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Use when independently assessing how bounded Python code could accomplish the same behavior with less maintained code, more Pythonic design, newer language/stdlib capabilities, or well-maintained ecosystem libraries.

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Design and run controlled experiments using the experiment-registry MCP server — domain-agnostic, pluggable, mechanically enforced. Use when you need evidence that a change actually improves behaviour.

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Scan Claude Code session transcripts to find the strongest user reactions to assistant instruction-following failures, reconstruct the triggering assistant output, and render a shareable terminal-style PNG artifact. Use when you want to surface and share a moment where the assistant completely missed what was asked — captures what they were doing, what Claude said, and how the user reacted. Triggers on: "rtfp", "read the fucking prompt", "find my worst AI moment", "make a rage screenshot from this session".

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Use when building or debugging Typer/Rich CLI applications. Activates on Rich table rendering, console output in non-TTY environments, CliRunner testing with Rich output, snapshot testing, Typer command wiring, exception chain prevention with AppExit/AppExitRich patterns, table width at 80-column wrapping, Progress/Live in non-interactive contexts, stderr/stdout separation, or force_terminal vs width configuration. Grounds AI-generated CLI code in verified correctness patterns and prevents known Typer/Rich integration mistakes.

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Configures CI/CD pipelines for automated Python package publishing to PyPI or GitLab Package Registry. Use when creating GitHub Actions or GitLab CI release workflows, setting up trusted publishing or API token-based PyPI authentication, configuring version management with git tags and hatch-vcs, writing pyproject.toml publishing metadata, testing packages against TestPyPI, or documenting the release process for a Python project.

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Use when building dependency-free Python 3.11+ scripts for airgapped, stdlib-only, or restricted environments where third-party package installation is prohibited — triggers on "stdlib-only", "airgapped", "no dependencies", "no internet", "restricted environment", or confirmed environments where external packages cannot be installed.

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Pytest testing patterns for Python — fixtures, behavioral naming, behavior/risk-driven coverage, property-based testing with Hypothesis when useful, and mutation testing when justified. Use when writing tests, designing fixtures, configuring coverage, or applying parametrize, async testing, or property-based strategies.

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Investigate pytest failures without treating tests or implementation as automatic authority. Use when diagnosing regressions, debugging test errors, or deciding whether to fix a test or code; trace the contract, product, fixture, and observation before choosing an evidence-backed correction.

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Use when independently hunting Python code smells, deviations from engineering standards, maintenance hazards, or suspicious design choices in a bounded review surface.

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Shared Python 3.11+ development rules — type safety and the boundary policy for `Any` (ty, native generics, Protocol, TypeIs, Pydantic), layered architecture and SOLID, error handling, security, performance, identifier naming, PEP 723 script dependencies, Rich/Typer output, tooling defaults (uv, ruff, ty, hatchling, pytest), and testing requirements (behavioral coverage, TDD). Activates when any Python skill or agent needs the shared rules for implementation, code review, refactoring, or test authoring.

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Use when the user asks for a read-only, actionable audit of Python code quality, modernization, maintenance burden, ecosystem opportunities, or Pythonic design across a PR, git diff, staged/unstaged changes, files, or directories.

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Generates professional PyPI-compliant README files in Markdown or reStructuredText. Use when creating a Python package README for PyPI publication, converting between README.md and README.rst formats, validating markup with twine check before publishing, configuring the readme field in pyproject.toml, integrating sphinx-readme to generate PyPI-compatible RST from Sphinx docs, troubleshooting rendering errors on PyPI, or previewing README rendering locally with grip or docutils.

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Configures and runs git hooks with prek (or the pre-commit it replaces — same `.pre-commit-config.yaml`). Use when adding or troubleshooting git hooks, writing a `prepare-commit-msg` or `commit-msg` stage hook, or authoring `.pre-commit-hooks.yaml` for hook distribution.

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Use when writing Python scripts that must run on Windows, Linux, and macOS — especially when Rich or Typer output breaks on Windows, when dealing with Unicode/encoding errors, ANSI escape handling, terminal detection, path separators, or console color support. Provides verified cross-platform patterns covering stdout/stderr encoding guards, Windows console quirks, terminal capability detection, and portable I/O for CLI, TUI (Rich/Textual), and GUI environments.

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Use when building CLI applications with Typer and Rich — creating commands with Annotated parameter syntax, defining arguments and options, composing subcommands, async concurrent CLI tasks with semaphores, testing with CliRunner, PEP 723 shebang scripts, progress bars, Rich terminal output, or non-TTY display width handling.

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Provides Hatchling build backend guidance for Python packaging — use when configuring pyproject.toml metadata, build targets (wheel, sdist, binary), file selection with git-style globs, build hooks, metadata hooks, version management (code/regex/env sources), editable installs, the hatch-vcs plugin, plugin development, build environment setup (UV/pip/Cython), setuptools migration, or troubleshooting Hatchling errors. Covers PEP 517/518/621/660 standards and context variable interpolation.

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Analyze specific pytest failures using authoritative contracts and a cited product/test evidence chain. Use for failing test names or pytest output, distinguishing product, interface, oracle, and harness defects without automatically changing expectations or forcing an architectural redesign.

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Configures pyproject.toml and Python packaging using PEP 517/518/621/660/723 standards. Use when creating or updating pyproject.toml, selecting a build backend (hatchling/setuptools/flit), configuring ruff, ty, mypy, pytest, or coverage tool sections, setting up dependency constraints or optional extras, defining CLI entry points, configuring pre-commit hooks, establishing src-layout directory structure, or preparing a package for PyPI publishing.

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Runs structured Python cleanup and modernization — static analysis via prek/ruff, smell investigation to root cause, typed-boundary hardening by inventorying Any usage, and modernization within the project's requires-python lane. Use when refactoring Python code, removing dead code, hardening type boundaries, or running a modernization pass on a file or scope.

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Runs deterministic Python quality checks against a path or scope — formatting, linting, type checking, and typing-boundary policy. Use when checking or fixing code quality via prek, ruff, ty, pytest, or the check-typing-boundaries policy script. Reports results grouped by category; fixes only when explicitly requested.

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Use when working with Python tooling — uv package management, Hatchling build backend, ty or mypy type checker configuration, ruff linting, pre-commit hook setup, TOML read-write with tomlkit or tomllib, or PyPI packaging and release workflows. Routes to standalone specialist skills for deep dives on any single tool.

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Review pytest test effectiveness against authoritative contracts, relevant faults, behavior-preserving refactors, realistic boundaries, isolation, and diagnostics. Use for test-quality audits, coverage-gap reviews, regression-test changes, or mocking reviews; preserve existing project gates rather than inventing coverage targets.

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Provides agent selection criteria, workflow patterns (TDD, feature addition, code review, refactoring, debugging), quality gates, and python-cli-architect vs stdlib-scripting routing for Python engineering tasks. Activated by python-engineering:orchestrate at Step 1 before any task is routed. Also activates when an orchestrator needs to select the correct Python specialist agent or chain agents across a multi-step Python workflow.

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Use when a Python task explicitly requires test-driven development, tests-first implementation, or a red-green-refactor workflow.

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