Find the perfect capability for your agent.
A Pythonic wrapper around RDKit with simplified interfaces and sensible defaults. Preferred for standard drug discovery workflows including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformer generation, and parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation.
Unified Python access to 40+ bioinformatics web services; use when you need to query multiple databases (e.g., UniProt/KEGG/ChEMBL/Reactome) with one consistent API in a single workflow, especially for cross-database analysis and identifier mapping.
Use Bio.PDB to parse and analyze protein structures (PDB/mmCIF) for structural bioinformatics tasks; use when you need structure parsing, geometry calculations, or structural comparison/superposition.
Systematic methodology to reduce skill file size through externalization, consolidation, and progressive loading patterns. Triggers: large skill, skill optimization, skill size, 300 lines, inline code, skill refactoring, skill context reduction, skill modularization Use when: skills exceed 300 lines, multiple code blocks (10+) with similar functionality, heavy Python inline with markdown, functions >20 lines embedded DO NOT use when: skill is under 300 lines and well-organized. DO NOT use when: creating new skills - use modular-skills instead. Consult this skill when skills-eval shows "Large skill file" warnings.
This skill should be used when the user asks to "create a Temporal workflow", "write a Temporal activity", "debug stuck workflow", "fix non-determinism error", "Temporal Python", "Temporal TypeScript", "Temporal Go", "Temporal Golang", "Temporal Java", "workflow replay", "activity timeout", "signal workflow", "query workflow", "worker not starting", "activity keeps retrying", "Temporal heartbeat", "continue-as-new", "child workflow", "saga pattern", "workflow versioning", "durable execution", "reliable distributed systems", or mentions Temporal SDK development.
Add comprehensive type hints to Python/TypeScript code to improve IDE support, catch errors early, and enable better AI code understanding
当你需要使用 Docker/Docker Compose 部署 XRK-AGT（含 Python 子服务端、Redis、MongoDB），或排查容器化环境问题时使用。
Interactive LeetCode-style teacher for technical interview preparation. Generates coding playgrounds with real product challenges, teaches patterns and techniques, supports Python/TypeScript/Kotlin/Swift, and provides progressive difficulty training for data structures and algorithms.
Comprehensive guide for building serverless applications with Cloudflare Workers. Use when developing Workers, configuring bindings, implementing runtime APIs, testing Workers, using Wrangler CLI, deploying to production, or building edge functions with JavaScript/TypeScript/Python/Rust.
Differential protein abundance testing using MSstats, limma, proDA, and scipy/statsmodels for Python. Multiple testing correction with BH FDR.
Select the right Python charting library (seaborn, matplotlib, graphviz) and produce publication-quality static visualizations. Use when creating charts, plots, graphs, diagrams, heatmaps, visualizations from data, or when choosing between matplotlib/seaborn/graphviz. Also triggers for network diagrams, flowcharts, dependency trees, state machines, and entity-relationship diagrams. For interactive browser-rendered charts or uploaded data exploration, defer to charting-vega-lite instead.
Develop and run Mojo code in Claude.ai containers. Handles installation, compilation, and execution. Use when writing Mojo code, benchmarking Mojo vs Python, or when user mentions Mojo, Modular, or MAX. Routes to Modular's official skills (mojo-syntax, mojo-python-interop, mojo-gpu-fundamentals) for language-specific correction layers.
Discovers and indexes Python code in skills, enabling cross-skill imports. Use when importing functions from other skills or analyzing skill codebases.
This skill should be used for Python scripting and Gemini image generation. Use when users ask to generate images, create AI art, edit images with AI, or run Python scripts with uv. Trigger phrases include "generate an image", "create a picture", "draw", "make an image of", "nano banana", or any image generation request.
Analyze CSV files, generate summary statistics, and create visualizations using Python and pandas. Use when the user uploads, attaches, or references a CSV file, asks to summarize or analyze tabular data, requests insights from CSV data, or wants to understand data structure and quality.
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
