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Found 3,373 Skills
When the user wants to write the first 30 seconds of a YouTube video, create retention hooks, improve video openings, or reduce early drop-off. Also use when the user says 'write a hook,' 'video intro,' 'opening for my video,' 'first 30 seconds,' 'retention hook,' 'stop the scroll,' 'my videos have bad retention at the start,' 'viewers are leaving in the first minute.' For full script structure, see script-structure. For title/thumbnail pairing, see title-craft and thumbnail-design.
Create a git commit with clear, conventional commit messages. You MUST read this when the user wants to commit staged changes, write a commit message, or finalize code changes with proper conventional commit format since it describes how to follow the user's specific requirements.
The meta skill. Turn any raw feature into a properly-skilled, tested, resolvable unit of agent capability. Cross-modal eval is the recommended Phase 3 quality gate: 3 frontier models from different providers critique the output, you iterate to quality, THEN write tests that lock in the proven-good behavior.
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Control Unreal Engine 5 editor via HTTP commands. Spawn/delete/transform actors, manage blueprints, materials, animation blueprints, and any UObject property via reflection. Use when the user asks to create, modify, or query anything in UE5 editor, or mentions UE5, Unreal, actors, blueprints, levels, materials, animation, input, or characters.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
@copilotkit/react-core — mount CopilotKitProvider in a Next.js App Router / React Router v7 / TanStack Start / SPA app, drop in CopilotChat/CopilotPopup/CopilotSidebar (v2 chat components ship from react-core/v2 — NOT react-ui, which is CSS-only in v2), access and subscribe to agents with useAgent / useAgentContext / useCapabilities, switch between multiple agents, manage durable Intelligence threads with useThreads, register browser-side tools via useFrontendTool, render tool calls with useRenderTool / useComponent / useDefaultRenderTool, gate execution with useHumanInTheLoop, wire file attachments with useAttachments, configure suggestion pills, and register activity- and custom-message renderers. publicLicenseKey is canonical (publicApiKey is deprecated alias). Load the reference under references/ that matches your task.
OCRNet for scene text recognition. Recognizes text content from cropped text-region images and supports CTC and attention-based decoders. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a TAO OCRNet model. Trigger phrases include "train OCRNet", "scene text recognition", "OCR cropped text", "CTC / attention text decoder".
Find and fix game performance problems methodically — measure with the engine profiler first, reason about the frame-time budget, locate the CPU-vs-GPU bottleneck, then apply the right fix: object pooling, draw-call batching, fewer allocations/GC spikes, and asset budgets. Engine- neutral method that pairs with each engine's profiler. Use when the user mentions performance, optimize, low/dropping FPS, frame drops, stutter, lag, profiler, frame budget, draw calls, batching, garbage collection/GC spikes, object pooling, or "the game runs slow".
Add persistent, structured long-term memory to AI agents using Maximem Synap. Use this skill whenever the user is building, debugging, or evaluating an AI agent and mentions any of: "memory", "long-term memory", "persistent memory", "agent memory", "remember across sessions", "context window", "agent forgets", "user preferences", "personalization", "RAG over conversations", "multi-tenant memory", "memory layer", "Mem0", "Zep", "Letta", "SuperMemory", "Cognee", or asks how to integrate memory into LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK, Pydantic AI, CrewAI, AutoGen, Google ADK, Haystack, Agno, Semantic Kernel, Microsoft Agent Framework, NVIDIA NeMo, LiveKit, Pipecat, Claude Agent SDK, Mastra, Vercel AI SDK, or MCP (no-code). Also trigger on direct mentions of "Synap", "Maximem", "maximem-synap", or `synap-*` package names. Covers SDK setup, scoping (User/Customer/Client), ingestion, retrieval, and one drop-in package per framework.
Read-only root-cause analysis for a tracker issue. Identifies the bug's location and the minimal change surface so the next agent can implement the fix without re-exploring the repo. Outputs a short summary, the files that need to change, and the proposed approach.
Find the root cause of a bug before fixing it. Use when a bug, test failure, or unexpected behavior appears, before proposing any fix.