gh-jamie-bitflight-claude-skills-development-skills/skills/python3-development/references/python-development-orchestration.md

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title	description	version	last_updated	document_type	python_compatibility	related_docs
Python Development Orchestration Guide	Guide for orchestrating Python development tasks using specialized agents and commands	1.0.0	2025-11-02	guide	3.11+	../SKILL.md ./modern-modules.md ./tool-library-registry.md

Python Development Orchestration Guide

Comprehensive guide for orchestrating Python development tasks using specialized agents and commands. This guide provides detailed workflows and patterns for coordinating multiple agents to accomplish complex Python development goals.

Quick Reference: For a concise overview and quick-start examples, see SKILL.md.

Available Agents and Commands

Agents (in ~/.claude/agents/)

• python-cli-architect: Build modern CLI applications with Typer and Rich
• python-portable-script: Create stdlib-only portable scripts
• python-pytest-architect: Design comprehensive test suites
• python-code-reviewer: Review Python code for quality and standards
• spec-architect: Design system architecture
• spec-planner: Break down tasks into implementation plans

Commands (in this skill: references/commands/)

• /modernpython: Apply Python 3.11+ best practices and modern patterns
• /shebangpython: Validate PEP 723 shebang compliance

External Skills

• uv: Package management with uv (always use for Python dependency management)

Core Workflow Patterns

1. TDD Workflow (Test-Driven Development)

When to use: Building new features, fixing bugs with test coverage

Pattern:

1. Design → @agent-spec-architect
   Input: Feature requirements
   Output: Architecture design, component interfaces

2. Write Tests → @agent-python-pytest-architect
   Input: Architecture design, expected behavior
   Output: Complete test suite (fails initially)

3. Implement → @agent-python-cli-architect OR @agent-python-portable-script
   Input: Tests, architecture design
   Output: Implementation that makes tests pass

4. Review → @agent-python-code-reviewer
   Input: Implementation + tests
   Output: Review feedback, improvement suggestions

5. Validate (follow Linting Discovery Protocol)
   - If .pre-commit-config.yaml exists: `uv run pre-commit run --files <files>`
   - Else: Format → Lint → Type check → Test in sequence
   - Apply: /modernpython to check modern patterns
   - Verify: CI compatibility by checking .gitlab-ci.yml or .github/workflows/

Example:

User: "Build a CLI tool to process CSV files with progress bars"

Step 1: @agent-spec-architect
  "Design architecture for CSV processing CLI with progress tracking"
  → Architecture design with components

Step 2: @agent-python-pytest-architect
  "Create test suite for CSV processor based on this architecture"
  → Test files in tests/

Step 3: @agent-python-cli-architect
  "Implement CSV processor CLI with Typer+Rich based on these tests"
  → Implementation in packages/

Step 4: @agent-python-code-reviewer
  "Review this implementation against the architecture and test requirements"
  → Review findings, suggested improvements

Step 5: Validate
  → All tests pass, coverage >80%, linting clean

2. Feature Addition Workflow

When to use: Adding new functionality to existing codebase

Pattern:

1. Requirements → User or @agent-spec-analyst
   Output: Clear requirements, acceptance criteria

2. Architecture → @agent-spec-architect
   Input: Requirements, existing codebase structure
   Output: Design that integrates with existing code

3. Implementation Plan → @agent-spec-planner
   Input: Architecture design
   Output: Step-by-step implementation tasks

4. Implement → @agent-python-cli-architect OR @agent-python-portable-script
   Input: Implementation plan, existing code patterns
   Output: New feature implementation

5. Testing → @agent-python-pytest-architect
   Input: Implementation, edge cases
   Output: Tests for new feature + integration tests

6. Review → @agent-python-code-reviewer
   Input: All changes (implementation + tests)
   Output: Quality assessment, improvements

7. Validate
   - Check: No regressions in existing tests
   - Verify: New feature has >80% coverage
   - Apply: /modernpython for consistency

3. Code Review Workflow

When to use: Before merging changes, during PR review

Pattern:

1. Self-Review → Apply /modernpython
   Check: Modern Python patterns used
   Check: No legacy typing imports

2. Standards Validation → Apply /shebangpython (if scripts)
   Check: PEP 723 compliance
   Check: Correct shebang format

3. Agent Review → @agent-python-code-reviewer
   Input: All changed files
   Output: Comprehensive review findings

4. Fix Issues → Appropriate agent
   Input: Review findings
   Output: Corrections

5. Re-validate
   - Run: uv run pre-commit run --all-files
   - Run: uv run pytest
   - Verify: All review issues addressed

4. Refactoring Workflow

When to use: Improving code structure without changing behavior

Pattern:

1. Tests First → Verify existing test coverage
   Check: Tests exist for code being refactored
   Check: Tests pass before refactoring
   If missing: @agent-python-pytest-architect creates tests

2. Refactor → @agent-python-cli-architect or @agent-python-portable-script
   Input: Code to refactor + test suite
   Constraint: Must not break existing tests
   Output: Refactored code

3. Validate → Tests still pass
   Run: uv run pytest
   Verify: Coverage maintained or improved

4. Review → @agent-python-code-reviewer
   Input: Before/after comparison
   Output: Verification refactoring improved quality

5. Apply Standards (follow Linting Discovery Protocol)
   - Apply: /modernpython for modern patterns
   - If .pre-commit-config.yaml exists: `uv run pre-commit run --files <files>`
   - Else: Format → Lint (with --fix) → Type check in sequence

5. Debugging Workflow

When to use: Investigating and fixing bugs

Pattern:

1. Reproduce → Write failing test
   @agent-python-pytest-architect
   Input: Bug description, steps to reproduce
   Output: Test that demonstrates bug

2. Trace → Investigate root cause
   Use: Debugging tools, logging
   Identify: Specific code causing issue

3. Fix → Appropriate agent
   @agent-python-cli-architect or @agent-python-portable-script
   Input: Failing test + root cause
   Output: Fix that makes test pass

4. Test → Verify fix + no regressions
   Run: Full test suite
   Verify: Bug test now passes
   Verify: No other tests broke

5. Review → @agent-python-code-reviewer
   Input: Fix + test
   Output: Verification fix is proper solution

6. Validate
   - Apply: /modernpython
   - Run: uv run pre-commit run --files <changed>

Agent Selection Guide

When to Use python-cli-architect

Use when:

• DEFAULT choice for scripts and CLI tools
• Building command-line applications with rich user interaction
• Need progress bars, tables, colored output
• User-facing CLI tools and automation scripts
• Any script where UX matters (formatted output, progress feedback)
• PEP 723 + uv available (internet access present)

Characteristics:

• Uses Typer for CLI framework
• Uses Rich for terminal output
• Focuses on UX and polish
• PEP 723 makes dependencies transparent (single file)
• Better UX than stdlib alternatives
• Works anywhere with Python 3.11+ and internet access

Complexity Advantage (IMPORTANT):

• ✅ LESS development complexity - Libraries handle the hard work (argument parsing, output formatting, validation)
• ✅ LESS code to write - Typer CLI boilerplate and Rich formatting come built-in
• ✅ Better UX - Professional output with minimal effort
• ✅ Just as portable - PEP 723 + uv makes single-file scripts with dependencies work seamlessly

This agent is EASIER to use than stdlib-only approaches. Choose this as the default unless portability restrictions exist.

Rich Width Handling: For Rich Panel/Table width issues in CI/non-TTY environments, see Typer and Rich CLI Examples for complete solutions including the get_rendered_width() helper pattern.

Example tasks:

• "Build a CLI tool to manage database backups with progress bars"
• "Create an interactive file browser with color-coded output"
• "Create a script to scan git repositories and show status tree"
• "Build a deployment verification tool with progress bars"

When to Use python-portable-script

Use when (RARE - ask user first if unclear):

• Restricted environment: No internet access (airgapped, embedded systems)
• No uv available: Locked-down systems where uv cannot be installed
• Hard stdlib-only requirement: Explicitly requested by user
• 1% case: Only when deployment environment truly restricts dependencies

Characteristics:

• Stdlib only (argparse, pathlib, subprocess)
• Defensive error handling
• Cross-platform compatibility
• Stdlib only (no PEP 723 needed - nothing to declare)
• Use PEP 723 ONLY if adding external dependencies later
• Ask deployment environment questions before choosing this agent
• This is the EXCEPTION, not the rule
• Consider python-cli-architect first unless restrictions confirmed

Complexity Trade-off (IMPORTANT):

• ❌ MORE development complexity - Manual implementation of everything (argument parsing, output formatting, validation, error handling)
• ❌ MORE code to write - Build from scratch what libraries provide tested
• ❌ Basic UX - Limited formatting capabilities
• ✅ Maximum portability - The ONLY reason to choose this: runs anywhere Python exists without network access

This agent is NOT simpler to use - it requires MORE work to build the same functionality. Choose it ONLY for portability, not for simplicity.

Note: Only use this agent if deployment environment restrictions are confirmed. With PEP 723 + uv, python-cli-architect is preferred for better UX. ASK: "Will this run without internet access or where uv cannot be installed?" See PEP 723 Reference for details on when to use inline script metadata.

Example tasks:

• "Create a deployment script using only stdlib"
• "Build a config file validator that runs without dependencies"

Agent Selection Decision Process

For Scripts and CLI Tools

Step 1: Default to python-cli-architect

• Provides better UX (Rich components, progress bars, tables)
• PEP 723 + uv handles dependencies (still single file)
• Works in 99% of scenarios

Step 2: Only use python-portable-script if:

• User explicitly states "stdlib only" requirement
• OR deployment environment is confirmed restricted:
  • No internet access (airgapped network, embedded system)
  • uv cannot be installed (locked-down corporate environment)
  • Security policy forbids external dependencies

Step 3: When uncertain, ASK:

1. "Where will this script be deployed?"
2. "Does the environment have internet access?"
3. "Can uv be installed in the target environment?"
4. "Is stdlib-only a hard requirement, or would you prefer better UX?"

Decision Tree:

Does the deployment environment have internet access?
├─ YES → Use python-cli-architect (default)
│         Single file + PEP 723 + uv = transparent dependencies
│
└─ NO → Is uv installable in the environment?
        ├─ YES → Use python-cli-architect (default)
        │         uv can cache dependencies for offline use
        │
        └─ NO → Use python-portable-script (exception)
                 Truly restricted environment requires stdlib-only

If answers indicate normal environment → python-cli-architect

If answers indicate restrictions → python-portable-script

When in doubt: Use python-cli-architect. PEP 723 + uv makes single-file scripts with dependencies just as portable as stdlib-only scripts for 99% of deployment scenarios.

When to Use python-pytest-architect

Use when:

• Designing test suites from scratch
• Need comprehensive test coverage strategy
• Implementing advanced testing (property-based, mutation)
• Test architecture decisions

Characteristics:

• Modern pytest patterns
• pytest-mock exclusively (never unittest.mock)
• AAA pattern (Arrange-Act-Assert)
• Coverage and mutation testing

Example tasks:

• "Design test suite for payment processing module"
• "Create property-based tests for data validation"

When to Use python-code-reviewer

Use when:

• Reviewing code for quality, patterns, standards
• Post-implementation validation
• Pre-merge code review
• Identifying improvement opportunities

Characteristics:

• Checks against modern Python standards
• Identifies anti-patterns
• Suggests improvements
• Validates against project patterns

Example tasks:

• "Review this PR for code quality"
• "Check if implementation follows best practices"

Command Usage Patterns

/modernpython

Apply to: Load as reference guide (optional file path argument for context)

Use when:

• 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.

Usage:

/modernpython
→ Loads comprehensive reference guide
→ Provides Python 3.11+ pattern examples
→ Includes PEP citations with WebFetch commands
→ Shows legacy patterns to avoid
→ Shows modern alternatives to use
→ Framework-specific guides (Typer, Rich, pytest)

With file path:

/modernpython packages/mymodule.py
→ Loads guide for reference while working on specified file
→ Use guide to manually identify and refactor legacy patterns

/shebangpython

Apply to: Individual Python scripts

Use when:

• Creating new standalone scripts
• Ensuring PEP 723 compliance
• Correcting script configuration

Pattern:

/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

Integration with uv Skill

Always use uv skill for:

• Package management: uv add <package>
• Running scripts: uv run script.py
• Running tools: uv run pytest, uv run ruff
• Creating projects: uv init

Never use:

• pip install (use uv add)
• python -m pip (use uv)
• pipenv, poetry (use uv)

Quality Gates

CRITICAL: The orchestrator MUST instruct agents to follow the Linting Discovery Protocol from the main SKILL.md before executing quality checks.

Linting Discovery Protocol (see SKILL.md for full details):

1. Check for pre-commit: If .pre-commit-config.yaml exists, use uv run pre-commit run --files <files>
2. Else check CI config: Read .gitlab-ci.yml or .github/workflows/*.yml for exact linting commands
3. Else detect tools: Check pyproject.toml for configured dev tools

Format-First Requirement: ALWAYS format before linting (formatting fixes many lint issues automatically)

Every Python development task must pass:

1. Format-first: uv run ruff format <files> (or via pre-commit)
2. Linting: uv run ruff check <files> (clean, after formatting)
3. Type checking: Use detected type checker (basedpyright, pyright, or mypy)
4. Tests: uv run pytest (>80% coverage)
5. Standards: /modernpython for modern patterns
6. Script compliance: /shebangpython for standalone scripts

Preferred execution (when .pre-commit-config.yaml exists):

# This runs ALL checks in correct order (format → lint → type → test)
uv run pre-commit run --files <changed_files>

For critical code (payments, auth, security):

• Coverage: >95%
• Mutation testing: uv run mutmut run
• Security scan: uv run bandit -r packages/

CI Compatibility: After local checks pass, verify CI requirements are met by checking CI config files for additional validators.

Reference Example

Complete working example: ~/.claude/agents/python-cli-demo.py

This file demonstrates all modern Python CLI patterns:

• PEP 723 inline script metadata with correct shebang
• Typer + Rich integration (Typer includes Rich, don't add separately)
• Modern Python 3.11+ patterns (StrEnum, Protocol, TypeVar, etc.)
• Proper type annotations with Annotated syntax
• Rich components (Console, Progress, Table, Panel)
• Async processing patterns
• Comprehensive docstrings

Use this as the reference implementation when creating CLI tools.

Examples of Complete Workflows

Example: Building a CLI Tool

User: "Build a CLI tool to validate YAML configurations"

Orchestrator:
1. @agent-spec-architect
   "Design architecture for YAML validation CLI"
   → Component design, validation rules

2. @agent-python-pytest-architect
   "Create test suite for YAML validator"
   → tests/test_validator.py with fixtures

3. @agent-python-cli-architect
   "Implement YAML validator CLI with Typer based on tests"
   Reference: ~/.claude/agents/python-cli-demo.py for patterns
   → packages/validator.py with Typer+Rich UI

4. Validation (Linting Discovery Protocol):
   /shebangpython packages/validator.py
   # If .pre-commit-config.yaml exists:
   uv run pre-commit run --files packages/validator.py tests/
   # Else:
   uv run ruff format packages/ tests/
   uv run ruff check packages/ tests/
   uv run <detected-type-checker> packages/ tests/
   uv run pytest

5. @agent-python-code-reviewer
   "Review validator implementation"
   → Quality check, improvements

6. Fix any issues and re-validate

Example: Fixing a Bug

User: "Fix bug where CSV parser fails on empty rows"

Orchestrator:
1. @agent-python-pytest-architect
   "Write test that reproduces CSV parser bug with empty rows"
   → tests/test_csv_parser.py::test_empty_rows (failing)

2. @agent-python-cli-architect
   "Fix CSV parser to handle empty rows, making test pass"
   → packages/csv_parser.py updated

3. Validation:
   uv run pytest  # Verify bug test passes
   uv run pytest  # Verify no regression

4. @agent-python-code-reviewer
   "Review bug fix and test"
   → Verify proper solution

5. Apply standards (Linting Discovery Protocol):
   /modernpython packages/csv_parser.py
   # If .pre-commit-config.yaml exists:
   uv run pre-commit run --files packages/csv_parser.py tests/
   # Else: Format → Lint (--fix) → Type check sequence

Anti-Patterns to Avoid

Don't: Write Python code as orchestrator

❌ Orchestrator writes implementation directly

Do: Delegate to appropriate agent

✅ @agent-python-cli-architect writes implementation
✅ @agent-python-code-reviewer validates it

Don't: Skip validation steps

❌ Implement → Done (no tests, no review, no linting)

Do: Follow complete workflow

✅ Implement → Test → Review → Validate → Done

Don't: Mix agent contexts

❌ Ask python-portable-script to build Typer CLI
❌ Ask python-cli-architect to avoid all dependencies

Do: Choose correct agent for context

✅ python-cli-architect for user-facing CLI tools
✅ python-portable-script for stdlib-only scripts

Summary

Orchestration = Coordination, Not Implementation

1. Choose the right agent for the task
2. Provide clear inputs and context
3. Chain agents for complex workflows (architect → test → implement → review)
4. Always validate with quality gates
5. Use commands for standards checking
6. Integrate with uv skill for package management

Success = Right agent + Clear inputs + Proper validation