1.3천건 · 관련도순
Uses Chrome DevTools MCP for accessibility (a11y) debugging and auditing based on web.dev guidelines. Use when testing semantic HTML, ARIA labels, focus states, keyboard navigation, tap targets, and color contrast.
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See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.
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Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search.
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Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.
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Build search applications with vector, hybrid, and semantic search capabilities.
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Maxim Design System Intelligence — token architecture (primitive→semantic→component), component library governance, CSS variable systems, Tailwind integration, design-to-code handoff, Storybook, Figma token export. Activate for design system creation, token audits, component specs, dark mode systems, brand-to-token sync. Powered by Maxim behavioral science + Atomic Design + TOGAF governance.
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Audit context files (CLAUDE.md, .claude/ tree, READMEs, docs) for broken references and verifiably stale content. Runs a deterministic offline link/path checker first, then a bounded semantic review where every finding must cite evidence. Reports findings; fixes only on request. Use when the user says "docs check", "/docs-checker", "check the docs for stale content", "audit CLAUDE.md", or when a scheduled cloud routine fires.
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OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
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End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptools_scm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.typed, setuptools_scm, semver, mypy, pre-commit, GitHub Actions CI/CD, or PyPI publishing.
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CI/CD pipeline patterns with GitHub Actions, release automation, and testing strategies. Use for: github actions, workflow, CI, CD, pipeline, deploy, release, semantic release, changesets, goreleaser, matrix, cache, secrets, environment, artifact, reusable workflow, composite action.
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Formats commit messages using the Conventional Commits specification. Use when creating commits, writing commit messages, or when the user mentions commits, git commits, or commit messages. Ensures commits follow the standard format for automated tooling, changelog generation, and semantic versioning.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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Generate flat, minimal light/dark-aware SVG diagrams as standalone HTML files, using a unified educational visual language with 9 semantic color ramps, sentence-case typography, and automatic dark mode. Best suited for educational and non-software visuals — physics setups, chemistry mechanisms, math curves, physical objects (aircraft, turbines, smartphones, mechanical watches), anatomy, floor plans, cross-sections, narrative journeys (lifecycle of X, process of Y), hub-spoke system integrations (smart city, IoT), and exploded layer views. If a more specialized skill exists for the subject (dedicated software/cloud architecture, hand-drawn sketches, animated explainers, etc.), prefer that — otherwise this skill can also serve as a general-purpose SVG diagram fallback with a clean educational look. Ships with 15 example diagrams.
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Analyzes keyword usage in provided content, calculates density, suggests semantic variations and LSI keywords based on the topic. Prevents over-optimization. Use PROACTIVELY for content optimization.
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Write idiomatic C++ code with modern features, RAII, smart pointers, and STL algorithms. Handles templates, move semantics, and performance optimization. Use PROACTIVELY for C++ refactoring, memory safety, or complex C++ patterns.
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Complete guide for AutoFixture and Bogus integration. Use when you need to combine AutoFixture with Bogus to generate test data that is both anonymous and realistic. Covers SpecimenBuilder integration, hybrid generators, test data factories, and circular reference handling. Keywords: autofixture bogus integration, autofixture bogus, bogus integration, Faker, EmailSpecimenBuilder, PhoneSpecimenBuilder, NameSpecimenBuilder, realistic test data, semantic data, hybrid generator, HybridTestDataGenerator, OmitOnRecursionBehavior, circular reference
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Implements accessible .NET UI. SemanticProperties, ARIA, AutomationPeer, testing per platform.
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Rewrite AEM Edge Delivery Services page content for AI search discoverability (GEO — Generative Engine Optimization). Analyzes content structure, semantic clarity, and factual density, then produces optimized rewrites that rank in both traditional search and AI-powered search engines. Use when optimizing pages for search visibility, AI discoverability, or content clarity.
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Improve search engine visibility with proper meta tags, structured data, sitemaps, and semantic HTML.
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Idiomatic Go concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.
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Semantic Design System Skill for Google Stitch. Generates agent-friendly DESIGN.md files that enforce premium, anti-generic UI standards — strict typography, calibrated color, asymmetric layouts, perpetual micro-motion, and hardware-accelerated performance.
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