Development Claude Skills
8 676 development skills from 759 authors. They write and change code. Half of them fit into 1 830 tokens or less — that is what one costs your context window when the agent loads it. 1 213 ship runnable scripts rather than instructions alone. 42 of them cannot work without an MCP server, most often rube. We also found 1 172 copies of these same skills sitting in other people's repositories — counted once here, not 1 172 times.
8 676 unique 759 authors 5 252 updated this month 1 369 from vendors
Hook recipes and working examples — plugin hooks, frontmatter hooks in skills/agents/commands, prompt-based LLM hooks, and complete code examples in Python and Node.js. Use when building hook scripts, integrating hooks into plugins, implementing prompt-based hooks, or looking for hook configuration patterns.
Mermaid diagram syntax reference for all diagram types — flowchart, sequence, class, state, ER, gantt, git graph, mindmap, timeline, user journey, pie, quadrant, XY chart, block, sankey, C4, kanban, and more. Use when constructing or debugging any Mermaid diagram definition.
Use when writing asyncio Python code — async/await coroutines, concurrent I/O with asyncio.gather, task creation and cancellation, semaphore rate limiting, producer-consumer queues, async context managers, async generators, WebSocket servers, aiohttp web scraping, async database operations, run_in_executor for blocking calls, or testing async code with pytest-asyncio. Covers FastAPI and aiohttp patterns, synchronization primitives, timeout handling, and common pitfalls like event loop blocking and missing await.
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.
Use when creating a new feature development task — scaffolds a structured task file at .claude/tasks/{feature-name}.md with phased breakdown (Design, Implementation, Testing, Documentation), acceptance criteria, context preservation, and TaskCreate tracking. Activates on "create a feature task", "set up development tracking", "plan a feature implementation", or when preparing work for python-cli-architect or python-pytest-architect agents.
Structured 6-phase Python debugging workflow covering problem intake, scoping, hypothesis formation, systematic investigation, root-cause analysis, and fix implementation. Use when diagnosing tracebacks, test failures, AttributeError, TypeError, intermittent failures, async/await issues, or any unexpected Python behavior. Applies a dual-hypothesis approach (implementation bug vs test bug), minimal reproduction isolation, data-flow tracing, and produces a structured Bug Investigation Report with confirmed root cause and regression test.
Use before any CLI/TUI display code is written, modified, or audited — runs the 7-stage discipline (Context, Register, Shape brief, Implement, Critique, Audit, Polish) for Typer, Rich, Textual, and Questionary work, grounded in per-project PRODUCT.md and DESIGN.TUI.md/DESIGN.md. Triggers on output formatting, display design, interactive prompts, visual consistency, TUI layout, progress display, dashboard design, design audit, design polish, design critique, shape brief, register decision (brand-cli vs product-cli), and AI-slop checks.
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.
MkDocs documentation project reference covering CLI commands, mkdocs.yml configuration, Material theme setup, and plugin integration. Bundled references include complete CLI parameters, all mkdocs.yml settings with valid values, Material theme customization options, and plugin configs for mkdocstrings, mermaid2, mkdocs-gen-files, mkdocs-literate-nav, and mkdocs-typer2. Use when initializing a MkDocs site, configuring mkdocs.yml, customizing the Material theme, integrating plugins, building static docs from Markdown, or generating API documentation from Python docstrings.
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.
Configures and implements git hooks using pre-commit or prek (Rust-based drop-in replacement) for automated code quality checks, formatting, linting, and commit message processing. Use when setting up .pre-commit-config.yaml, implementing prepare-commit-msg or commit-msg stage hooks, designing .pre-commit-hooks.yaml hook definitions for distribution, troubleshooting hook installation or execution, or managing hook stages across multi-language projects.
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.
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.
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.
Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.
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.
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.
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.
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.
Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.
Python web and API development enforcing strict route/domain/data layer separation, Pydantic v2 strict request-response models, edge-resolved auth, and async-safe HTTP clients. Use when working with FastAPI, Starlette, Django, Flask, HTTP endpoints, request models, authentication flows, async handlers, or any Python web framework task.
Validates and corrects Python shebangs and PEP 723 inline script metadata by applying four shebang-selection rules. Use when auditing or fixing shebangs in Python files — choosing between plain python3 and the uv shebang for standalone scripts with external dependencies, enforcing correct uv flag ordering (--quiet before run subcommand), adding or removing PEP 723 metadata blocks to match actual import requirements, checking execute bit presence, or avoiding redundant transitive dependencies when typer is declared (typer bundles rich and shellingham automatically).
Executes the implementation phase of the python-engineering stinkysnake modernization workflow. Use when stinkysnake phases 1-8 are complete — modernization plan reviewed, interfaces designed, and failing tests written. Implements functions in dependency order (types, data structures, utilities, core logic, integration, entry points) applying modern Python patterns (Protocol, dataclass, Pydantic, modern type annotations, httpx, orjson). Runs iterative pytest loops until all tests pass, then verifies with static analysis via prek or ruff. Success criteria — all tests pass, no type errors, no lint errors, coverage meets project threshold.
Routes Python engineering tasks to specialist skills by matching trigger patterns before any architecture, plan, or code is written. Use when working with Typer CLI frameworks, Rich or Textual terminal UIs, CLI UI/UX design, questionary prompts, FastMCP/MCP servers, ty type checker, uv package manager, Hatchling build backend, TOML editing, pre-commit/prek hooks, async Python, PyPI packaging, complex linting, technical debt modernization, testing workflows, feature development, or stdlib-only scripting.
Shared Python 3.11+ development standards covering type safety (ty, native generics, Protocol, TypeIs), layered architecture, error handling, performance, identifier naming, UI/CLI patterns (Rich/Typer), testing requirements (pytest, 80% coverage, TDD), and quality gates. Activates when any Python skill or agent needs to apply shared standards for implementation, code review, refactoring, or test authoring.
Handles TOML configuration file operations in Python using tomlkit for comment-preserving read-modify-write cycles. Use when reading or writing pyproject.toml or any .toml config file, selecting between tomlkit and tomllib, modifying TOML while preserving comments and whitespace, implementing atomic config file updates, integrating TOML with Python dataclasses, handling TOML parse errors, or applying XDG base directory patterns for config file locations.
Use when working with ty — the Astral Python type checker. Activates for running type checks, interpreting diagnostic error codes, suppressing ty errors with inline comments, configuring ty.toml or pyproject.toml, resolving unresolved imports, targeting Python versions, and integrating ty into editors or CI. Covers CLI flags, configuration schema, rule severity, environment discovery, module resolution, and all installation methods including uvx and uv add --dev.
Use when working with Astral's uv — the fast Python package and project manager replacing pip, pipx, pyenv, poetry, and virtualenv. Activates for project initialization, dependency management, PEP 723 inline scripts, virtual environments, Python version management, workspace and monorepo configuration, tool installation via uvx, building and publishing packages, Docker and CI/CD integration, package index configuration, SBOM export, and migrating from pip, poetry, pipx, pyenv, or conda.
Use when building async APIs, concurrent systems, or I/O-bound Python applications requiring non-blocking operations. Covers asyncio, async/await patterns, task scheduling, synchronization primitives, and high-performance concurrent programming.
Use when reviewing Python code for modernization opportunities, writing new Python 3.11+ code to ensure modern patterns, or refactoring legacy code to current idioms. Covers proper types, DRY, SRP, framework patterns, and idiomatic Python improvements.
Use when orchestrating a Python development task via specialized agents. Activates on "build a Python CLI", "add a feature", "write tests", "refactor Python code", "debug Python", "code review", or any multi-agent Python workflow. Invoke as /orchestrate with a task description or alone to use conversation context.
Use when setting up automated code quality checks on git commit, configuring .pre-commit-config.yaml, implementing git hooks for formatting or linting, creating prepare-commit-msg hooks, or distributing a tool as a pre-commit hook. Covers pre-commit and prek for multi-language projects.
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 the python-cli-architect agent needs project structure reference and task completion quality gates for Python CLI projects. Loaded automatically by the python-cli-architect agent — covers package layout, Hatchling configuration, and implementation quality criteria.
Debug functional issues in Python code using specs, logs, and observed behavior. Use when a feature isn't working as specified, when investigating runtime errors, or when scoping a problem before implementing a fix.
Python 3 development plugin documentation index. Load when needing to read about Python implementation patterns, task file conventions, or stdlib scripting.
Configure pyproject.toml and Python packaging for distribution. Use when setting up a new Python package, when configuring build tools and dependencies, or when preparing a project for PyPI publishing.
Comprehensive Python code review checking patterns, types, security, and performance. Use when reviewing Python code for quality issues, when auditing code before merge, or when assessing technical debt in a Python codebase.
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 searching a codebase by behavior, intent, or natural language description rather than exact identifiers. Activates the CocoIndex Code MCP server for semantic code search — finding implementations without knowing exact names, exploring unfamiliar codebases, or locating code by concept.
Validate Python shebangs and PEP 723 inline script metadata. Use when checking if Python files have correct shebangs based on their dependency requirements, when fixing incorrect shebang patterns, or when adding PEP 723 script blocks to standalone scripts with external dependencies.
LAST RESORT for stdlib-only Python 3.11+ scripts in CONFIRMED restricted environments (airgapped systems, no uv, no internet access). Creates portable dependency-free scripts using argparse, logging, config management (JSON/TOML/INI), and cross-platform patterns. Use ONLY when environment restrictions prevent Typer+Rich with PEP 723. Triggers on "stdlib-only script", "no dependencies", "airgapped", "restricted environment", "portable script no network". For standard CLI development, use python-cli-architect with Typer+Rich instead.
Use when reading or writing pyproject.toml or .toml config files in Python, editing TOML while preserving comments and formatting, designing configuration file formats for Python tools, working with tomlkit or tomllib, or implementing atomic config file updates.
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 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 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.
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".
Summarize files by reading content, extracting key passages, and applying type-specific strategies. Activates on summarize this file, what's in this file, describe this codebase, file summary, analyze this file, tl;dr this file, what does this code do, explain this config, break down this script. Routes to strategies for code, config, data, documentation, markup, and binary files based on extension and word count.