biorxiv-database

21.8k

davila7

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

senior-data-scientist

21.8k

davila7

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

GeoMaster

10.8k

K-Dense-AI

Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, and 7 programming languages (Python, R, Julia, JavaScript, C++, Java, Go) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.

biorxiv-database

10.8k

K-Dense-AI

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

moai-lang-python

776

modu-ai

Python 3.13+ development specialist covering FastAPI, Django, async patterns, data science, testing with pytest, and modern Python features. Use when developing Python APIs, web applications, data pipelines, or writing tests.

moai-lang-r

776

modu-ai

R 4.4+ development specialist covering tidyverse, ggplot2, Shiny, and data science patterns. Use when developing data analysis pipelines, visualizations, or Shiny applications.

pycse

273

Jkitchin Pycse Pycse

Python computations in science and engineering (pycse) - helps with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments (DOE), Latin hypercube sampling, surface response modeling, and neural network-based UQ with DPOSE. Use when working with numerical optimization, data fitting, experimental design, or uncertainty analysis.

data-science

75

htlin222

Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.

jupyter

63

OpenHands

Read, modify, execute, and convert Jupyter notebooks programmatically. Use when working with .ipynb files for data science workflows, including editing cells, clearing outputs, or converting to other formats.

mlops-engineer

39

404kidwiz

Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "model deployment", "feature store", "model versioning", "ML monitoring", "Kubeflow", "MLflow".

python-pro

36

zenobi-us

Expert Python developer specializing in modern Python 3.11+ development with deep expertise in type safety, async programming, data science, and web frameworks. Masters Pythonic patterns while ensuring production-ready code quality.

ai-ml-data-science

34

vasilyu1983

End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).

statistical-analysis

29

omer-metin

Comprehensive statistical analysis for research, experiments, and data science. Covers hypothesis testing, effect sizes, confidence intervals, Bayesian methods, regression, and advanced techniques. Emphasizes correct interpretation and avoiding common statistical mistakes. Use when \", \" mentioned.

marketing

29

omer-metin

World-class marketing expertise combining Seth Godin's permission marketing philosophy, Neil Patel's data-driven growth tactics, and the strategic frameworks from brands like Apple, Nike, and Dollar Shave Club. Marketing is the art and science of creating customers. Great marketing isn't about shouting louder — it's about being more relevant. The best marketers understand that attention is earned, not bought. They build systems that compound, create word-of-mouth, and turn customers into advocates. Marketing is the bridge between what you've built and the people who need it. Use this skill when any of the following terms are mentioned: marketing, campaign, go-to-market, gtm, launch, promotion, advertising, ads, acquisition, demand gen, lead gen, channel, funnel, conversion, cac, customer acquisition, reach, awareness, consideration, demand-generation, analytics, channels.

scientific-method

29

omer-metin

The scientific method applied to computational research, data science, and experimental software engineering. Covers hypothesis formulation, experimental design, controls, reproducibility, and avoiding common methodological pitfalls like p-hacking, HARKing, and confirmation bias. Use when ", " mentioned.

computer-scientist-analyst

28

rysweet

Analyzes events through computer science lens using computational complexity, algorithms, data structures, systems architecture, information theory, and software engineering principles to evaluate feasibility, scalability, security. Provides insights on algorithmic efficiency, system design, computational limits, data management, and technical trade-offs. Use when: Technology evaluation, system architecture, algorithm design, scalability analysis, security assessment. Evaluates: Computational complexity, algorithmic efficiency, system architecture, scalability, data integrity, security.

senior-data-scientist

28

hainamchung

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

biorxiv-database

26

lifangda

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

python

23

Mindrally

Expert in Python development with best practices across web, data science, and automation

kp-applied-domain

23

Tibsfox

Domain knowledge for 10 applied subjects: computer science, data science, world languages, psychology, environmental science, nutrition, economics, creative writing, logic, digital literacy. Use when generating Applied & Practical tier pack content.

tecton

22

ferdinandyb

Run Tecton plan and tests via Pants in the data-science repo. Handles long-running commands with proper output capture to avoid truncation.

data-science

16

travisjneuman

Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.

pymatgen

15

jkitchin

Comprehensive guidance for using pymatgen (Python Materials Genomics) for computational materials science. Covers structure creation and manipulation, file I/O (CIF, POSCAR, XYZ), symmetry analysis, Materials Project API integration, phase diagrams, electronic structure analysis, and DFT input generation. Use when working with crystal structures, materials properties, computational chemistry calculations, or materials databases. Triggers include 'pymatgen', 'crystal structure', 'Materials Project', 'CIF file', 'POSCAR', 'band structure', 'phase diagram', or materials analysis tasks.

biorxiv-database

15

oimiragieo

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

DAG Development

14

nealcaren

Develop causal diagrams (DAGs) from social-science research questions and literature, then render publication-ready figures using Mermaid, R, or Python.

data-science

10

justaddcoffee

Statistical analysis strategies and data exploration techniques

streamlit

9

TerminalSkills

Streamlit turns Python scripts into interactive web applications for data science. Learn to build dashboards with widgets, charts, file uploads, caching, multi-page apps, and deployment to Streamlit Cloud.

exploratory-data-analysis

7

andikarachman

Detect file types and perform format-specific EDA across 200+ scientific data formats. Use when /ds:eda encounters non-tabular or unfamiliar data files, or when format-specific analysis guidance is needed.

statistical-analysis

7

andikarachman

Guided statistical analysis with test selection, assumption checking, power analysis, and APA reporting. Use when /ds:experiment needs to design comparison protocols, validate assumptions, or report results.

scikit-learn

7

andikarachman

Scikit-learn API patterns for preprocessing, pipelines, model selection, and evaluation. Use when /ds:experiment needs to build sklearn pipelines, tune hyperparameters, or evaluate models.

tuning-hyperparameters

7

andikarachman

Hyperparameter tuning workflow reference -- strategy selection, Bayesian optimization with Optuna, search space design, and result analysis. Use when /ds:experiment needs to choose a tuning strategy, design search spaces, or analyze tuning runs.

target-leakage-detection

7

andikarachman

Detect target leakage in feature sets by checking temporal validity, feature-target correlation, and information flow. Use before training any model.

data-quality-frameworks

7

andikarachman

Data quality validation with Great Expectations, dbt tests, and data contracts. Use when building formal validation rules, expectation suites, or data contracts for repeatable quality gates.

aeon

7

andikarachman

Aeon API patterns for time series machine learning -- classification, regression, clustering, anomaly detection, segmentation, and similarity search. Use when /ds:experiment needs time-series-specific ML algorithms (ROCKET, InceptionTime, DTW classifiers), or /ds:eda needs temporal feature extraction (Catch22, ROCKET features) or change point detection. For classical statistical forecasting (ARIMA/SARIMAX) use statsmodels; for tabular ML pipelines use scikit-learn; for visualization use matplotlib.

model-card

7

andikarachman

Generate standardized model documentation following HuggingFace Model Card and NVIDIA Model Card++ formats. Use when preparing a model for deployment or handoff.

eda-checklist

7

andikarachman

Systematic exploratory data analysis checklist covering structure, quality, distributions, relationships, and target analysis. Use when starting EDA on any dataset.

split-strategy

7

andikarachman

Select and implement appropriate train/validation/test split strategies based on data characteristics. Use when designing the evaluation framework for a model.

pandas-pro

7

andikarachman

Pandas API patterns for DataFrame operations, data cleaning, aggregation, merging, and performance optimization. Use when generating pandas code for data loading, manipulation, or profiling in /ds:eda, /ds:preprocess, or /ds:experiment.

statsmodels

7

andikarachman

Statsmodels API patterns for OLS, GLM, discrete choice, time series (ARIMA/SARIMAX), and diagnostics. Use when /ds:experiment needs statsmodels model fitting, diagnostics, or time-series forecasting, or /ds:eda needs VIF and stationarity checks. For guided test selection and APA reporting use statistical-analysis.

data-preprocessing

7

andikarachman

Pre-model data preparation pipelines for cleaning, validation, transformation, and ETL orchestration. Use when raw data needs deduplication, schema validation, format conversion, or quality assurance before EDA or modeling.

machine-learning-engineer

7

louloulin

Machine learning model development, training, deployment and MLOps expert

julia-scientific

7

plurigrid

Julia package equivalents for 137 K-Dense-AI scientific skills. Maps Python bioinformatics, chemistry, ML, quantum, and data science packages to native Julia ecosystem.

statistical-tests

7

andikarachman

Select appropriate statistical tests based on data type, distribution, and hypothesis. Use when comparing groups, testing relationships, or validating assumptions.

setup

7

andikarachman

Check Python environment for required DS/ML libraries and report versions or missing packages. Use when setting up a new project or debugging import errors.

shap

7

andikarachman

SHAP API patterns for model interpretability -- explainer selection, feature attribution, and visualization. Use when /ds:experiment needs per-prediction explanations, global feature importance, or interaction analysis. For built-in tree importance and permutation importance use scikit-learn; for coefficient-based interpretation use statsmodels.

reproducibility-checklist

7

andikarachman

Verify that an ML experiment meets reproducibility requirements: random seeds, library versions, data hashes, environment capture. Use when reviewing experiments before shipping.

experiment-tracking

7

andikarachman

Standard format for logging ML experiments including hypothesis, config, results, and learnings. Use when running experiments to maintain a consistent record.

polars

7

andikarachman

Polars expression API for high-performance DataFrame operations, lazy evaluation, joins, aggregations, and I/O. Use as a parallel alternative to pandas-pro when working with large datasets or generating Polars code for data loading, manipulation, or profiling in /ds:eda, /ds:preprocess, or /ds:experiment.