Topic: ai
10,359 skills in this topic.
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vscode-ext-commands
Guidelines for contributing commands in VS Code extensions. Indicates naming convention, visibility, localization and other relevant attributes, following VS Code extension development guidelines, libraries and good practices
github/awesome-copilot 27,659
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vscode-ext-localization
Guidelines for proper localization of VS Code extensions, following VS Code extension development guidelines, libraries and good practices
github/awesome-copilot 27,659
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web-design-reviewer
This skill enables visual inspection of websites running locally or remotely to identify and fix design issues. Triggers on requests like "review website design", "check the UI", "fix the layout", "find design problems". Detects issues with responsive design, accessibility, visual consistency, and layout breakage, then performs fixes at the source code level.
github/awesome-copilot 27,659
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winapp-cli
Windows App Development CLI (winapp) for building, packaging, and deploying Windows applications. Use when asked to initialize Windows app projects, create MSIX packages, generate AppxManifest.xml, manage development certificates, add package identity for debugging, sign packages, publish to the Microsoft Store, create external catalogs, or access Windows SDK build tools. Supports .NET (csproj), C++, Electron, Rust, Tauri, and cross-platform frameworks targeting Windows.
github/awesome-copilot 27,659
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winui3-migration-guide
UWP-to-WinUI 3 migration reference. Maps legacy UWP APIs to correct Windows App SDK equivalents with before/after code snippets. Covers namespace changes, threading (CoreDispatcher to DispatcherQueue), windowing (CoreWindow to AppWindow), dialogs, pickers, sharing, printing, background tasks, and the most common Copilot code generation mistakes.
github/awesome-copilot 27,659
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cli-ifying
Convert Claude Code slash commands, skills, or agents into standalone CLI tools for Linux/Mac (Bash) and Windows (PowerShell). Use when user says "cli-ify", "make this a CLI", "convert to command line", or wants to reuse a skill outside of Claude Code. First assesses suitability - pushes back if the capability is better as a Claude skill.
frankbria/cli-ify-plugin 1
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example-data-processor
Process CSV data files by cleaning, transforming, and analyzing them. Use this when users need to work with CSV files, clean data, or perform basic data analysis tasks.
fkesheh/skill-mcp 25
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clarity-gate
Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
frmoretto/clarity-gate 25
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clarity-gate
Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
frmoretto/clarity-gate 25
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clarity-gate
Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
frmoretto/clarity-gate 25
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clarity-gate
Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
frmoretto/clarity-gate 25
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lex-uk-law
i-dot-ai/lex 38
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lex-uk-law
UK legal research using the Lex API. Use this skill whenever the user asks about UK law, legislation, statutory instruments, Acts of Parliament, legal provisions, amendments, or anything that could benefit from searching authoritative UK legal sources. Also use when the user mentions specific UK Acts (e.g. "the Data Protection Act"), asks about legal requirements, or needs to understand how legislation has changed over time. Even if the user doesn't explicitly say "search legislation", if they're asking a question that UK law could answer, use this skill.
i-dot-ai/lex 38
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implementing-llms-litgpt
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
Orchestra-Research/AI-Research-SKILLs 6,644
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mamba-architecture
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
Orchestra-Research/AI-Research-SKILLs 6,644
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nanogpt
Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
Orchestra-Research/AI-Research-SKILLs 6,644
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rwkv-architecture
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
Orchestra-Research/AI-Research-SKILLs 6,644
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ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Orchestra-Research/AI-Research-SKILLs 6,644
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lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
Orchestra-Research/AI-Research-SKILLs 6,644
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optimizing-attention-flash
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.
Orchestra-Research/AI-Research-SKILLs 6,644
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gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Orchestra-Research/AI-Research-SKILLs 6,644
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gptq
Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
Orchestra-Research/AI-Research-SKILLs 6,644
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dspy
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
Orchestra-Research/AI-Research-SKILLs 6,644
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guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
Orchestra-Research/AI-Research-SKILLs 6,644