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Found 945 Skills
C# scripting in Unity for gameplay, behavior, and engine integration. PROACTIVELY activate for: (1) writing Unity C# scripts, (2) MonoBehaviour lifecycle (Awake/OnEnable/Start/Update/FixedUpdate/LateUpdate), (3) coroutines and async/await in Unity, (4) delegates, events, Action/Func patterns, (5) ScriptableObject creation and serialization, (6) GetComponent / TryGetComponent and component caching, (7) physics scripting (Rigidbody, raycast, collision/trigger callbacks), (8) animation scripting (Animator parameters, state machines, IK), (9) NavMesh and NavMeshAgent scripting, (10) input handling (Input System package), (11) custom serialization and SerializeField. Provides: lifecycle reference, coroutine vs async patterns, ScriptableObject templates, Rigidbody/collision recipes, NavMesh examples, and Input System setup.
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment setup, training-loop or other code authoring/scaffolding, contributor/CI/packaging questions, repo-specific questions in physicsnemo-sym/-cfd/-curator, or general (non-physics) ML/PyTorch.
Find focused, runnable Deepgram recipes for a specific feature × language. Use whenever someone wants a minimal working code snippet for ONE feature (transcribe URL, diarize, smart-format, voice agent connect, etc.) rather than a full starter app. Recipes are under 50 lines, read DEEPGRAM_API_KEY from env, and ship with a runnable example_test. Covers Python, JavaScript, Go, .NET, Java, Rust, and the Deepgram CLI.
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.
Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists or non-security ownership questions.
Generate interactive TiddlyWiki-style HTML software manuals with screenshots, API docs, and multi-level code examples. Use when creating user guides, software documentation, or API references. Triggers on "software manual", "user guide", "generate manual", "create docs".
This skill should be used when the user asks to "optimize a DSPy program", "use MIPROv2", "tune instructions and demos", "get best DSPy performance", "run Bayesian optimization", mentions "state-of-the-art DSPy optimizer", "joint instruction tuning", or needs maximum performance from a DSPy program with substantial training data (200+ examples).
n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.
Comprehensive MDX component patterns (Note, Pitfall, DeepDive, Recipes, etc.) for all documentation types. Authoritative source for component usage, examples, and heading conventions.
Comprehensive documentation specialist covering API documentation, technical writing, design documentation, migration guides, and changelog generation. Use when creating OpenAPI/Swagger specs, generating SDKs, writing user guides, creating README files, documenting architecture, writing design specs, creating ADRs, writing migration guides, or generating changelogs from git commits. Handles versioning, examples, developer experience, and user-facing documentation.
Provides comprehensive uni-app-x component and API integration guidance. Use when the user needs official uni-app-x components or APIs, wants per-component or per-API examples, or needs cross-platform compatibility details for uni-app-x.