Skill 디렉토리
모든 추천은 리포지토리, 감사, 설치 경로와 명확하게 연결됩니다.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
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 observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.
Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
Dependency management strategies for Golang projects — go.mod management, installing/upgrading packages, Minimal Version Selection, vulnerability scanning, outdated dependency tracking, binary size analysis, Dependabot/Renovate setup, conflict resolution, and go.work workspaces. Use when adding, removing, or upgrading Go dependencies, auditing vulnerabilities, resolving version conflicts, or setting up automated dependency updates.
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.
A collection of 39+ profession-specific Agent Skills plugins for Microsoft Copilot Cowork, installable via pre-built zips or sideloading.
Integrate an external, licensed Itasca PFC 5.0 fistPkg26 tree as a reproducible material-generation and compression/diametral/tension test baseline; use to validate its PFC5.0 layout, copy a private working case, preserve provenance, and audit ck/ct/dc/tt/ft extension points for asphalt workflows.
Use when the user wants an Abel causal map, graph exploration, target or candidate discovery, company/market mechanism read, or life/business investment decision read rather than auth setup or tradable strategy discovery.
Skill 디렉토리
모든 추천은 리포지토리, 감사, 설치 경로와 명확하게 연결됩니다.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
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 observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.
Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
Dependency management strategies for Golang projects — go.mod management, installing/upgrading packages, Minimal Version Selection, vulnerability scanning, outdated dependency tracking, binary size analysis, Dependabot/Renovate setup, conflict resolution, and go.work workspaces. Use when adding, removing, or upgrading Go dependencies, auditing vulnerabilities, resolving version conflicts, or setting up automated dependency updates.
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.
A collection of 39+ profession-specific Agent Skills plugins for Microsoft Copilot Cowork, installable via pre-built zips or sideloading.
Integrate an external, licensed Itasca PFC 5.0 fistPkg26 tree as a reproducible material-generation and compression/diametral/tension test baseline; use to validate its PFC5.0 layout, copy a private working case, preserve provenance, and audit ck/ct/dc/tt/ft extension points for asphalt workflows.
Use when the user wants an Abel causal map, graph exploration, target or candidate discovery, company/market mechanism read, or life/business investment decision read rather than auth setup or tradable strategy discovery.
Skill 디렉토리
모든 추천은 리포지토리, 감사, 설치 경로와 명확하게 연결됩니다.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
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 observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.
Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
Dependency management strategies for Golang projects — go.mod management, installing/upgrading packages, Minimal Version Selection, vulnerability scanning, outdated dependency tracking, binary size analysis, Dependabot/Renovate setup, conflict resolution, and go.work workspaces. Use when adding, removing, or upgrading Go dependencies, auditing vulnerabilities, resolving version conflicts, or setting up automated dependency updates.
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.
A collection of 39+ profession-specific Agent Skills plugins for Microsoft Copilot Cowork, installable via pre-built zips or sideloading.
Integrate an external, licensed Itasca PFC 5.0 fistPkg26 tree as a reproducible material-generation and compression/diametral/tension test baseline; use to validate its PFC5.0 layout, copy a private working case, preserve provenance, and audit ck/ct/dc/tt/ft extension points for asphalt workflows.
Use when the user wants an Abel causal map, graph exploration, target or candidate discovery, company/market mechanism read, or life/business investment decision read rather than auth setup or tradable strategy discovery.
Skill 디렉토리
모든 추천은 리포지토리, 감사, 설치 경로와 명확하게 연결됩니다.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
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 observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.
Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
Dependency management strategies for Golang projects — go.mod management, installing/upgrading packages, Minimal Version Selection, vulnerability scanning, outdated dependency tracking, binary size analysis, Dependabot/Renovate setup, conflict resolution, and go.work workspaces. Use when adding, removing, or upgrading Go dependencies, auditing vulnerabilities, resolving version conflicts, or setting up automated dependency updates.
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.
A collection of 39+ profession-specific Agent Skills plugins for Microsoft Copilot Cowork, installable via pre-built zips or sideloading.
Integrate an external, licensed Itasca PFC 5.0 fistPkg26 tree as a reproducible material-generation and compression/diametral/tension test baseline; use to validate its PFC5.0 layout, copy a private working case, preserve provenance, and audit ck/ct/dc/tt/ft extension points for asphalt workflows.
Use when the user wants an Abel causal map, graph exploration, target or candidate discovery, company/market mechanism read, or life/business investment decision read rather than auth setup or tradable strategy discovery.