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Claude Skills by thedixitjain
github.com/thedixitjain12,707 skills283 installs48,721 views
- PymcBayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.Votes: 0GitHub stars: 3
- Pyspark Transformer'Transform pyspark transformer operations. Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category. Use when working with pyspark transformer functionality. Trigger with phrases like \"pyspark transformer\", \"pyspark transformer\", \"pyspark\". 'Votes: 0GitHub stars: 3
- PytdcUse Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle workflows.Votes: 0GitHub stars: 3
- Pytorch FsdpFully sharded data-parallel training for large models.Votes: 0GitHub stars: 3
- Pytorch Lightning K Dense AiDeep 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.Votes: 0GitHub stars: 3
- Pytorch LightningClean training loops with built-in distributed support.Votes: 0GitHub stars: 3
- Pytorch Model Trainer'Build pytorch model trainer operations. Auto-activating skill for ML Training. Triggers on: pytorch model trainer, pytorch model trainer Part of the ML Training skill category. Use when working with pytorch model trainer functionality. Trigger with phrases like \"pytorch model trainer\", \"pytorch trainer\", \"pytorch\". 'Votes: 0GitHub stars: 3
- Pytorch Patterns Affaan M 2PyTorch深度学习模式与最佳实践，用于构建稳健、高效且可复现的训练流程、模型架构和数据加载。Votes: 0GitHub stars: 3
- Pytorch Patterns Affaan M日本語翻訳：このファイルは pytorch-patterns 用の日本語翻訳が必要ですVotes: 0GitHub stars: 3
- Pytorch PatternsPyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.Votes: 0GitHub stars: 3
- Quantized ExportExport a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when choosing a quantization format for a target device, or when an exported model fails its smoke test.Votes: 0GitHub stars: 3
- Refresh Semantic ModelAutomatically invoke this skill whenever the user asks to refresh a semantic model or a dataset. Can also be used to manage, optimize, troubleshoot, or configure a refresh or a refresh schedule.Votes: 0GitHub stars: 3
- Restat IdentificationUse when the causal-identification or measurement strategy is the bottleneck for a The Review of Economics and Statistics (REStat) manuscript — a DID / RD / IV / shift-share design, or a measurement / measurement-error problem. Stress-tests the design to REStat's applied-econometrics-and-measurement bar before exhibits are finalized.Votes: 0GitHub stars: 3
- Restat RebuttalUse when a The Review of Economics and Statistics (REStat) decision letter (R&R or reject-and-resubmit) has arrived and the manuscript needs a response-to-referees letter and a revision plan. Drafts the response strategy; it does not run new analysis (route that to the analysis skills).Votes: 0GitHub stars: 3
- Restat Referee StrategyUse when anticipating the objections a The Review of Economics and Statistics (REStat) referee will raise for a given design, and pre-empting them in the manuscript before submission. Maps threats to defenses; it does not draft the post-decision response letter (that is restat-rebuttal).Votes: 0GitHub stars: 3
- Restat RobustnessUse when the headline estimate of a The Review of Economics and Statistics (REStat) manuscript needs to survive specification, sample, measurement, and inference choices before submission. Builds the robustness suite that REStat referees expect; it does not establish the primary identification.Votes: 0GitHub stars: 3
- Restat SubmissionUse when running the final pre-submission preflight for The Review of Economics and Statistics (REStat) via Editorial Express — format rules, abstract limit, online-appendix cap, article categories, submission fee, the proprietary-data fee hold, and house style. Final checks; it does not draft content.Votes: 0GitHub stars: 3
- Restat Tables FiguresUse when building or revising the exhibits of a The Review of Economics and Statistics (REStat) manuscript so the main result is legible in one table or figure in AEA/REStat house style. Designs publication-grade exhibits; it does not run the underlying estimation.Votes: 0GitHub stars: 3
- Restat Theory ModelUse when deciding how much theory or structure a The Review of Economics and Statistics (REStat) manuscript should carry — right-sizing a model so it interprets or disciplines the empirical estimate without becoming the contribution. Calibrates the theory's role; it does not develop new theory for its own sake.Votes: 0GitHub stars: 3
- Restat Topic SelectionUse when deciding whether an applied-economics project fits The Review of Economics and Statistics (REStat) rather than AER / AEJ:Applied / J. Econometrics / a field journal, and sharpening the question to REStat's empirical-and-measurement bar. Decides venue fit and frames the question; it does not run the analysis.Votes: 0GitHub stars: 3
- Restat WorkflowUse when deciding which restat-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for a The Review of Economics and Statistics (REStat) submission. Routes — it does not replace — the specialized skills.Votes: 0GitHub stars: 3
- Review Of Economics And StatisticsUse when targeting Review of Economics and Statistics (REStat) or deciding whether an applied econometrics manuscript fits this venue. Encodes the journal's fit, framing, method-and-evidence bar, house style, official-submission re-check, and desk-reject heuristics.Votes: 0GitHub stars: 3
- Risk ClassificationClassifies agent tasks into 4 risk tiers (GREEN/YELLOW/RED/CRITICAL). Use when assessing action reversibility before committing to an approach.Votes: 0GitHub stars: 3
- Risk Management SpecialistMedical device risk management specialist implementing ISO 14971 throughout product lifecycle. Provides risk analysis, risk evaluation, risk control, and post-production information analysis. Use when user mentions risk management, ISO 14971, risk analysis, FMEA, fault tree analysis, hazard identification, risk control, risk matrix, benefit-risk analysis, residual risk, risk acceptability, or post-market risk.Votes: 0GitHub stars: 3
- Roc Curve Plotter'Manage roc curve plotter operations. Auto-activating skill for ML Training. Triggers on: roc curve plotter, roc curve plotter Part of the ML Training skill category. Use when working with roc curve plotter functionality. Trigger with phrases like \"roc curve plotter\", \"roc plotter\", \"roc\". 'Votes: 0GitHub stars: 3
- Rss Artifact EvaluationUse when packaging the artifacts behind an RSS (Robotics: Science and Systems) paper — code, trained policies, trial ledgers, hardware documentation, and footage — first as anonymous review-time evidence and then as the public release the venue's free open-access proceedings culture expects, without any badge program to structure it.Votes: 0GitHub stars: 3
- Running Clustering Algorithms'Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting \"run clustering\", \"cluster analysis\", or \"group data points\". Trigger with relevant phrases based on skill purpose. 'Votes: 0GitHub stars: 3
- Ruview Model TrainingTrain RuView models — camera-free WiFlow pose (10 sensor signals, no labels), camera-supervised pose (MediaPipe + ESP32 CSI → 92.9% PCK@20, ADR-079), RuVector contrastive embeddings (AETHER, ADR-024), domain generalization (MERIDIAN, ADR-027), local SNN environment adaptation, plus GPU training on GCloud and Hugging Face publishing. Use when building, fine-tuning, evaluating, or shipping a model.Votes: 0GitHub stars: 3
- Scanpy K Dense AiStandard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.Votes: 0GitHub stars: 3
- ScanpyScanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.Votes: 0GitHub stars: 3
- Scholar Evaluation K Dense AiProvide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.Votes: 0GitHub stars: 3
- Scholar Evaluation論文、提案書、文献レビュー、方法論セクション、証拠の質、引用サポート、研究論文フィードバックのための構造化された学術的作業評価。Votes: 0GitHub stars: 3
- Sci StatisticsUse to enforce Science's statistics and reproducibility reporting — n and replication, test choice and assumptions, effect sizes with uncertainty, multiple-comparison control, randomization/blinding, and pre-registration where relevant.Votes: 0GitHub stars: 3
- Scikit Learn K Dense AiMachine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.Votes: 0GitHub stars: 3
- Scikit LearnMachine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.Votes: 0GitHub stars: 3
- ScveloRNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference.Votes: 0GitHub stars: 3
- Seaborn K Dense AiStatistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.Votes: 0GitHub stars: 3
- SeabornSeaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.Votes: 0GitHub stars: 3
- Senior Computer VisionComputer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.Votes: 0GitHub stars: 3
- Senior Data ScientistWorld-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled e...Votes: 0GitHub stars: 3
- Senior Ml Engineer../../../engineering-team/skills/senior-ml-engineer/SKILL.mdVotes: 0GitHub stars: 3
- Sensys Artifact EvaluationUse when packaging a SenSys artifact for the Artifact Evaluation Committee — choosing which of the three ACM badges (Available, Functional, Reproduced) to pursue, building a hardware-optional evaluation path for reviewers without your testbed, documenting energy and hardware provenance, and passing the smoke run that proves functionality.Votes: 0GitHub stars: 3
- Sensys ExperimentsUse when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth, on-device latency and memory, and same-hardware baselines, so the evidence meets SenSys's built-and-measured bar rather than a simulation or offline-benchmark one.Votes: 0GitHub stars: 3
- Serving Llms VllmvLLM: high-throughput LLM serving, OpenAI API, quantization.Votes: 0GitHub stars: 3
- Setting Up Experiment Tracking'Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to \"setup experiment tracking\" or \"initialize MLflow\". Trigger with relevant phrases based on skill purpose. 'Votes: 0GitHub stars: 3
- Setup EngineConfigure the project's game engine and version. Pins the engine in CLAUDE.md, detects knowledge gaps, and populates engine reference docs via WebSearch when the version is beyond the LLM's training data.Votes: 0GitHub stars: 3
- SetupProvisions the oracle ML inference daemon with onnxruntime via uv. Use when setting up local ONNX model inference for skill quality evaluation.Votes: 0GitHub stars: 3
- Sigcomm Artifact EvaluationUse when packaging an ACM SIGCOMM paper's code, traces, topologies, and configuration for the artifact-evaluation committee — choosing ACM badges (Artifacts Available, Evaluated, Results Reproduced) as claim calibration, building downscaled topologies and trace substitutes, and making a networking testbed result rebuildable by a reviewer.Votes: 0GitHub stars: 3
- Siggraph Artifact EvaluationUse when pursuing the Graphics Replicability Stamp (GRSI) or Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper, covering how graphics replicability differs from ACM artifact badging, what volunteers actually run, deterministic result reproduction, Software Heritage archiving, and the separate post-acceptance timing.Votes: 0GitHub stars: 3
- Sigir Artifact EvaluationUse when packaging code, run files, test collections, or judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources track paper, building reviewer-runnable IR repositories, run-file and qrels hygiene, licensing and datasheets, and single- vs double-anonymous handling.Votes: 0GitHub stars: 3