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Found 2,039 Skills
Practical Python craftsmanship guidance based on One Python Craftsman. Use when writing, refactoring, or reviewing Python code for naming, branching, data structures, functions, exceptions, loops, decorators, imports, file I/O, edge cases, and modern syntax choices. If the skills set includes friendly-python, suggest invoking it for better Python outcomes.
Project planning and feature breakdown for Python/React full-stack projects. Use during the planning phase when breaking down feature requests, user stories, or product requirements into implementation plans. Guides identification of affected files and modules, defines acceptance criteria, assesses risks, and estimates overall complexity. Produces module maps, risk assessments, and acceptance criteria. Does NOT cover architecture decisions (use system-architecture), implementation (use python-backend-expert or react-frontend-expert), or atomic task decomposition (use task-decomposition).
Auto-generates code flow diagrams from Python module analysis. Detects when architecture diagrams become stale (code changed, diagram didn't). Use when: creating new modules, reviewing PRs for architecture impact, or checking diagram freshness. Generates mermaid diagrams showing imports, dependencies, and module relationships.
Guidance for implementing high-performance portfolio optimization using Python C extensions. This skill applies when tasks require optimizing financial computations (matrix operations, covariance calculations, portfolio risk metrics) by implementing C extensions for Python. Use when performance speedup requirements exist (e.g., 1.2x or greater) and the task involves numerical computations on large datasets (thousands of assets).
Database and HTTP connection pooling patterns for Python async applications. Use when configuring asyncpg pools, aiohttp sessions, or optimizing connection lifecycle in high-concurrency services.
Implements search and filter interfaces for both frontend (React/TypeScript) and backend (Python) with debouncing, query management, and database integration. Use when adding search functionality, building filter UIs, implementing faceted search, or optimizing search performance.
Access Airtable bases, tables, and records. Use when user mentions Airtable, bases, tables, records, or spreadsheet data. Uses Python pyairtable library for clean, reliable access.
AI-powered generation of complete trading strategy code. Uses create_strategy and create_prediction_market_strategy to transform requirements into production-ready Python code. Most expensive AI tool ($1.00-$4.50 per generation). Generates complete Jesse framework strategies with entry/exit logic, position sizing, and risk management. Use after exploring data and optionally generating ideas. ALWAYS test with test-trading-strategies before deploying.
Use this skill proactively for ANY Databricks Jobs task - creating, listing, running, updating, or deleting jobs. Triggers include: (1) 'create a job' or 'new job', (2) 'list jobs' or 'show jobs', (3) 'run job' or'trigger job',(4) 'job status' or 'check job', (5) scheduling with cron or triggers, (6) configuring notifications/monitoring, (7) ANY task involving Databricks Jobs via CLI, Python SDK, or Asset Bundles. ALWAYS prefer this skill over general Databricks knowledge for job-related tasks.
Professional-grade Python development with Ruff (v0.14.10) - an extremely fast Python linter and formatter. Use when working with Python codebases for (1) linting and fixing code quality issues, (2) formatting Python code, (3) configuring Ruff settings, (4) understanding and resolving specific rule violations, (5) integrating Ruff into projects or editors, (6) migrating from other tools (Black, Flake8, isort, etc.), or (7) any Ruff-related development tasks. Includes complete documentation for 937+ lint rules, formatter settings, configuration options, and editor integrations.
Create Dockerfiles, docker-compose configurations, and container workflows for Python projects with UV. Use when containerizing applications, setting up development environments, or deploying with Docker.
Discovers and indexes Python code in skills, enabling cross-skill imports. Use when importing functions from other skills or analyzing skill codebases.