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CRAG666/dotfiles

SkillsMP 已收集 CRAG666/dotfiles 中的 26 个 Skill。打开任一 Skill 可查看来源和详情。

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已收集 skills
26
GitHub 星标
47
GitHub Forks
4

这个仓库中的 skills

2 个职业分类 · 已分类 8%

已展示 26 / 26 个已收集 Skill。

职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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,…

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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,…

原文语言:英语

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职业分类
未分类
描述

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,…

原文语言:英语

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职业分类
未分类
描述

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,…

原文语言:英语

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职业分类
未分类
描述

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,…

原文语言:英语

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职业分类
未分类
描述

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.

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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,…

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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,…

原文语言:英语

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职业分类
未分类
描述

Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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.

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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.

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
未分类
描述

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…

原文语言:英语

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职业分类
其他生物科学家
描述

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…

原文语言:英语

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职业分类
技术写作员
描述

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,…

原文语言:西班牙语

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已展示 26 / 26 个已收集 Skill。