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概要と使いどころ
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
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Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security tasks.
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Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
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。
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
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Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
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Routes problems to appropriate mathematical frameworks using expert heuristics
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。
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.
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。
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
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Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
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。
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
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。
Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.
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。
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
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 the user needs current library, framework, or API documentation and version-specific code examples.
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。
Create or redesign self-contained single-file HTML artifacts with a visual direction shaped by the user's brief, project, and subject. Use when HTML is the deliverable for a report, explainer, landing page, presentation, tool, mixed artifact, or broad request. This is the collection's only implicit router. Route clear wireframe, prototype, mockup, plan, or diagram requests to the matching direct-invocation specialist when available. Do not use for ordinary application implementation when a standalone HTML file is not the deliverable.
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。
Generate AI design contract (AI-SPEC.md) for phases that involve building AI systems — framework selection, implementation guidance from official docs, and evaluation strategy
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。
Testing strategies and methodologies including TDD, E2E testing, and multi-framework support
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Automate Miro tasks via Rube MCP (Composio): boards, items, sticky notes, frames, sharing, connectors. Always search tools first for current schemas.
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Manage your self-hosted Immich photo library through conversation: natural language and OCR search, geographic album curation, duplicate detection, people and faces, metadata repair, video frames and PDF photobooks. 94 MCP tools and 13 skills, tested live on Immich 2.x and 3.x. Install: claude plugin install immich-photo-manager
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。
Convert existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; use only when the request names federated/NVFLARE conversion or asks multiple sites to train collaboratively while keeping each site's data local, and either names PyTorch Lightning or preliminary source inspection identifies one Lightning owner; do not use for non-federated Lightning work such as DDP, profiling, inference serving, or training-loop changes, nor for plain PyTorch, TensorFlow/Keras, other frameworks, deployment, POC/production lifecycle, or experiment workflows.
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。
Convert existing plain or manual PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; use when the user names plain PyTorch or preliminary source inspection identifies one plain-PyTorch owner, and not for Lightning, other frameworks, deployment, POC/production lifecycle, or experiment workflows.
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。
Route open-ended NVFLARE advice and only conversion requests whose preliminary source inspection reports unresolved or conflicting ownership; never load this skill merely to inspect a concrete conversion request before selecting its detected framework converter.
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Evidence-gated workflow for MoE performance optimization in Megatron Bridge. Covers measurement contracts, the Three Walls framework, parallel folding, profiling, matched A/B tuning, and final validation.
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Use this skill when choosing or running NeMo Relay installation for the CLI, Python, Node.js, Rust, OpenClaw, Hermes, or maintained framework integrations before runtime configuration or quick-start setup.
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Use when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish.
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。