Total 51,102 skills
Showing 12 of 51102 skills
Scaffold brand-new DatoCMS plugin projects with datocms-plugin-sdk and connect(). Use when users want to create a new plugin folder from scratch, bootstrap the Vite/React package structure, choose initial plugin surfaces such as field extensions, config screens, sidebars, pages, asset sources, or dropdown actions, and wire the first hook implementation. Prefer `datocms-plugin-builder` for edits to an existing plugin project.
Design or restyle DatoCMS plugins so they look and feel native to the DatoCMS UI. Use when users ask to make a plugin match the DatoCMS dashboard, polish plugin config screens, pages, sidebars, panels, modals, forms, tables, empty states, or overall plugin layout structure. This skill owns DatoCMS plugin design-system work, native-look restyling, and UI density or spacing cleanup. Prefer `datocms-react-ui` when a public component exists, and otherwise use raw React and CSS that reproduce DatoCMS spacing, typography, density, color, and interaction patterns without importing private CMS classes.
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
Run the trigger evaluation pipeline — classify, analyze, and optionally compare against a baseline. Only run when explicitly asked — evals are expensive.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.
Load automatically when planning, researching, or implementing Medusa storefront features (calling custom API routes, SDK integration, React Query patterns, data fetching). REQUIRED for all storefront development in ALL modes (planning, implementation, exploration). Contains SDK usage patterns, frontend integration, and critical rules for calling Medusa APIs.
Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
GraphQL performance optimization and best practices for building scalable APIs. This skill should be used when writing, reviewing, or refactoring GraphQL schemas, resolvers, or query execution code. Triggers on tasks involving GraphQL APIs, resolver optimization, query performance, or data fetching patterns.