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Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.

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Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

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Generate comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and best practices

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Fix MSBuild targets that generate files during the build but those files are missing from compilation or output. Only activate in MSBuild/.NET build context. USE FOR: generated source files not compiling (CS0246 for a type that should exist), custom build tasks that create files but they are invisible to subsequent targets, globs not capturing build-generated files because they expand at evaluation time before execution creates them, ensuring generated files are cleaned by the Clean target. Covers correct BeforeTargets timing (CoreCompile, BeforeBuild, AssignTargetPaths), adding to Compile/FileWrites item groups, using $(IntermediateOutputPath) instead of hardcoded obj/ paths. DO NOT USE FOR: C# source generators that already work via the Roslyn pipeline, T4 design-time generation that runs in Visual Studio, non-MSBuild build systems. INVOKES: no tools — pure knowledge skill.

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Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.

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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.

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Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

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You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

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Build tools that agents can use effectively, including architectural reduction patterns. Use when creating new tools for agent systems, debugging tool-related failures or misuse, or optimizing existing tool sets for better agent performance.

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Expert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.

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Comprehensive guide for creating and managing skills in Claude Code with auto-activation system, following Anthropic's official best practices including the 500-line rule and progressive disclosure pattern.

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Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.

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Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

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Audit and update an arbitrary codebase to follow the Code.mil federal open-source licensing model (MIT with 17 U.S.C. Section 105 disclaimers and INTENT.md), including CRAN/R package DESCRIPTION setup.

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Orchestrates the tdmcp Anthropic Connectors Directory (Desktop Extension / MCPB) submission prep — designs the package, writes the privacy page, migrates .dxt→.mcpb, drafts all form answers, and QAs the result. Use for ANY work on the tdmcp directory/marketplace submission, including re-runs after a rejection ("update the submission", "redo the form answers", "re-migrate the bundle", "fix the privacy page", "the directory rejected us"). Drives a 4-agent pipeline (architect → docs-author ∥ bundle-engineer → QA).

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Optimize website and web application performance including loading speed, Core Web Vitals, bundle size, caching strategies, and runtime performance

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Use when adding interactive 3D scenes from Spline.design to web projects, including React embedding and runtime control API.

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Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI.

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Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems.

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Create and render fixed-canvas editable visual designs, including posters, marketing graphics, covers, menus, banners, and social cards. Default outputs include fixed and editable HTML, PNG, a mouse editor with scan and exploded layer views, and an evidence-backed Design Replay. Do not use for websites, slide decks, videos, or standalone logo systems. Always use this skill when the project contains `poster.json`.

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Orchestrate App Store releases with asc, including staging a version, uploading or building an artifact, publishing, and submitting for review. Use when the user wants to prepare or execute a release. Keep Game Center item preparation in this skill; route other readiness failures, stuck submissions, cancellation, and retry decisions to asc-submission-health.

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Define, validate, run, resume, and audit repo-local multi-step automations with current `asc workflow` and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.

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Diagnose App Store submission blockers and operate review health with asc, including readiness validation, repair routing, status monitoring, cancellation, and retry decisions. Use when validation fails, a version is not in a valid state, review status is unclear or stuck, or a failed submission must be repaired and retried. For staging, upload, publication, and submission execution, use asc-release-flow.

日本語の概要は準備中です。原文の説明を表示しています。