'chat run' 검색 결과

Crabbox 임시 환경에서 네이티브 앱이나 웹 앱을 실행해 채팅 사이드 패널에 표시합니다. 실행 확인, 화면 조작 테스트, 미리보기 재표시와 프로세스 종료를 지원합니다.

• 네이티브 앱 실행 및 화면 확인
• 웹 앱 미리보기
• 화면 조작을 통한 앱 테스트

OpenClaw에 전달한 자연어 요청을 Claude Code 같은 외부 코딩 에이전트로 연결합니다. ACP나 acpx로 작업을 시작하거나 이어가며 대화 맥락을 유지합니다.

• 스레드에서 Claude Code 시작
• 외부 에이전트의 기존 작업 계속
• acpx로 직접 지시 전달

1Password 명령줄 도구의 설치와 인증을 확인하고 저장된 비밀번호와 API 키를 명령이나 설정에 전달합니다. 데스크톱 앱 연동과 서비스 계정도 지원합니다.

• 1Password 명령줄 도구 설치
• API 키를 명령에 전달
• CI에서 서비스 계정 인증 사용

Hugging Face Hub에서 로컬 언어 모델을 찾고, 메모리에 맞는 GGUF 파일을 고르도록 돕습니다. 파일 용량을 비교하고 llama.cpp 실행 명령을 준비합니다.

• 메모리에 맞는 로컬 모델을 찾습니다
• GGUF 종류와 파일 용량을 비교합니다
• 로컬 채팅 서버 실행을 준비합니다

Create a morning brief as a styled HTML page using connected calendars, email, and chat. It shows the day's meeting load, items needing attention, recent resolutions, and preparation for tomorrow, with an option for recurring weekday runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Getting a visual overview of today's schedule
• Checking unanswered requests that need attention today
• Finding concrete preparation for tomorrow's meetings

Add interactive panels to an MCP server so users can select records, view charts, or follow progress inside a conversation. The skill guides tool connections, interface implementation, sandbox restrictions, and testing in hosts that support these panels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a searchable contact or file picker
• Display charts and previews inside a conversation
• Show live progress for a long-running job

Prepare datasets for language model training by formatting examples, checking conversation templates, and packing examples into training sequences. Document data sources, synthetic data ratios, and validation results in a dataset card before starting a run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert raw examples into training-ready JSONL
• Check conversation templates and assistant-only training targets
• Review example boundaries after sequence packing

Build web apps connecting a CopilotKit React interface to agents hosted in Microsoft Foundry. Covers AG-UI, the communication protocol between them, plus tool approvals, conversation history, shared state, deployment, debugging, and dependency upgrades.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a chat interface for a Foundry hosted agent
• Add approval screens for agent tool execution
• Investigate failures across the agent and web interface

Sets up shared project rules and a glossary so people and AI agents work from the same context. It preserves existing documents, introduces additional records when needed, and provides a check mode to audit inconsistencies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up shared rules for a new development project
• Moving project knowledge out of chat history
• Preparing documentation for new contributors

Use the weknora CLI to manage knowledge bases and documents, search stored material, and ask questions grounded in it. This shared guide covers authentication, structured JSON results, error handling, and previews of changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up authentication for a WeKnora server
• Managing knowledge bases and uploaded documents
• Searching stored material and asking questions about it

Compare assigned work from connected trackers with TASKS.md, review stale tasks, and fill gaps in stored context about people and projects. An optional comprehensive scan finds possible missed commitments in recent activity, with changes confirmed by the user.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review assigned tracker tasks against TASKS.md
• Triage overdue tasks and long-standing active items
• Fill missing context about people, projects, and acronyms

Add Storybook stories, examples of component states, for the Kilo VS Code extension UI. The existing Playwright runner turns them into screenshot comparisons to check visual changes across states and widths.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add screenshot coverage for chat or settings screens
• Check layouts at 420px and 200px widths
• Show permission requests using mock data

Connects supported AI clients, including Claude Code and Codex, to OpenWork through MCP, a protocol for accessing external tools. It guides account sign-in and verifies that tools for finding and running organization capabilities are available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting an AI client to OpenWork organization resources
• Signing in and verifying available MCP tools
• Switching organizations or troubleshooting authentication

Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Trace execution steps in a Python AI application
• Select tracing integrations for an existing framework
• Add manual tracing with @observe

Build and debug LangBot plugins that handle messages, commands, and AI tool calls. The skill covers SDK usage, common errors, test environment setup, WebSocket message testing, and preparation for publishing to LangBot Space.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a plugin that reacts to incoming messages
• Fix plugin SDK errors and missing responses
• Test group and private chats through WebSocket

Compare assigned work from connected trackers with TASKS.md, review stale tasks, and fill gaps in stored context about people and projects. An optional comprehensive scan finds possible missed commitments in recent activity, with changes confirmed by the user.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review assigned tracker tasks against TASKS.md
• Triage overdue tasks and long-standing active items
• Fill missing context about people, projects, and acronyms

Add animated AI activity indicators to React interfaces, with distinct states for searching, reasoning, listening, writing, and creating. The skill covers matching indicators to real tasks, choosing sizes and themes, and providing accessible status announcements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Showing search and writing states in an AI chat
• Indicating microphone input in a voice interface
• Replacing a generic loading spinner

Submit shopping questions to Amazon's Alexa/Rufus assistant and collect answers as JSON, a structured data format. Use product search results as context for category research, or run a series of general shopping questions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Collecting product recommendations for a specific category
• Gathering answers to a batch of shopping questions
• Researching deals and shopping trends

Run Weave Router locally with Codex to reproduce routing bugs and check response conversion, model selection, and routing markers. Temporary configuration keeps everyday Codex settings intact, with real providers or repeatable mock responses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reproduce a Codex routing bug locally
• Verify a router fix from request to response
• Check forced model selection and routing markers

Implement Gemini API applications in Python or TypeScript for text, image, video, and speech generation. Covers ongoing conversations, external tool calls, structured responses, background research, managed agents, and migration from the older generateContent API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a chatbot that retains conversation context
• Add image, video, or speech generation to an app
• Connect external tools and return structured data

Delegate coding tasks to Codex, Claude Code, or Pi agents via background process. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Claude Code: use --print --permission-mode bypassPermissions (no PTY). Codex/Pi/OpenCode: pty:true required.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build MCP servers for Copilot Chat using the OpenAI Apps SDK or MCP Apps SDK widget rendering support (any language). Use this skill when: - Creating MCP servers that integrate with M365 Copilot declarative agents - Building rich interactive widgets (React + Fluent UI) that render in Copilot Chat - Implementing tools that return structuredContent for widget rendering - Adapting an existing MCP server to support Copilot widget rendering - Setting up devtunnels for localhost MCP server exposure - Configuring mcpPlugin.json manifests with RemoteMCPServer runtime Do NOT use this skill for general agent development (scaffolding, manifests, deployment) — use declarative-agent-developer instead. This skill is ONLY for MCP server + widget development. Triggers: "MCP server for Copilot", "OpenAI Apps SDK", "Copilot widget", "structuredContent", "MCP plugin", "devtunnels MCP", "OAI app", "widget rendering", "UI widget"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Migrate workloads from Google Cloud Platform to AWS — plus AI and agentic workloads from any provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, migrate App Engine to Elastic Beanstalk, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform, app code, or billing exports, then clarify, design, estimate costs, generate artifacts, and collect feedback. Clarify must finish before Design, Estimate, or Generate. Do not use for: Azure migrations (see azure-to-aws), on-premises migrations, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, or GCP-to-GCP refactoring.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Have a fast, conversational analysis with the AWS DevOps Agent. Use for cost optimization, architecture review, topology mapping, knowledge / runbook discovery, security audits, dependency questions, and quick diagnostics — anything that needs a 5-30 second answer rather than a 5-8 minute deep investigation. Trigger words include cost, optimize, review, architecture, topology, what runbooks, show me, compare, audit, what if.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.