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SkillsMP は CRAG666/dotfiles から 26 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 26 件中 26 件を表示しています。
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…
原文の言語: 英語
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation…
原文の言語: 英語
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or…
原文の言語: 英語
Convert heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction,…
原文の言語: 英語
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots…
原文の言語: 英語
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…
原文の言語: 英語
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas,…
原文の言語: 英語
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,…
原文の言語: 英語
Search 11 academic literature APIs for papers, preprints, citations, and open-access full text, and return results with reproducible provenance. Covers PubMed, PMC (full text), Europe PMC (full-text and preprint search), bioRxiv, medRxiv, arXiv, OpenAlex,…
原文の言語: 英語
Prior art patent search via Google Patents' JSON endpoint and Lens.org, with PDF download. Use when the user asks to search patents, prior art, "anterioridad", "patentes ancla", freedom-to-operate, novelty context, or patent landscaping. Covers query syntax,…
原文の言語: 英語
High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.
原文の言語: 英語
High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU…
原文の言語: 英語
STRICT: stdlib idioms are mandatory — reinventing a stdlib feature is a defect, not a style choice. Apply whenever the user writes, refactors, optimizes, or reviews Python, including one-liners and code-review feedback. Verify non-trivial stdlib APIs against…
原文の言語: 英語
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,…
原文の言語: 英語
Create and audit truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal…
原文の言語: 英語
Use whenever the user writes, drafts, revises, edits, polishes, or translates scientific or academic prose in ENGLISH - Q1 research articles, theses, dissertations, abstracts, introductions, methods, results, discussions, conclusions, acknowledgments,…
原文の言語: 英語
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
原文の言語: 英語
Machine 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…
原文の言語: 英語
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.
原文の言語: 英語
Statistical 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…
原文の言語: 英語
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.
原文の言語: 英語
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or…
原文の言語: 英語
Personal default directives for any generated or edited code: check for an existing library before writing anything, then emit the smallest correct code with no AI filler. Apply whenever writing, editing, refactoring, or emitting code in any language — new…
原文の言語: 英語
STRICT structure-and-complexity layer for non-trivial code work: designing systems, writing new modules, refactoring, implementing algorithms, or making structural decisions. Complements code-style-defaults (form of the output) and python-native (Python…
原文の言語: 英語
Enforces rigorous scientific methodology for machine learning experiments intended to support publication-grade claims (Q1 journals, conference papers, regulated decisions). Use this skill when designing an ML pipeline, splitting datasets, evaluating…
原文の言語: 英語
Usar siempre que el usuario escriba, redacte, revise o traduzca texto científico/académico en ESPAÑOL: artículos, papers, tesis, abstracts, introducciones, metodologías, resultados, discusión, conclusiones, agradecimientos, revisiones de literatura, defensas,…
原文の言語: スペイン語