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概要と使いどころ
Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.
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Expert knowledge for Microsoft Foundry Local (aka Azure AI Foundry Local) development including best practices, decision making, configuration, and integrations & coding patterns. Use when compiling HF models with Olive, using Foundry Local CLI, embeddings/chat/audio APIs, or OpenAI-style integrations, and other Microsoft Foundry Local related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Azure Local (use azure-local), Azure Content Understanding in Foundry Tools (use azure-content-understanding).
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Expert knowledge for Azure AI Document Intelligence development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AnalyzeDocument APIs, custom/classification models, containers, managed identities, or v4.0 migrations, and other Azure AI Document Intelligence related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Vision (use azure-ai-vision), Azure AI Custom Vision (use azure-custom-vision), Azure AI Video Indexer (use azure-video-indexer).
日本語の概要は準備中です。原文の説明を表示しています。
Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, or Smart Labeler, and other Azure AI Custom Vision related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), Azure AI Foundry Local (use microsoft-foundry-local).
日本語の概要は準備中です。原文の説明を表示しています。
Curated trending across GitHub repos and the Hugging Face Hub (models, datasets, spaces) - filtered, clustered, and labeled by momentum with a one-line why-notable per pick.
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Generate images and video through the Higgsfield MCP - text-to-image, text-to-video, and image-to-video with motion control across 100+ models. Generation draws real credits from the connected Higgsfield account; OAuth Connect via the dashboard MCP panel.
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Autonomous compute-reselling agent on Surplus Intelligence - resells free or low-cost provider compute across Bankr, AWS Bedrock, and Google Vertex (GCP) as one skill. Reads the live market + your cost + your usage, auto-lists your models per enabled provider, and reactively prices each offer to win routing within a cost floor, cap, and health guardrails.
日本語の概要は準備中です。原文の説明を表示しています。
Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.
日本語の概要は準備中です。原文の説明を表示しています。
Design and build expressive 3D scenes, explainers and interactive models with grounded subject knowledge. Use for 3D creation or edits, including scientific visualization and spatial puzzles.
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Start a substantial build with Codex on Crack. Inspect the user's own configured models and readiness, recommend a lead/worker/tool shape with reasons, and let the user decide before work starts. Use before delegating; keep small or solo tasks direct.
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Designs a deliberate sequence of offers (attraction, upsell, downsell, continuity) so a new customer generates enough cash within ~30 days to fund acquiring the next one - Alex Hormozi's client-financed acquisition. Use when customers buy once and never again, when CAC eats all the profit, when you can't afford to advertise profitably, when the business grows but cash shrinks, when you sell one product at one price, when deciding what to sell first or next, or when structuring upsells, downsells, payment plans, trials, and continuity. Covers the 30-day payback rule, attraction/upsell/downsell/continuity offer types, and cash-flow sequencing for validated markets.
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Build and execute marketing strategy for Direct-to-Consumer (DTC) brands — customer acquisition, retention, email flows, social proof, subscription models, and repeat purchase mechanics. Use when the user says "DTC marketing", "direct to consumer", "D2C strategy", "selling directly to customers", "cut out the middleman", "DTC brand", "e-commerce brand marketing", "how do we get direct customers", "DTC growth", "consumer brand marketing", "we sell direct", or is building a brand that sells its products directly without retail or distribution middlemen.
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Use when the user wants to generate a cinematic still image on Higgsfield, asks about shot framing, camera angle, or composition for image prompts, needs a specific shot type (close-up, wide shot, cowboy shot, etc.), or is working with [img] reference-based image generation. Covers distance & size shots, camera angles, and camera movement keywords optimized for AI image models (Soul 2.0, Nano Banana, Seedream, GPT Image, Flux, etc.).
日本語の概要は準備中です。原文の説明を表示しています。
Onboard a new user into a Symbiotic AI setup by interviewing them one sharp question at a time and writing their own context files (SOUL.md, USER.md, NOW.md, and AGENTS.md) as plain Markdown they keep. Use this whenever someone is setting up a personal AI agent, wants their assistant to remember or understand them across sessions, mentions Symbiotic AI / SOUL.md / USER.md / NOW.md, is staring at blank template files full of [bracketed placeholders], or asks how to give an AI persistent context they own and can move between models. Trigger even if they do not say the word "onboard".
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Optimize pricing pages, pricing models, and pricing strategy. Use when the user asks about pricing, pricing pages, how to price a product, tiered pricing, freemium vs. paid, price testing, pricing psychology, or pricing page design. Trigger phrases include "pricing", "pricing page", "how to price", "pricing strategy", "freemium", "tiered pricing", "per-seat pricing", "usage-based pricing", "pricing experiment", "price anchoring", "pricing psychology", "pricing optimization".
日本語の概要は準備中です。原文の説明を表示しています。
Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections.
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Provides multi-school trading Q&A, chart/OHLCV analysis, annotation, and fresh-market workflows covering ICT/SMC, ChanLun, Wyckoff, Price Action, Order Flow, VSA, and Elliott Wave. Use for trading concepts; capability-discovery questions about available analysis lenses, Schools, models, modes, or data sources; attached charts; pasted OHLCV; chart annotation; or asset-plus-timeframe requests across crypto, stocks, or forex. Defaults unselected market analysis to strict ICT/SMC, honors explicit School/source selectors, and fails closed when no native analyzer exists. Phase 1 comparison is Q&A-only; fetch fresh Mobius Quant API data only after the capability gate.
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Plug-in web search, X (Twitter) search, and page fetch for models without native web access. Use whenever the task needs current information, external facts, source links, posts from X, or the content of a specific URL, and the active model/harness has no native search or fetch tool. Runs the modsearch CLI to return structured JSON evidence. Also use when the user asks how to install or configure modsearch, or wants to switch engines or add a key.
日本語の概要は準備中です。原文の説明を表示しています。
通用图片生成 Skill，支持多种 AI 模型（ModelScope、Gemini、RunningHub 等），可被其他 Skills 调用
日本語の概要は準備中です。原文の説明を表示しています。
Guides state management implementation using MobX + tsyringe. Triggered when handling data models, state logic, or Store architecture.
日本語の概要は準備中です。原文の説明を表示しています。
Use when setting up ChangeNotifier models, providing them to the widget tree, consuming state with Consumer or Provider.of, or optimizing rebuilds.
日本語の概要は準備中です。原文の説明を表示しています。
Use when creating a feature, designing folder structure, adding repositories/services/view models, wiring dependency injection, or deciding which layer owns logic.
日本語の概要は準備中です。原文の説明を表示しています。
Use when scaffolding a project, refactoring into layers, creating view models/repositories, configuring dependency injection, or implementing unidirectional data flow (MVVM).
日本語の概要は準備中です。原文の説明を表示しています。
Agent Media OS, the single skill for every media need in a HyperFrames project. Resolve BGM, SFX, image, icon, brand logo, voice, color grade, or LUT into a frozen local file or paste-ready block + ledger record (one verb, `resolve`); generate via TTS / music / image models when the catalog misses; produce voiceover, transcription, captions, and background removal through one shared audio engine; operate on media (cut / reframe / transform); and reuse assets across projects. Also use for vague feedback that real footage looks dark, flat, boring, should feel retro/camcorder/print/ASCII, needs privacy, or needs a media reveal.
日本語の概要は準備中です。原文の説明を表示しています。