스킬 찾기

Design autonomous coding agents with patterns inspired by Cline and OpenAI Codex. Explore tool calling interfaces, permission and approval systems, browser automation, and workflows where people review or guide the agent's actions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning an autonomous coding agent
• Designing tool and function calling interfaces
• Defining permissions and approval steps

Search Apple Notes by meaning or exact text, discover related notes and unexpected connections, and synthesize ideas with citations. The skill guides local setup and tool selection, with on-device search and optional local or cloud language models for synthesis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find past notes by topic or exact phrase
• Discover unexpected connections between notes
• Summarize ideas with citations to source notes

Add Application Insights monitoring to browser apps to record page views, clicks, network requests, and errors. Also covers custom measurements and tracing generative AI operations across browser and backend code to investigate behavior and performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitoring page views and clicks in a browser app
• Recording network requests and application errors
• Linking browser activity to backend traces

Hand a scoped coding task to Aider while the coordinating agent reviews changes, runs project checks, and commits the result. Covers task briefs, execution tracking, and follow-up edits, but the executable helper is not bundled.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegating a scoped code change to Aider
• Reviewing changes and tests before committing
• Requesting follow-up edits after a review

Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Schedule document ingestion and RAG index refreshes
• Design batch workflows for LLM requests
• Connect model training with quality evaluation

Helps design focused AI products built on APIs such as OpenAI and Anthropic. Covers product strategy, prompts, model selection, user experience, usage tracking, and cost management for tools aimed at solving specific customer problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning an AI SaaS product for a specific problem
• Designing prompts for product features
• Balancing model choices, costs, and user experience

Plan structured adversarial tests for AI applications to assess safety bypasses, data leaks, and unauthorized tool use. Organize test prompts, record findings, and prioritize fixes using reporting guidance and an adaptable testing template.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess an AI feature before release
• Compare security behavior of candidate models
• Retest after a prompt injection or jailbreak incident

Design and implement LLM applications, AI agents, and RAG systems that retrieve documents to support answers. Cover model selection, search quality, testing, monitoring, safety measures, and cost controls for applications intended for production use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an AI assistant that answers from company documents
• Improving search with keyword and vector retrieval
• Designing AI agents with multiple specialized roles

Review GitHub Actions workflows that run AI agents, tracing how external input reaches prompts and tools. Produce findings with evidence, severity, impact, and remediation guidance through static analysis, which examines configuration without running attacks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing AI agent integrations in GitHub Actions
• Tracing issue and PR text into agent prompts
• Reviewing sandbox settings and tool permissions

Review Git changes with the ocr command-line tool and a configured AI model. Get line-level findings grouped by severity for working changes, commits, or branch comparisons, with code fixes applied when explicitly requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing changes before a commit
• Checking a pull request or branch comparison
• Inspecting a specific commit for issues

Create images from text descriptions, edit existing pictures, or combine multiple images. The script selects an image service from configured API keys and saves results in the workspace, with options for size, quality, and aspect ratio.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating illustrations or icons from text
• Adding elements to an existing image
• Combining characters from multiple images

Create reusable agent skills, install them from URLs or local files, and update existing packages. Guides the structure of SKILL.md, supporting scripts and templates, naming, and optional format checks so agents can follow clearly defined workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn recurring tasks into reusable agent skills
• Install skills from URLs or local archives
• Restructure and update existing skills

Transfer known function and variable names from an older binary to a newer version. Compare disassembly and pseudocode with an LLM, produce structured mappings, and apply names and comments in IDA.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrate known function names after a software update
• Locate kernel functions when the new PDB is unavailable
• Find updated function addresses and structure offsets

Build AI applications that answer questions using retrieved documents. Covers vector databases, text embeddings that represent meaning as numbers, search strategies, and result reranking to connect language models with external knowledge.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building Q&A systems for internal documents
• Adding domain knowledge to chatbots
• Implementing natural language document search

Choose embedding models, which represent text as numerical vectors, for meaning-based search and RAG applications. Compare options for code, prose, and multilingual content, and plan document splitting, preprocessing, caching, and dimension reduction to improve retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing an embedding model for RAG
• Reviewing document chunk size and boundaries
• Planning code or specialist document search

Summarize articles, YouTube videos, podcasts, and local files, or extract available text and transcripts. Choose the summary length to suit the task and use transcript extraction to inspect video content when available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Get the main points of a linked article
• Check what a YouTube video is about
• Summarize a local PDF

Build systems that use Pinecone to find documents for AI answers or retain agent memories across sessions. Guides index setup, data loading, meaning-based and keyword search, memory separation, and end-to-end checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI assistant that answers from documents
• Keep agent facts and decisions across sessions
• Combine meaning-based search with exact terminology

Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Grouping observed mistakes into failure categories
• Choosing which evaluation tests to build first

Build, update, explain, or review Microsoft Agent Framework applications in .NET or Python. The skill guides agent and workflow design using current official documentation and supports migration from Semantic Kernel or AutoGen.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an agent application in .NET or Python
• Review agent and workflow design
• Migrate from Semantic Kernel or AutoGen

Plan how to sell AI products to enterprises, from product messaging and variable pricing to failure accountability. Use practical frameworks to structure demos, address operational concerns, and identify buyers prepared to adopt AI agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose how to describe an AI product
• Compare pricing models for variable AI usage
• Address responsibility for AI failures

Generate illustrations, icons, game textures, sprites, and mockups from written descriptions using OpenAI or Google Gemini. The skill selects an available provider, saves the resulting image, and reports its location.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate icons or illustrations from descriptions
• Create game textures and sprites
• Make mockups to explore visual ideas

Build applications with GitHub Copilot SDK using Python, TypeScript, Go, or .NET. Add custom tools, display responses as they arrive, preserve conversations across restarts, and connect external tools through MCP, a protocol for connecting agents to tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Embed an AI assistant in an application
• Add custom tools for an agent to call
• Display generated responses as they arrive

Create and run evaluations in Arize using an LLM as the judge. Define scoring criteria, connect them to actual data fields, evaluate recorded operations or experiment results, and set up continuous scoring for new data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check answers for unsupported claims
• Score experiment results for correctness
• Continuously evaluate new production records

Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up tracing for an LLM application
• Recording tool inputs and outputs in an agent
• Adding Arize export to an existing OpenTelemetry setup