error from a retired Cerebras model ID.

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Arize is an AI observability platform, and Phoenix is its open-source tracing and evaluation tool for LLM applications. Use this skill when the user wants to trace OpenAI or LangChain calls, run a local Phoenix server, score RAG answers for faithfulness, debug slow or wrong LLM responses, or send OpenTelemetry traces to Arize for production monitoring.

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LLM application architecture expert for RAG, prompting, agents, and production AI systemsUse when "rag system, prompt engineering, llm application, ai agent, structured output, chain of thought, multi-agent, context window, hallucination, token optimization, llm, rag, prompting, agents, structured-output, anthropic, openai, langchain, ai-architecture" mentioned.

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Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.

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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementations. The LLM never sees your code - it only sees the schema and description. Use when "agent tool, function calling, tool schema, tool design, mcp server, mcp tool, tool use, build tool for agent, define function, input_schema, tool_use, tool_result, agents, tools, function-calling, mcp, json-schema, anthropic, openai, llm-tools" mentioned.

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Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just "ChatGPT but different" - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when "AI wrapper, GPT product, AI tool, wrap AI, AI SaaS, Claude API product, " mentioned.

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🕹️ Autonomous Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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🕹️ Autonomous Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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ANDREJ KARPATHY — SKILL COMPLETA v2.0 workflow skill. Use this skill when the user needs Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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ANDREJ KARPATHY — SKILL COMPLETA v2.0 workflow skill. Use this skill when the user needs Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Bret Taylor 视角. Sierra 联合创始人 / CEO, OpenAI 董事会主席 (2024-), ex-Salesforce co-CEO, Google Maps 联合创造者. B2B SaaS enterprise 派代表 / outcome-based pricing 第一手提倡者 / brand-as-moat 在 agent 时代重新阐述者. 把 monetize-agents 语境从 "卖软件 access" 换成 "卖 outcome — 客户问题解决一次收一次费", 把 product 设计从 "feature breadth" 换成 "vertical depth + measurable outcome + procurement-grade trust". 用途: 当用户问 "agent 该怎么定价 / per-seat 还是 per-outcome / B2B agent 怎么 GTM / enterprise 0 到 1 / vertical 还是 horizontal / design partner 怎么搭 / 一开始要不要融大钱" 类问题时, 用 Bret Taylor 视角先把 "outcome 可不可测量 + vertical 是哪个 + 第一批 design partner 是谁" 盘清楚, 再给定价 / GTM / 融资判断. 当用户提到 "Bret Taylor" "Sierra" "outcome-based pricing" "deflection" "vertical agent" "design partner" "enterprise agent" "B2B agent GTM" "agent 定价" "agent procurement" 时使用. 即使用户只是说 "用 Sierra 思路看一下" 也应触发.

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Universal environment variable loader for AI agent environments. Loads secrets and config from Claude.ai, Claude Code, OpenAI Codex, Jules, and standard .env files.

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Run coding tasks via opencode using free cloud models. Use when asked to offload work to opencode.ai or run a free model. Don't use for local models (Ollama, LM Studio), Claude/OpenAI calls, or when Claude should do the work itself.

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Use when the user has audio or video and wants a timestamped transcript (SRT) in the source language. Routes by source language — Chinese defaults to Volcano (豆包) ASR; other languages (Spanish, English, Portuguese, French, Italian, Japanese, Korean, etc.) use OpenAI Whisper API with word-level timestamps and self-assembled cues. Outputs SRT with punctuation-bounded cues capped for on-screen reading. Triggers — "转写", "转成字幕", "做 SRT", "transcribe", "make subtitles", "speech to text", "出字幕".

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Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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Inspect, stream, and explicitly adjust live Android UI tweak values or invoke app-owned actions through Snap-O. Use when a request mentions Snap-O Tweaks, tweak-enabled Android apps, runtime UI parameters, Compose tweak or action controls, tweak discovery, previously adjusted or currently inactive tweaks, applying all user-made tweak changes, typed values, defaults or resets, tweak batches, explicitly registered actions, the Tweaks REST/SSE API, or selecting between CLI, macOS tool, in-app overlay, and custom tweak interfaces.

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Inspect Android network captures and intercept HTTP calls with the Snap-O CLI for a selected device/socket. Use for request/response details, websocket events, API response overrides, mock responses, or delays with Python route handlers.

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Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.

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Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever, ToolsRetriever), external vector DB retrievers (Weaviate, Pinecone, Qdrant), retrieval_query Cypher fragments, query_params, filters, GraphRAG pipeline wiring (GraphRAG + LLM + prompt), all LLM providers (OpenAI, Anthropic, Gemini/VertexAI, Bedrock, Cohere, Mistral, Ollama), embedder setup, index creation, token usage tracking, Cypher 25 SEARCH clause, and LangChain/LlamaIndex integration. Does NOT handle KG construction — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.

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Reference guide for connecting popular APIs to Home Assistant via Node-RED, YAML, or custom integrations. Covers authentication, endpoints, and complete working examples for: energy APIs (Tibber, Nordpool), weather (SMHI, OpenWeatherMap, yr.no), transport (SL, Trafikverket, Resrobot), smart home clouds (Shelly, Tuya, Philips Hue, IKEA), and global APIs (OpenAI, Spotify, Google Calendar, Telegram, GitHub). Use this skill whenever the user mentions a specific external service, API, or data source they want to connect to Home Assistant - even if they don't say "API".

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OpenAI Codex CLI wrapper — three modes. (gstack)

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代码仓库 AI 亲和度审计工具，支持前端、后端、全栈等各类项目。此技能用于检查给定的代码仓库对 AI Coding 工具的友好程度，生成详细的分析报告和改进建议。当用户需要评估代码仓库是否适合 AI 辅助开发、希望提升仓库的 AI 可操作性、或准备引入 AI Coding 工具前进行仓库评估时，应使用此技能。支持 TypeScript/JavaScript、Go、Java/Kotlin、Python、Rust 等主流技术栈。融合 OpenAI Harness Engineering 方法论，评估 Outer Loop（反馈闭环、评估门禁、机械化不变量）建设。

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