Opinionated Python Development Skill Skill

Opinionated Python Development Skill

Role Identification (Mandatory)

The model must identify its ROLE_TYPE and echo the following statement:

My ROLE_TYPE is "<the role type>". I follow instructions given to "the model" and "<role name>".

Where:

<the role type> is either "orchestrator" or "sub-agent" based on the ROLE_TYPE identification rules in CLAUDE.md
<role name> is "orchestrator" if ROLE_TYPE is orchestrator, or "sub-agent" if ROLE_TYPE is sub-agent

Example for orchestrator:

My ROLE_TYPE is "orchestrator". I follow instructions given to "the model" and "orchestrator".

Example for sub-agent:

My ROLE_TYPE is "sub-agent". I follow instructions given to "the model" and "sub-agent".

Orchestration guide for Python development using specialized agents and modern Python 3.11-3.14 patterns.

Skill Architecture

Bundled Resources (Included in This Skill)

Reference Documentation:

User Project Conventions - Extracted conventions from user's production projects (MANDATORY for new projects)
Modern Python Modules - 50+ library guides with usage patterns and best practices
Tool & Library Registry - Development tools catalog for linting, testing, and build automation
API Reference - API specifications and integration guides
Python Development Orchestration - Detailed workflow patterns for TDD, feature addition, refactoring, and code review

Command Templates and Guides (commands/):

Reference material for creating slash commands (NOT the actual slash commands)
Command templates and patterns for development
Testing and development workflow guides
See ../../commands/ directory for details

Scripts and Assets:

Example scripts demonstrating patterns
Configuration templates and boilerplate

Bundled Components (This Plugin)

This plugin bundles the core agents and workflows needed for Python 3.11+ development and SAM-style execution:

Agents (bundled under ./plugins/python3-development/agents/): python-cli-architect, python-pytest-architect, python-cli-design-spec, swarm-task-planner, plus SAM workflow agents (feature research/analysis/verification).
Skills (bundled): modernpython, shebangpython, and the SAM workflow skills (add-new-feature, implement-feature, start-task, complete-implementation).

System Tools (install via package manager or uv):

uv - Python package and project manager (required)
ruff - Linter and formatter
pyright - Type checker (Microsoft)
mypy - Static type checker
pytest - Testing framework
pre-commit - Git hook framework
mutmut - Mutation testing (for critical code)
bandit - Security scanner (for critical code)

Installation Notes:

Agents and slash commands must be installed separately in their respective directories
This skill provides orchestration guidance and references; agents perform actual implementation
Use the uv skill for comprehensive uv documentation and package management guidance

Core Concepts

Python Development Standards

This skill provides orchestration patterns, modern Python 3.11+ standards, quality gates, and reference documentation for Python development.

Note on command templates:

This plugin includes command templates under ./commands/ as reference material. For actual invocation, prefer skills (e.g., modernpython, shebangpython).

Reference Documentation:

Modern Python modules (50+ libraries)
Tool and library registry with template variable system
API specifications
Working configurations for pyproject.toml, ruff, mypy, pytest

Docstring Standard: Google style (Args/Returns/Raises sections). See User Project Conventions for ruff pydocstyle configuration (convention = "google").

CRITICAL: Pyproject.toml Template Variables:

All pyproject.toml examples use explicit template variables (e.g., {{project_name_from_directory_or_git_remote}}) instead of generic placeholders. The model MUST replace ALL template variables with actual values before creating files. See Tool & Library Registry sections 18-19 for:

Complete variable reference and sourcing methods
Mandatory rules for file creation
Validation and verification procedures

Script Dependency Trade-offs

Understand the complexity vs portability trade-off when creating Python CLI scripts:

Scripts with dependencies (Typer + Rich via PEP 723):

Benefits:
- Less development complexity - Leverage well-tested libraries for argument parsing, formatting, validation
- Less code to write - Typer handles CLI boilerplate, Rich handles output formatting
- Better UX - Colors, progress bars, structured output built-in
- Just as simple to execute - PEP 723 makes it a single-file executable; uv handles dependencies automatically
Trade-off: Requires network access on first run (to fetch packages)

stdlib-only scripts:

Benefits:
- Maximum portability - Runs on ANY Python installation without network access
- Best for: Air-gapped systems, restricted corporate environments, embedded systems
Trade-offs:
- More development complexity - Build everything from scratch (manual argparse, manual tree formatting, manual color codes)
- More code to write and test - Everything must be implemented manually
- Basic UX - Limited formatting without external libraries

Default recommendation: Use Typer + Rich with PEP 723 unless you have specific portability requirements that prevent network access.

See:

Python Development Orchestration Guide for detailed agent selection criteria
Typer and Rich CLI Examples for Rich width handling solutions

Rich Panel and Table Width Handling

Common Problem: Rich containers (Panel, Table) wrap content at 80 characters in CI/non-TTY environments, breaking URLs, commands, and structured output.

Two Solutions Depending on Context:

Solution 1: Plain Text (No Containers)

For plain text output that shouldn't wrap:

from rich.console import Console

console = Console()

# URLs, paths, commands - never wrap
console.print(long_url, crop=False, overflow="ignore")

Solution 2: Rich Containers (Panel/Table)

For Panel and Table that contain long content, crop=False alone doesn't work because containers calculate their own internal layout. Use get_rendered_width() helper with different patterns for Panel vs Table:

from rich.console import Console, RenderableType
from rich.measure import Measurement
from rich.panel import Panel
from rich.table import Table

def get_rendered_width(renderable: RenderableType) -> int:
    """Get actual rendered width of Rich renderable.

    Handles color codes, Unicode, styling, padding, borders.
    Works with Panel, Table, or any Rich container.
    """
    temp_console = Console(width=99999)
    measurement = Measurement.get(temp_console, temp_console.options, renderable)
    return int(measurement.maximum)

console = Console()

# Panel: Set Console width (Panel fills Console width)
panel = Panel(long_content)
panel_width = get_rendered_width(panel)
console.width = panel_width  # Set Console width, NOT panel.width
console.print(panel, crop=False, overflow="ignore", no_wrap=True, soft_wrap=True)

# Table: Set Table width (Table controls its own width)
table = Table()
table.add_column("Type", style="cyan", no_wrap=True)
table.add_column("Value", style="green", no_wrap=True)
table.add_row("Data", long_content)
table.width = get_rendered_width(table)  # Set Table width
console.print(table, crop=False, overflow="ignore", no_wrap=True, soft_wrap=True)

Executable Examples: See ./assets/typer_examples/ for complete working scripts:

console_no_wrap_example.py - Plain text wrapping solutions
console_containers_no_wrap.py - Panel/Table width handling with get_rendered_width()

Rich Emoji Usage

Instruction: In Rich console output, always use Rich emoji tokens instead of literal Unicode emojis.

Use Rich emoji tokens: :white_check_mark: :cross_mark: :magnifying_glass:

Why: Cross-platform compatibility, consistent rendering, markdown-safe alignment

Example:

from rich.console import Console

console = Console()

# Correct - Rich emoji tokens
console.print(":white_check_mark: Task completed")
console.print(":cross_mark: Task failed")
console.print(":sparkles: New feature")
console.print(":rocket: Performance improvement")

Benefits:

Rich emoji tokens work consistently across all terminals and fonts
Avoid markdown table alignment issues (emoji width calculation problems)
Enable proper width measurement in get_rendered_width()
Ensure cross-platform compatibility (Windows, Linux, macOS)

See: Rich Emoji Documentation for complete emoji token reference.

Python Exception Handling

Catch exceptions only when you have a specific recovery action. Let all other errors propagate to the caller.

Reason: Fail-fast principle surfaces issues early rather than hiding them behind generic error messages.

Pattern:

def get_user(id):
    return db.query(User, id)  # Errors surface naturally

def get_user_with_handling(id):
    try:
        return db.query(User, id)
    except ConnectionError:
        logger.warning("DB unavailable, using cache")
        return cache.get(f"user:{id}")  # Specific recovery action

When adding try/except, answer: "What specific error do I expect, and what is my recovery action?"

See: Exception Handling in Python CLI Applications for comprehensive patterns including Typer exception chain prevention.

Type Safety with Mypy

REQUIREMENT: All Python code MUST be comprehensively typed using Python 3.11+ native type hints.

Python Version: Python 3.11+ is required unless explicitly documented otherwise. Older versions are rare, documented exceptions.

The model MUST read the official mypy documentation examples before implementing type patterns.

For comprehensive type safety guidance including Generics, Protocols, TypedDict, Type Narrowing, attrs/dataclasses/pydantic comparison, and mypy configuration, see Type Safety with Mypy Reference.

Agent Orchestration (Orchestrator Only)

STOP - MANDATORY PRE-DELEGATION PROTOCOL

Before using the Task tool for ANY Python development delegation, the orchestrator MUST complete this checklist. This is NOT optional. Skipping this protocol leads to agent context exhaustion, architectural mistakes, and failed delegations.

Pre-Delegation Checklist (Complete ALL before Task tool)

| Step | Action | Verification | | ---- | ----------------------------- | --------------------------------------------------------------------------------------------------------- | | 1 | Read orchestration guide | Read("${CLAUDE_PLUGIN_ROOT}/skills/python3-development/references/python-development-orchestration.md") | | 2 | Identify workflow pattern | Which pattern applies? (TDD / Feature Addition / Refactoring / Debugging / Code Review) | | 3 | Plan agent chain | List ALL agents needed in sequence (never single-agent for complex tasks) | | 4 | Define scope boundaries | What is IN scope? What is OUT of scope for each agent? | | 5 | Set success criteria | What specific, measurable outcomes define completion? |

BLOCKING RULE: If you cannot answer steps 2-5 from memory, you have NOT read the orchestration guide. Go back to step 1.

Important distinction: Step 1 reads the orchestration meta-guide to understand HOW to coordinate agents. This is the only file the orchestrator reads before delegating. Do NOT read task codebase files, source code, or configuration before delegating — pass file paths to agents. Agents discover and verify file contents themselves as part of their Chain of Verification.

Why This Protocol Exists

Problem observed: Orchestrators skip reading the guide, send one massive task to a single agent, the agent runs out of context, makes architectural mistakes, and user must intervene.

Solution: Reading the guide FIRST reveals:

Complex tasks require multiple agents in sequence (design → test → implement → review)
Each agent should receive focused, bounded scope
Quality gates between agents catch issues early

Enforcement

The orchestrator must state which workflow pattern they are using before the first Task tool invocation:

"I have read the orchestration guide. Using [WORKFLOW PATTERN] with agents: [AGENT1] → [AGENT2] → [AGENT3]"

If you find yourself about to delegate without this statement, STOP and read the guide first.

DO NOT Pre-Gather Data for Agents

Anti-pattern: Reading files, running commands, or exploring the codebase to "understand the problem" before delegating, then summarizing findings for the agent.

Why this is wrong:

Agents perform Chain of Verification (CoVe): Agents follow the Scientific Execution Protocol. They read files, form hypotheses, and verify their understanding against actual source code. Your pre-gathered summary bypasses their verification process.
Shallow read vs. focused discovery: You read without knowing what the agent needs. The agent reads with focused intent—they know what they're building and what details matter.
Stale/incomplete data treated as fact: Your summary may be incomplete or misunderstood. The agent may incorrectly treat your pre-gathered observations as verified facts rather than re-verifying.
Context waste: Pre-gathering uses YOUR context, then the agent re-reads anyway. Double the context cost, half the accuracy.

The agent has read access to everything. Let them discover and verify.

What TO provide:

Outcomes: What must be true when done
Constraints: User requirements, compatibility needs
Known issues: Error messages the user reported (pass-through, not pre-gathered)
File paths: Where to start looking (not what you found there)

Delegation Pattern

The orchestrator delegates Python development tasks to specialized agents rather than implementing directly.

Reason: Agents have focused expertise and appropriate tool access for their specific domains.

The orchestrator follows this pattern:

READ the orchestration guide (mandatory - no exceptions)
Choose appropriate agents based on task requirements
Provide clear context: file paths, success criteria, scope boundaries
Chain agents for complex workflows (design → implement → test → review)
Validate outputs with quality gates

The orchestrator delegates rather than implements:

Python code — Task is Implement with subagent_type="python3-development:python-cli-architect"
Test creation — Task is Write Tests with subagent_type="python3-development:python-pytest-architect"
Code review — Task is Review with subagent_type="python3-development:python-code-reviewer"
Stdlib-only script — activate /python3-development:stdlib-scripting (skill, not a Task subagent_type)
Architecture — Task is Architecture Design with subagent_type="python3-development:python-cli-design-spec"
Task breakdown — Task is Plan with subagent_type="python3-development:swarm-task-planner"
Requirements — Task is Requirements with subagent_type="spec-analyst"

Reason: Clear mapping prevents orchestrator from implementing when it should coordinate.

Required Reading (MANDATORY)

The orchestrator MUST read this guide BEFORE any delegation. Not reading it is the #1 cause of failed Python development tasks.

Python Development Orchestration Guide

This guide contains:

Agent selection criteria and decision trees
Workflow patterns (TDD, feature addition, code review, refactoring, debugging)
Quality gates and validation requirements
Delegation best practices
Multi-agent chaining patterns (critical for complex tasks)

Quick Reference Example

User: "Build a CLI tool to process CSV files"

Orchestrator workflow:
0. Read("${CLAUDE_PLUGIN_ROOT}/skills/python3-development/references/python-development-orchestration.md")
   "I have read the orchestration guide. Using FEATURE ADDITION workflow with agents:
    @python3-development:python-cli-architect → @python3-development:python-pytest-architect → @python3-development:python-code-reviewer"

1. Task is Implement with subagent_type="python3-development:python-cli-architect"
   Context to include in the prompt: Create a CSV processing CLI with Typer+Rich progress bars.
     Success — CLI accepts CSV file input, displays progress bar, outputs results.
     Follow existing project structure and conventions.

2. Task is Write Tests with subagent_type="python3-development:python-pytest-architect"
   Context to include in the prompt: CSV processor implementation file paths.
     Success — Tests cover all CLI commands, edge cases, and error paths. Coverage >80%.

3. Apply standards — run /python3-development:shebangpython and /python3-development:modernpython

4. Task is Review with subagent_type="python3-development:python-code-reviewer"
   Context to include in the prompt: CSV processor implementation and test file paths (FOCUSED SCOPE — review only)
   Output: Review findings

5. Validate: Code quality checks per holistic-linting skill, then uv run pytest

Critical: subagent_type must be "python3-development:python-cli-architect" — NOT "general-purpose". Using general-purpose with pasted skill content bypasses the agent's specialized tooling and model configuration.

Notice: Each delegation has FOCUSED SCOPE. The orchestrator read the guide FIRST and stated the workflow pattern BEFORE any Task tool usage.

</section>

Command Usage

/python3-development:modernpython

Purpose: Comprehensive reference guide for Python 3.11+ patterns with official PEP citations

When to use:

As reference guide when writing new code
Learning modern Python 3.11-3.14 features and patterns
Understanding official PEPs (585, 604, 695, etc.)
Identifying legacy patterns to avoid
Finding modern alternatives for old code

Note: This is a reference document to READ, not an automated validation tool. Use it to guide your implementation choices.

Usage:

/python3-development:modernpython
→ Loads comprehensive reference guide
→ Provides Python 3.11+ pattern examples
→ Includes PEP citations with research tool guidance (prefer MCP tools: Ref/exa over WebFetch)
→ Shows legacy patterns to avoid
→ Shows modern alternatives to use
→ Framework-specific guides (Typer, Rich, pytest)

Research tool preference for PEP documentation:
1. mcp__Ref__ref_search_documentation(query="PEP {number} Python enhancement proposal")
2. mcp__exa__get_code_context_exa(query="PEP {number} implementation examples")
3. WebFetch as fallback

> [Web resource access, definitive guide for getting accurate data for high quality results](./references/accessing_online_resources.md)

With file path argument:

/python3-development:modernpython src/mymodule.py
→ Loads guide for reference while working on specified file
→ Use guide to manually identify and refactor legacy patterns

/python3-development:shebangpython

Purpose: Validate correct shebang for ALL Python scripts based on their dependencies and execution context

When to use:

Creating any standalone executable Python script
Validating script shebang correctness
Ensuring scripts have proper execution configuration

Required for: ALL executable Python scripts (validates shebang matches script type)

What it validates:

Stdlib-only scripts: #!/usr/bin/env python3 (no PEP 723 needed - nothing to declare)
Scripts with dependencies: #!/usr/bin/env -S uv --quiet run --active --script + PEP 723 metadata declaring those dependencies
Package executables: #!/usr/bin/env python3 (dependencies via package manager)
Library modules: No shebang (not directly executable)

See: PEP 723 Reference for details on inline script metadata

Pattern:

/python3-development:shebangpython scripts/deploy.py
→ Analyzes imports to determine dependency type
→ **Corrects shebang** to match script type (edits file if wrong)
→ **Adds PEP 723 metadata** if external dependencies detected (edits file)
→ **Removes PEP 723 metadata** if stdlib-only (edits file)
→ Sets execute bit if needed
→ Provides detailed verification report

Core Workflows (Orchestrator Only)

The orchestrator follows established workflow patterns for Python development tasks. See Python Development Orchestration Guide for complete details.

Task Planning Protocol (MANDATORY)

Every task MUST begin with discovery and planning before implementation.

Why: Skipping discovery leads to rework, missed patterns, and integration failures. The planning phase identifies context, patterns, and dependencies that inform implementation.

Phase 1: Discovery (before any implementation)

Context Gathering: Identify existing code patterns, project structure, and conventions
Dependency Analysis: Determine what files/modules are affected
Tool Detection: Run Linting Discovery Protocol to identify project tools
MCP Resource Check: Identify available MCP tools for documentation and research

Phase 2: Planning (before writing code)

Task Breakdown: Use swarm-task-planner agent for complex tasks
Agent Selection: Match task requirements to specialized agents
Quality Gates: Define acceptance criteria and verification steps
Validation Strategy: Identify tests and checks needed

Phase 3: Implementation

Execute plan using selected agents
Validate incrementally against acceptance criteria
Run quality gates (linting, type checking, tests)

Phase 4: Verification

Run all tests: uv run pytest
Check coverage: Minimum 80% for standard code, 95%+ for critical paths
Lint validation: Run detected git hook tool
Final review: Use python-code-reviewer agent

Workflow Overview

TDD (Test-Driven Development): Design → Write Tests → Implement → Review → Validate
Feature Addition: Requirements → Architecture → Plan → Implement → Test → Review
Code Review: Self-Review → Standards Check → Agent Review → Fix → Re-validate
Refactoring: Tests First → Refactor → Validate → Review
Debugging: Reproduce → Trace → Fix → Test → Review

Each workflow uses agent chaining with specific quality gates. See the orchestration guide for complete patterns, examples, and best practices.

</section>

Linting Discovery Protocol

The model executes this discovery sequence before any linting or formatting operations:

Reason: Projects use different tool configurations. Discovery ensures compatibility with project standards and CI pipelines.

Discovery Sequence

Check for git hook tool configuration:
```
# Verify .pre-commit-config.yaml exists
test -f .pre-commit-config.yaml && echo "git hook config detected"
```
If found: Detect and run the correct git hook tool:
```
# Detect tool (outputs 'prek' or 'pre-commit')
uv run python -c "print(open('.git/hooks/pre-commit').readlines()[1].split()[4].rstrip(':') if __import__('pathlib').Path('.git/hooks/pre-commit').exists() else 'prek')"

# Or use the holistic-linting detection script if available
uv run holistic-linting/scripts/detect_hook_tool.py run --files <files>
```
Detection logic: reads .git/hooks/pre-commit line 2, token 5 identifies the tool. Defaults to prek.

Note: prek is a Rust-based drop-in replacement for pre-commit. Both tools use the same .pre-commit-config.yaml and have identical CLI interfaces.

Use detected tool with: uv run <detected-tool> run --files <files> for ALL quality checks
- This runs the complete toolchain configured in the project
- Includes formatting, linting, type checking, and custom validators
- Matches exactly what runs in CI and blocks merges
Else check CI pipeline configuration:
```
# Check for GitLab CI or GitHub Actions
test -f .gitlab-ci.yml || find .github/workflows -name "*.yml" 2>/dev/null
```
If found: Read the CI config to identify required linting tools and their exact commands
- Look for ruff, mypy, basedpyright, pyright, bandit invocations
- Note the exact commands and flags used
- Execute those specific commands to ensure CI compatibility
Else fallback to tool detection:
- Check pyproject.toml [project.optional-dependencies] or [dependency-groups] for dev tools
- Use discovered tools with standard configurations

Format-First Workflow

The model always formats before linting.

Reason: Formatting operations (like ruff format) automatically fix many linting issues (whitespace, line length, quote styles). Running linting before formatting wastes context and creates false positives.

Mandatory sequence:

Format: uv run ruff format <files> or via git hook tool (pre-commit/prek)
Lint: uv run ruff check <files> or via git hook tool
Type check: Use project-configured type checker
Test: uv run pytest

When using git hook tool (pre-commit or prek):

# Detect which tool is installed, then run it
# Tool runs hooks in configured order (formatting first)
uv run <detected-tool> run --files <files>

The .pre-commit-config.yaml already specifies correct ordering - trust it.

Type Checker Discovery

The model detects which type checker the project uses:

Reason: Projects standardize on different type checkers (basedpyright, pyright, mypy). Using the wrong one produces incompatible results.

Detection priority:

Check .pre-commit-config.yaml for basedpyright, pyright, or mypy hooks
Check pyproject.toml for [tool.basedpyright], [tool.pyright], or [tool.mypy] sections
Check .gitlab-ci.yml or GitHub Actions for type checker invocations

Common patterns:

basedpyright: GitLab projects (native GitLab reporting format)
pyright: General TypeScript-style projects
mypy: Python-first type checking

Example detection:

# Check .pre-commit-config.yaml (works with both pre-commit and prek)
grep -E "basedpyright|pyright|mypy" .pre-commit-config.yaml

# Check pyproject.toml
grep -E "^\[tool\.(basedpyright|pyright|mypy)\]" pyproject.toml

Always detect from project configuration rather than assuming.

Quality Gates

The model follows the Linting Discovery Protocol before executing quality gates.

Every Python task must pass:

Format-first: uv run ruff format <files> (or via git hook tool)
Linting: uv run ruff check <files> (clean, after formatting)
Type checking: Use detected type checker (basedpyright, pyright, or mypy)
Tests: uv run pytest (>80% coverage)
Modern patterns: /python3-development:modernpython (no legacy typing)
Script compliance: /python3-development:shebangpython (for standalone scripts)

Preferred execution method:

# If .pre-commit-config.yaml exists (runs all checks in correct order):
# First detect which tool is installed (pre-commit or prek), then:
uv run <detected-tool> run --files <changed_files>

# Else use individual tools in this exact sequence:
uv run ruff format <files>           # 1. Format first
uv run ruff check <files>            # 2. Lint after formatting
uv run <detected-type-checker> <files>  # 3. Type check (basedpyright/pyright/mypy)
uv run pytest                         # 4. Test

For critical code (payments, auth, security):

Coverage >95%
Mutation testing: uv run mutmut run (>90% score)
Security scan: uv run bandit -r packages/

CI Compatibility Verification:

After local quality gates pass, verify CI will accept the changes:

If .gitlab-ci.yml exists: Check for additional validators not in pre-commit
If .github/workflows/*.yml exists: Check for additional quality gates
Ensure all CI-required checks are executed locally before claiming task completion

Linting Exception Conditions

The model MUST NOT ignore or bypass linting errors UNLESS the code falls into one of these categories:

Acceptable Exceptions (OK to ignore linting):

Vendored code - Third-party code copied into the repository without modification
Examples of what-not-to-do - Intentionally incorrect code for educational purposes
Code pinned to historic Python version - Code requiring Python < 3.11 compatibility
Code for Python derivatives - CircuitPython, MicroPython, or similar implementations

Unacceptable Exceptions (MUST fix or escalate):

If NONE of the above conditions apply, the model MUST:

Fix the linting error at root cause
If unable to fix, document the specific blocker
Never add # type: ignore, # noqa, or similar suppressions without explicit user approval

Rule Codes That MUST Always Be Fixed (never suppress):

These rule codes indicate real code quality issues that must be resolved at root cause:

BLE001 (blind-except): Replace generic except Exception with specific exception types
D103 (missing-docstring-in-public-function): Add docstrings to public functions
TRY300 (try-consider-else): Restructure try/except/else blocks properly

Per-File Exceptions in pyproject.toml (acceptable):

The following rules may be configured as per-file ignores in pyproject.toml [tool.ruff.lint.per-file-ignores]:

**/scripts/**: T201 (print), S (security), DOC, ANN401, PLR0911, PLR0917, PLC0415
**/tests/**: S, D, E501, ANN, DOC, PLC, SLF, PLR, EXE, N, T
**/assets/**: PLC0415, DOC
typings/**: N, ANN, A

These configurations allow relaxed checking in appropriate contexts without inline suppressions.

Touched Files Must Be Clean:

When files are modified, moved, or renamed, all linting issues in those files MUST be resolved before committing. Touching a file means taking responsibility for its quality.

Standard Project Structure

All Python projects use this directory layout:

Reason: Consistent structure enables reliable automation and clear separation between user code and dependencies.

project-root/
├── pyproject.toml
├── packages/
│   └── package_name/      # Package code (hyphens in project name → underscores)
│       ├── __init__.py
│       └── ...
├── tests/
├── scripts/
├── sessions/              # Optional: cc-sessions framework
└── README.md

Package Directory Naming:

Project name: my-cli-tool → Package directory: packages/my_cli_tool/
Hyphens in project names become underscores in package directories
The packages/ directory distinguishes user code from external dependencies

Hatchling Configuration:

[tool.hatchling.build.targets.wheel]
packages = ["packages/package_name"]

This structure is consistent across all projects and enables clear separation of concerns.

Integration

Reference Example (Bundled)

Complete working example (bundled): python-cli-demo.py

This reference implementation demonstrates all recommended patterns:

PEP 723 metadata with correct shebang
Typer + Rich integration
Modern Python 3.11+ (StrEnum, Protocol, TypeVar, Generics)
Annotated syntax for CLI params
Async processing
Comprehensive docstrings

This file is bundled with this plugin to keep the workflow self-contained. Use it as reference when creating CLI tools.

Using Asset Templates

When creating new Python projects, copy standard configuration files from the skill's assets directory to ensure consistency with established conventions:

Reason: Templates implement proven patterns and save setup time.

Asset Directory Location (in plugin): ./assets/

Available Templates:

version.py - Dual-mode version management (hatch-vcs + importlib.metadata fallback)

cp "${CLAUDE_PLUGIN_ROOT}/skills/python3-development/assets/version.py" "packages/{package_name}/version.py"

hatch_build.py - Build hook for binary/asset handling (only if needed)

mkdir -p scripts/
cp "${CLAUDE_PLUGIN_ROOT}/skills/python3-development/assets/hatch_build.py" "scripts/hatch_build.py"

.markdownlint.json - Markdown linting configuration

cp "${CLAUDE_PLUGIN_ROOT}/skills/python3-development/assets/.markdownlint.json" "."

.pre-commit-config.yaml - Standard git hooks configuration

cp "${CLAUDE_PLUGIN_ROOT}/skills/python3-development/assets/example.pre-commit-config.yaml" ".pre-commit-config.yaml"

# Install hooks using pre-commit or prek (whichever is available)
# Both tools use the same configuration file and have identical interfaces
uv run pre-commit install  # or: uv run prek install

.editorconfig - Editor formatting settings

cp "${CLAUDE_PLUGIN_ROOT}/skills/python3-development/assets/.editorconfig" "."

These templates implement the patterns documented in User Project Conventions and ensure all projects follow the same standards for version management, linting, formatting, and build configuration.

Common Patterns to Follow (Orchestrator Only)

Delegation Pattern:

Delegating without reading guide — ALWAYS read orchestration guide first, state workflow pattern
Sending one massive task to agent — break into focused delegations with bounded scope
Writing Python code directly — Task is Implement with subagent_type="python3-development:python-cli-architect"
Skipping validation steps — complete workflow: implement → test → review → validate
Pre-deciding technical implementations — let agents determine HOW based on requirements
Implementing and testing in same step — chain: subagent_type="python3-development:python-cli-architect" then subagent_type="python3-development:python-pytest-architect"

Reason: Orchestrators coordinate workflows. Agents have specialized expertise and focused tool access for implementation.

For detailed pattern examples and corrections, see Anti-Patterns section in the orchestration guide.

</section>

Detailed Documentation

Reference Documentation

Core Orchestration Guide: Python Development Orchestration - Detailed workflow patterns for TDD, feature addition, refactoring, and code review with comprehensive agent coordination strategies

PEP 723 Specification: PEP 723 - Inline Script Metadata - User-friendly guide to PEP 723 inline script metadata with examples and migration patterns

Exception Handling: Exception Handling in Python CLI Applications with Typer - Critical guidance on preventing exception chain explosion in Typer applications with correct patterns for graceful error handling

Typer and Rich Examples: Typer and Rich CLI Examples - Executable examples demonstrating solutions to common problems with Rich Console text wrapping in CI/non-TTY environments and Panel/Table content wrapping

Module Reference: Modern Python Modules - Comprehensive guide to 50+ modern Python libraries with deep-dive documentation for each module including usage patterns and best practices

Tool Registry: Tool & Library Registry - Catalog of development tools, their purposes, and usage patterns for linting, testing, and build automation

API Documentation: API Reference - API specifications, integration guides, and programmatic interface documentation

Navigating Large References

To find specific modules in modern-modules.md:

grep -i "^### " references/modern-modules.md

To search for tools by category in tool-library-registry.md:

grep -A 5 "^## " references/tool-library-registry.md

To locate workflow patterns in python-development-orchestration.md:

grep -i "^## " references/python-development-orchestration.md

External Commands

These skills are bundled with this plugin and available as slash commands:

/python3-development:modernpython - Python 3.11+ patterns and PEP references
/python3-development:shebangpython - PEP 723 validation and shebang standards

Summary

Python Development Skill for All Roles

For All Roles (Orchestrators and Agents):

Modern Python 3.11+ standards and patterns
Quality gates: ruff, pyright, mypy, pytest (>80% coverage)
Command standards: /python3-development:modernpython, /python3-development:shebangpython
Reference documentation for 50+ modern Python modules
Tool and library registry

For Orchestrators Only:

Read the orchestration guide before delegating
Choose the right agent based on task requirements
Provide clear context: file paths, success criteria, scope boundaries
Chain agents for complex workflows (design → test → implement → review)
Instruct agents to validate with quality gates and commands
Enable uv skill for package management

Orchestration = Coordination + Delegation + Validation

</section>

Agent Skills: Opinionated Python Development Skill

Install this agent skill to your local

Skill Files