676건 · 관련도순
End-to-end product development for iOS/macOS apps. Covers market research, competitive analysis, PRD generation, architecture specs, UX design, implementation guides, testing, and App Store release. Use for product planning, validation, or generating specification documents.
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On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.
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Generate multiple diverse solutions in parallel and select the best. Use for architecture decisions, code generation with multiple valid approaches, or creative tasks where exploring alternatives improves quality.
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Generate and download images through APIMart's asynchronous GPT-Image-2 API. Use when Codex needs APIMart image generation, GPT-Image-2 text-to-image, image-to-image with reference URLs or local image files, 1k/2k/4k output, aspect-ratio control, task polling, or saving APIMart image results locally.
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Guides the generation, reading, and updating of agentmap.md files. This skill ensures a consistent and up-to-date codebase map for AI Agents.
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Use when a user wants Codex to directly operate PowerPoint and reconstruct an entire reference image as editable PowerPoint objects while preserving its full layout, labels, connectors, and visual relationships. Do not use for paper-to-deck generation or VBA-only output.
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Implement reactive state management using Riverpod 3.x with code generation in Flutter. Use when defining providers, building AsyncNotifiers, or overriding providers in tests.
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Dart static analysis, linting, formatting, and code-generation standards. Use only for analysis_options.yaml, build_runner, dart format, DCM, lefthook, or analyze/format CI failures; defer Dart language, null-safety, Flutter tests, and generic CI questions.
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Angular CLI usage, code generation, build configuration, and bundle optimization. Use for Angular CLI/build tasks; defer standalone webpack configuration and generic test-runner setup.
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Guided setup of the tools the design harness can use — image/video generation channels, browser, encoders. Detects what the user already has, explains pros/cons/costs, asks instead of instructing, and saves choices to .omd/config.json so every later design task reuses them. Trigger: 'omd setup', '도구 셋업', '이미지 생성 뭐 쓸 수 있어', 'configure media', '세팅해줘', 'what can you generate'.
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Guided setup of the tools the design harness can use — image/video generation channels, browser, encoders. Detects what the user already has, explains pros/cons/costs, asks instead of instructing, and saves choices to .omd/config.json so every later design task reuses them. Trigger: 'omd setup', '도구 셋업', '이미지 생성 뭐 쓸 수 있어', 'configure media', '세팅해줘', 'what can you generate'.
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Generate or refresh a STRIDE-based threat model for the current repository using Bug Hunter-native artifacts. Use whenever the repository has no threat model yet, the architecture changed materially, a security review needs fresh trust-boundary context, or the user explicitly asks for a threat model.
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Validate security findings for exploitability, reachability, and real-world impact using Bug Hunter-native findings artifacts. Use after security scans, before patch generation, or whenever the user wants confirmation that a suspected vulnerability is actually exploitable.
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Builds AI chat interfaces and conversational UI with streaming responses, context management, and multi-modal support. Use when creating ChatGPT-style interfaces, AI assistants, code copilots, or conversational agents. Handles streaming text, token limits, regeneration, feedback loops, tool usage visualization, and AI-specific error patterns. Provides battle-tested components from leading AI products with accessibility and performance built in.
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Governance, risk, and compliance — risk assessment and scoring, control mapping across NIST CSF 2.0 / ISO 27001:2022 / SOC 2 / CIS Controls v8, gap analysis, audit evidence preparation, and security policy generation
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Static and dynamic malware analysis, YARA rule generation, sandbox configuration, behavioral profiling, and malware family classification
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Software supply chain security — SBOM generation and analysis, dependency confusion and typosquatting detection, malicious package indicators, CI/CD pipeline hardening, and artifact provenance/signing (SLSA, Sigstore)
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IOC extraction, threat intelligence correlation, MITRE ATT&CK mapping, hunt hypothesis generation, and detection rule creation
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Generate MSBuild binary logs (binlogs) for build diagnostics and analysis. USE FOR: adding /bl:{} to any dotnet build, test, pack, publish, or restore command to capture a full build execution trace, prerequisite for binlog-failure-analysis and build-perf-diagnostics skills, enabling post-build investigation of errors or performance. Requires MSBuild 17.8+ / .NET 8 SDK+ for {} placeholder; PowerShell must quote the complete switch as '-bl:{}'. DO NOT USE FOR: non-MSBuild build systems (npm, Maven, CMake), analyzing an existing binlog (use binlog-failure-analysis instead).
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Analyze MSBuild binary logs to diagnose build failures. USE FOR: build errors that are unclear from console output, diagnosing cascading failures across multi-project builds, tracing MSBuild target execution order, and generally any MSBuild build issues. Requires an existing .binlog file. DO NOT USE FOR: generating binlogs (use binlog-generation), non-MSBuild build systems.
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Guide for modernizing and migrating MSBuild project files to SDK-style format. USE FOR: converting legacy .csproj/.vbproj with verbose XML to SDK-style, migrating packages.config to PackageReference, removing Properties/AssemblyInfo.cs in favor of auto-generation, eliminating explicit Compile Include lists via implicit globbing, consolidating shared settings into Directory.Build.props. Indicators of legacy projects: ToolsVersion attribute, an Import of Microsoft.CSharp.targets via $(MSBuildToolsPath), .csproj files over 50 lines for simple projects. DO NOT USE FOR: projects already in SDK-style format, non-.NET build systems (npm, Maven, CMake), .NET Framework projects that cannot move to SDK-style.
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Scaffolds new agent skills for the dotnet/skills repository. Use when creating a new skill, generating SKILL.md files, writing a skill description that the runtime will actually route to, or setting up skill directory structures. Handles frontmatter generation, section templates, and validation guidance. Do not use for fixing a skill that already fails its evaluation (use improve-skill-quality) or for writing eval.yaml (use create-skill-test).
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Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
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MUST read before generating images. Prompt-crafting guide for gpt-image-2.5 covering tool routing (native image_generation server tool vs the generate_image tool), model and quality selection, prompt structure, exact text rendering, reference-image editing, transparent assets, output formats, and multi-turn refinement.
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