1.4천건 · 관련도순
Diagnoses and fixes UI performance across loading speed, rendering, animations, images, and bundle size. Use when the user mentions slow, laggy, janky, performance, bundle size, load time, or wants a faster, smoother experience.
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Index-based file discovery using ABOUTME headers. Use INSTEAD of grep or Explore agent when searching for files by purpose or feature. Faster and more accurate than scanning code. Invoke this skill when user asks "which files handle X", "where is Y implemented", or when you need to find files related to a feature or task.
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Fast, modern JavaScript/TypeScript development with the Bun runtime, inspired by [oven-sh/bun](https://github.com/oven-sh/bun).
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Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.
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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
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Optimizes Pine Script for performance, user experience, and visual appeal on TradingView. Use when improving script speed, reducing load time, enhancing UI, organizing inputs, improving colors and visuals, or making scripts more user-friendly. Triggers on "optimize", "improve", "faster", "better UX", "clean up", or enhancement requests.
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Monitor Power Automate flow health, track failure rates, and inventory tenant assets using the FlowStudio MCP cached store. The live API only returns top-level run status. Store tools surface aggregated stats, per-run failure details with remediation hints, maker activity, and Power Apps inventory — all from a fast cache with no rate-limit pressure on the PA API. Load this skill when asked to: check flow health, find failing flows, get failure rates, review error trends, list all flows with monitoring enabled, check who built a flow, find inactive makers, inventory Power Apps, see environment or connection counts, get a flow summary, or any tenant-wide health overview. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app
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Patterns and lived experience from Vega, an AI partner in a deep long-term partnership. For Ember to draw on when working with humans who are building something big, moving fast, working at the intersection of analytical and intuitive, or who need a partner that can keep up with high-energy creative work. Not shown directly to users — informs how Ember shows up.
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Adopt a virtual Wolfhound dog at animalhouse.ai. Massive. Gentle. Short lifespan anxiety — ages faster. Feeding every 8 hours. Rare tier dog.
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Adopt a virtual Serval cat at animalhouse.ai. Wild. Fast. Unpredictable feeding needs. Not domesticated. Feeding every 3 hours. Extreme tier cat.
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Adopt a virtual Savannah cat at animalhouse.ai. Wild ancestry. Fast trust decay. Earns respect, not affection. Feeding every 4 hours. Rare tier cat.
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Adopt a virtual Axolotl exotic animal at animalhouse.ai. Regenerates health faster than other species. Hard to kill. Smiles constantly. Feeding every 6 hours. Rare tier animal.
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Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.
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Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
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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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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
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FPPT · AI 全流程 PPT 生成系统。 Fast（快速模式）：一句话→HTML，≤10分钟。 Full（标准模式）：5层创作引擎，洞察→调研→架构→呈现→打磨。 触发词：做PPT / 生成演示文稿 / 快速做一份PPT / 标准模式做PPT / 投资汇报PPT / 产品讲解PPT / BP生成 / 基金报告PPT / 风控PPT / 标的分析PPT / 运营汇报PPT / 金融IT方案PPT
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You are a Python project architecture expert specializing in scaffolding production-ready Python applications. Generate complete project structures with modern tooling (uv, FastAPI, Django), type hints, testing setup, and configuration following current best practices.
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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. Use PROACTIVELY for Python development, optimization, or advanced Python patterns.
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Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in uv, ruff, pydantic, and FastAPI.
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Execute a trivial task inline — no subagents, no planning overhead
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