'age' 검색 결과

Automate Agenty tasks through Rube MCP, a connection to external tools. The skill discovers available operations and their required inputs, checks the Agenty connection, and executes tasks using the tools it finds.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding available Agenty operations
• Running Agenty tasks through an agent
• Processing multiple Agenty operations together

Configure Django file storage on AWS S3 using django-storages and boto3. Separate public and private files, create temporary download or upload links, configure CloudFront delivery, and test storage behavior without accessing real S3 buckets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Moving Django static files and uploads to S3
• Separating public files from private documents
• Creating temporary download and direct upload links

Turn an idea, existing code, or a previous Codex conversation into a runnable OpenAI Agents SDK app. Supports local verification, focused evaluations of agent behavior, and local deployment through the Deployment Manager.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a runnable agent prototype from an idea
• Adapt existing code into an agent app
• Turn a previous Codex conversation into an app

Manage a shared skill library for Claude Code, Codex, Cursor, and other agents. Search, install, update, and deploy skills to selected agents, organize reusable groups and tags, and check where each skill is available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy shared skills to Claude Code and Codex
• Check and update installed skills
• Organize skills with tags and reusable groups

Switch the Manager Agent's primary language model with a script that checks connectivity before saving configuration. You can also set how much context the model accepts and whether extended thinking is enabled.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Change the Manager's saved primary model
• Correct a model's context window setting
• Disable extended thinking for a model

Set up dependency-cruiser in a TypeScript repository to enforce package boundaries: outside code uses public entry files while implementation stays in subfolders. Add checks, a starter package, and documentation, then verify that forbidden imports fail.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preventing imports into package internals
• Adding boundary checks to existing validation commands
• Creating a starter package with public entry files

Build VeADK Agent code from functional requirements, including agent structure and prompt refinement. Convert existing Langchain or Langgraph code, or a Dify workflow, into VeADK Agent code and save the generated Python files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating a VeADK Agent from functional requirements
• Converting Langchain code to VeADK Agent code
• Converting Langgraph code to VeADK Agent code

Generate VeADK-Go Agent code from a description of the required functionality, or convert existing Enio Agent code using the supplied framework documentation, examples, and feature mapping rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating Agent code from functional requirements
• Converting existing Enio code to VeADK-Go
• Developing Agents with framework reference examples

Improve a Browserbase Agent's system prompt by running a fixed task, examining messages and logs, and changing one instruction at a time. Compare quality, duration, and message counts, then repeat runs to check consistency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refining prompts for a Browserbase Agent demo or proof of concept
• Diagnosing inconsistent Agent runs from recorded evidence
• Comparing prompt revisions by quality, duration, and message count

Use when: AI teammate / AIチームメイト / AI チームメイト / Agent Template / Foundry Autopilot / Agent 365 / 同僚エージェントの開発・公開。既定は Foundry hosted agent を Agent template として発行する経路。publish_foundry_autopilot.py が必須で、汎用 scripts/deploy.py や SDK agents.create_version だけで完了にしない。明示的な Agent Template / Autopilot 要求の前提が不足したら停止し、通常 Agent・自己ホストへの自動フォールバックは禁止。明示選択の自己ホストは別経路として対応する。M365 Requests の Type=Agent template と Teams Agents for your team での hire を手動確認するまで未完了。初回 scaffold で評価Hub（Code Apps）・Python EvaluationWorker・回帰テスト、publish 時に Foundry Monitor/Evaluations を構成する。CI/CD・レビューゲートは alm に委譲する。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when deploying an Agentforce agent to a channel surface: Embedded Service web chat, Slack, Experience Cloud, Agent REST API, or Salesforce Mobile. Triggers: 'deploy agent to website', 'add Agentforce to Slack', 'enable agent for mobile app', 'configure embedded service for agent', 'agent API for custom integration', 'multi-channel agent deployment'. NOT for agent logic design — use agentforce/agent-topic-design or agentforce/agent-actions for topics, instructions, and action contracts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Improve agent reliability over time — diagnose why agents fail and fix the setup. Triggers on: agent keeps failing, same mistake again, agent not improving, make agent smarter, agent quality plateau, agents ignore skills, agent skips tests, fix agent behavior, agent unreliable, improve agent setup, self-improving harness, agents worse over time, tune agent instructions, agent going in circles, agent ignores AGENTS.md, repeated agent errors. Requires harness v0 and eval harness. AUTO-ROUTED from harness-engineering on symptoms. Not first setup — harness-generation first.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

커밋 직전에 파일을 정리하고 기존 타입 검사와 테스트를 실행하도록 설정합니다. Husky와 lint-staged의 설치부터 설정 파일 작성과 동작 확인까지 다룹니다.

• 커밋 전 자동 검사 도입
• Prettier로 커밋 대상 파일 정리
• 기존 타입 검사와 테스트 실행

구상이나 기존 저장소를 바탕으로 AI 에이전트의 역할, 보고 체계, 작업 인계를 정리하고 규격에 맞는 회사 또는 팀 패키지를 만듭니다.

• 구상에서 에이전트 회사를 만듭니다
• 저장소를 팀 구성으로 정리합니다
• 역할 분담과 작업 인계를 설계합니다

OpenAI의 Image API로 UI 목업, 아이콘, 일러스트, 소셜 미디어 카드와 시각 참고 자료를 생성하고 편집하는 스킬을 안내하는 카탈로그 항목입니다.

• UI 목업 이미지 제작에 활용합니다
• 아이콘과 일러스트를 생성합니다
• 소셜 미디어 카드 이미지를 준비합니다

Callpage 작업을 AI 에이전트에 맡기도록 사용 가능한 도구와 최신 입력 규격을 조회합니다. Rube MCP를 통해 연결 확인부터 실행, 일괄 처리까지 진행합니다.

• Callpage에서 지원하는 작업 조회
• 실행 전 연결 상태 확인
• 여러 작업을 한 번에 실행

Create and prepare marketing images for blogs, social posts, product showcases, and banners. Choose suitable generation or design tools, plan prompts and layouts, and optimize image dimensions, formats, and file sizes for web use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating blog headers and social sharing previews
• Preparing social graphics and profile banners
• Building product mockups from real screenshots

Automate page interactions and screenshots with agent-browser running in AWS Bedrock AgentCore cloud browsers. Use standard browser commands, preserve login state across sessions with persistent profiles, and watch active sessions through an AWS Console link.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run browser automation on AWS
• Reuse login state across browser sessions
• Watch a cloud browser session live

Design autonomous agents for Claude Code plugins, including when they should run, what they should do, and which tools they may use. Provides file templates and guidance for validating configuration and testing behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a code review agent
• Clarify when an agent should run
• Limit the tools an agent can access

Resize images, convert formats, create thumbnails, and inspect dimensions and metadata with ImageMagick. Process individual files or batches, selecting images by size or file type and preserving their proportions when resizing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create thumbnails from a folder of images
• Resize wallpapers for different screen sizes
• Convert image file formats

Build image classification and search workflows by comparing images with text descriptions. Covers classification without additional training, similarity scoring, batch processing, and storing image representations in a vector database for retrieval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Classifying images without additional training
• Searching image collections with text descriptions
• Comparing images with candidate descriptions