Encuentra la capacidad perfecta para tu agente.
Use when working with Hatchling — configuring build system setup, pyproject.toml metadata, dependencies, entry points, build hooks, version management, wheel and sdist builds, package distribution, setuptools migration, or troubleshooting Hatchling build errors. Covers PEP 517/518/621/660 standards.
Use when creating a README for a Python package, preparing for PyPI publication, fixing README rendering errors found by twine check, choosing between README.md and README.rst, or configuring the readme field in pyproject.toml. Generates professional, PyPI-compliant README files.
Use when working with ty — running Python type checks, configuring ty.toml or pyproject.toml, suppressing diagnostics, interpreting error codes, targeting Python versions, or integrating ty with editors and CI. Covers CLI flags, configuration schema, rule severity, suppression comments, environment discovery, module resolution, and all installation methods.
Use when working with Astral's uv — managing Python project dependencies, creating PEP 723 scripts, installing tools, managing Python versions, configuring package indexes, or migrating from pip/poetry. Covers project initialization, virtual environments, workspace configuration, and CI/CD integration.
Universal release workflow. Auto-detects version files and changelogs. Supports Node.js, Python, Rust, Claude Plugin, and generic projects. Use when user says "release", "发布", "new version", "bump version", "push", "推送".
Set up CI/CD pipeline for Python package publishing to PyPI. Use when preparing to publish a package, when setting up automated releases, or when configuring GitHub Actions or GitLab CI for Python projects.
Use when building CLI applications with Typer — creating commands, defining arguments and options, composing subcommands, testing with CliRunner, or using advanced features like callbacks and autocompletion. Covers app creation, parameter types, subcommand composition, testing patterns, and output utilities.
Knowledge base of cross-platform compatibility tips, tricks, and helper patterns for Python scripts targeting Windows, Linux, macOS, CLI, TUI (Rich/Textual), and GUI environments. Use when writing Python scripts that must run on multiple platforms, when Rich or Typer output breaks on Windows, when dealing with Unicode/encoding issues, terminal detection, path separators, or console color support. Covers: stdout/stderr encoding guards, ANSI escape handling, Windows console quirks, terminal capability detection, and portable I/O patterns.
Python CLI application development with Typer and Rich. Activates on Typer, Rich, CLI tools, progress bars, terminal output, Annotated syntax, or command-line application requests. Covers app structure, parameter types, subcommands, async patterns, and testing with CliRunner.
Use when building Typer/Rich CLI applications or reviewing existing CLI code for correctness. Activates on requests involving Rich table rendering, console output handling, testing Rich-formatted output, or Typer command wiring. Prevents common AI mistakes — Rich table wrapping in non-TTY contexts, incorrect stderr/stdout separation, and integration pitfalls. Load alongside the typer and rich API reference skills.
Use when writing Python code with the Rich library — console output with markup, tables, progress bars, syntax highlighting, pretty printing, logging, or tracebacks. Covers Console, markup syntax, renderables (Panel/Table/Tree), Progress, Live, RichHandler, and the __rich_console__ protocol.
Use when building Typer/Rich CLI applications or reviewing existing CLI code for correctness. Activates on requests involving Rich table rendering, console output handling, testing Rich-formatted output, or Typer command wiring. Prevents common AI mistakes — Rich table wrapping in non-TTY contexts, incorrect stderr/stdout separation, and integration pitfalls. Load alongside the typer and rich API reference skills.
Use when building CLI applications with Typer — creating commands, defining arguments and options, composing subcommands, testing with CliRunner, or using advanced features like callbacks and autocompletion. Covers app creation, parameter types, subcommand composition, testing patterns, and output utilities.
Run or monitor the DPLA Python ingest orchestrator. Use when the user says run orchestrator, parallel ingest, ingest status, run hubs, orchestrator dry-run, or retry failed hubs. Covers venv, main entry point, status script, and logs.
Run or monitor the DPLA Python ingest orchestrator. Use when the user says run orchestrator, parallel ingest, ingest status, run hubs, orchestrator dry-run, or retry failed hubs. Covers venv, main entry point, status script, and logs.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
Python development best practices including PEP 8 style guidelines, type hints, docstring conventions, and common patterns. Use when writing or modifying Python code.
Rope-first workflow for ANY mechanical Python rename/move (functions/methods/classes/variables/modules/packages), updating imports/references via scripts/rope_refactor.py; use even for small renames; only fallback if rope fails/dynamic imports.
