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pytorch-lightning
Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
k-dense-aigenerate-image
Generate or edit images with AI models through the OpenRouter Image API (Gemini, Seedream, Recraft, GPT-Image, Riverflow). Use for photos, illustrations, artwork, concept art, visual assets, logos, and image editing or compositing from reference images. For flowcharts, circuits, pathways, and other technical diagrams, use the scientific-schematics skill instead.
k-dense-aipyzotero
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
k-dense-aiaeon
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
k-dense-ainetworkx
Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.
k-dense-aitransformers
Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Use when working with AutoModel, pipelines, tokenizers, or TrainingArguments—not for general ML outside the Transformers library.
k-dense-aisympy
Use when you need exact symbolic math in Python — algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX. Prefer NumPy or SciPy when floating-point approximations are sufficient.
k-dense-aimarket-research-reports
Build evidence-traceable market research reports and assumption-driven market sizing or forecast scenarios. Use for market definition, industry and customer evidence, competitive landscapes, TAM/SAM/SOM reconciliation, forecast sensitivity, and auditable report scaffolds.
k-dense-aiinfographics
Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.
k-dense-aishap
Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.
k-dense-aiget-available-resources
Detect host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.
k-dense-aidask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
k-dense-aimatlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
k-dense-aioptimize-for-gpu
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.
k-dense-aiscikit-survival
Build, evaluate, and audit right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.
k-dense-aiopen-notebook
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting.
k-dense-aiparallel-web
Use Parallel CLI for web search, URL extraction, deep research, structured data enrichment, entity discovery, and recurring web monitoring. Best for requests that explicitly need current web evidence, academic-source discovery, repeated entity lookups, exhaustive reports, or ongoing change tracking.
k-dense-ai
chakra-ui-migrate
将 Chakra UI 项目从 v2 迁移到 v3，涵盖包更改、codemod、Provider 设置、颜色模式、属性重命名、复合组件、主题、recipes 和 Next.js 更新。每当用户升级 Chakra UI 版本、升级后遇到破坏性更改、转换旧的 v2 模式（ColorModeScript、useColorModeValue、styleConfig、extendTheme、isDisabled、colorScheme、@chakra-ui/icons、framer-motion 依赖）、修复复合组件模式，或询问 Chakra UI v2 和 v3 之间的差异时，使用此技能——即使他们没有明确说“迁移”或“升级”。
chakra-uichakra-ui-refactor
Review, convert, and improve UI code using Chakra UI v3. Use this skill whenever a user wants to review Chakra UI code for issues, convert plain HTML/CSS, Tailwind, CSS Modules, or styled-components to Chakra UI, clean up messy Chakra components, fix layout structure or token usage, or asks anything like "is this correct", "what's wrong with this", "review my component", "refactor this", "clean up", "convert", or "chakra-ify this" — even without the words "review" or "refactor". Trigger on any request to check, improve, or convert Chakra UI code, however casually phrased.
chakra-uichakra-ui-builder
使用 Chakra UI v3 构建响应式且具备无障碍访问（a11y）能力的 UI 组件与布局，在全新或现有项目中安装及配置 Chakra UI，并利用 token、语义化 token、recipe 和 slot recipe 设计可扩展的主题风格。当用户提出以下需求时请调用此 Skill：使用 Chakra UI 构建、创建或生成任意 UI 组件、页面、表单、仪表盘、导航栏、卡片、落地页区域、价格表或布局；向项目中添加 Chakra UI、配置 ChakraProvider、运行 CLI 代码片段（snippets）、配置颜色模式或修复 Provider 包裹问题；或者询问关于主题化（theming）的内容——如定义品牌色、设计 token、语义化 token、深色模式数值、组件 recipe、slot recipe、类型生成（typegen）或导出/解耦默认主题（ejecting default theme）。只要涉及任何 Chakra UI 组件构建、项目配置、主题定制或图表（charts）开发相关的请求，无论表达多么随意（如“帮我加个品牌色”、“做个可复用的卡片样式”、“画个柱状图”、“显示折线图”、“写个登录表单”、“搞个侧边栏”、“把 Chakra 加到我的 App 里”），均需触发此 Skill。
chakra-ui
marketing-council
当用户希望针对营销问题获取多元专家视角时使用——提供一个由传奇营销大师（如 Seth Godin、David Ogilvy、Eugene Schwartz、April Dunford、Rory Sutherland、Alex Hormozi、Byron Sharp 等）组成的“模拟营销智囊团/顾问会”。当用户提到“营销智囊团”、“顾问委员会”、“营销顾问”、“Seth Godin 会怎么说”、“Ogilvy 会怎么看”、“如果是 Hormozi 会怎么做”、“多给几个视角”、“辩论一下”、“让智囊团评审”、“营销导师”，或询问某位知名营销专家会如何解决其具体问题时，均可触发此 Skill。智囊团会结合各位顾问有据可查的思考框架给出见解，揭示彼此的分歧点，并提炼出综合建议。明确最终方向后，可移交至定位（positioning）、Offer 策划（offers）、文案撰写（copywriting）、广告投放（ads）或相关 Skill 进行落地执行。
coreyhaines31
game-setup-and-config
Use this skill when creating a new Phaser 4 game instance or configuring GameConfig options. Covers renderer selection, canvas setup, scaling, pixel art, FPS settings, boot sequence, and all config sub-objects. Triggers on: new Phaser.Game, GameConfig, game setup, renderer, pixel art, FPS.
phaserjs
protect-mcp-setup
Configure Cedar policy enforcement and Ed25519 signed receipts for Claude Code tool calls. Use when setting up projects that need cryptographic audit trails, policy-gated tool execution, or compliance-ready evidence of agent actions.
wshobsonsigned-audit-trails-recipe
Step-by-step cookbook for setting up cryptographically signed audit trails on Claude Code tool calls. Use when explaining, evaluating, or demonstrating the pattern before committing to the protect-mcp runtime hooks. Covers Cedar policy, Ed25519 receipts, offline verification, tamper detection, CI/CD integration, and SLSA composition.
wshobson