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Found 2,090 Skills
Send and receive SMS/MMS, handle opt-outs and delivery webhooks. Use for notifications, 2FA, or messaging apps.
Manage port-out requests when numbers are being ported away from Telnyx. List, view, and update port-out status. This skill provides Python SDK examples.
Python development best practices including PEP 8 style guidelines, type hints, docstring conventions, and common patterns. Use when writing or modifying Python code.
Stream call audio in real-time, fork media to external destinations, and transcribe speech live. Use for real-time analytics and AI integrations. This skill provides Python SDK examples.
Deliver Python backends across async FastAPI and Django or Flask service styles while keeping API design, validation, auth, and service behavior explicit.
Guide for Using RQData Data API. Used when you need to query RQData data interfaces and obtain financial data. Supports data queries for markets such as A-shares, Hong Kong stocks, futures, options, indices, funds, and convertible bonds, including HTTP API and Python API documentation.
Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.
Production Python coding standards with automatic version detection (3.10-3.13). Use when writing, reviewing, or refactoring Python to ensure adherence to modern type syntax, LBYL exception handling, pathlib operations, ABC-based interfaces, and production-tested patterns. Not Dagster-specific - applies to any Python project.
Python OpenTelemetry style: module-scope tracers/meters, decorators for bounded work, error spans, logs, and no wrappers.
Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards.
Common Python anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.
Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.