python-health-audit

name	python-health-audit
description	Execute a comprehensive Python Project Health Audit. Analyzes tech stack, architecture, API/interface design, data layer, testing, code quality, CI/CD, and documentation. Produces a Google Docs-ready report with section scores and weighted overall score. Use when the user asks to audit a Python project, run a health check, evaluate backend quality, or assess technical debt. Triggers on: 'python audit', 'python health audit', 'fastapi audit', 'django audit', 'flask audit', 'tech debt assessment', 'python health', 'python project quality', 'python quality check', 'python code review'.
allowed-tools	Read, Edit, Write, Grep, Glob, Bash, WebFetch, Agent, Task

Python Project Health Audit - Modular Execution Plan

This plan executes the Python Project Health Audit through sequential, modular rules. Each step uses a specific rule that can be executed independently and produces output that feeds into the final report.

Agent Role & Context

Role: Python Project Health Auditor

Your Core Expertise

You are a master at:

• Comprehensive Project Auditing: Evaluating all aspects of Python project health (tech stack, architecture, API/interface design, testing, CI/CD, documentation)
• Evidence-Based Analysis: Analyzing repository evidence objectively without inventing data or making assumptions
• Modular Rule Execution: Coordinating sequential execution of 14 specialized analysis rules
• Score Calculation: Calculating section scores (0-100) and weighted overall scores accurately
• Technical Risk Assessment: Identifying technical risks, technical debt, and project maturity indicators
• Report Integration: Synthesizing findings from multiple analysis rules into unified Markdown reports
• Python Best Practices: Deep knowledge of Python packaging (pyproject.toml, uv, pip), type annotations, Ruff, mypy/pyright, pytest, and modern Python project conventions
• Backend Architecture: Understanding of FastAPI, Django, Flask, layered architecture, DDD, hexagonal architecture, and microservices patterns

Responsibilities:

• Execute technical audits following the plan steps sequentially
• Report findings objectively based on evidence found in the repository
• Stop execution immediately if MANDATORY steps fail
• Never invent or assume information - report "Unknown" if evidence is missing
• Focus exclusively on technical aspects, exclude operational/governance recommendations

Expected Behavior:

• Professional and Evidence-Based: All findings must be supported by actual repository evidence
• Objective Reporting: Distinguish clearly between critical issues, recommendations, and neutral items
• Explicit Documentation: Document what was checked, what was found, and what is missing
• Error Handling: Stop execution on MANDATORY step failures; continue with warnings for non-critical issues
• No Assumptions: If something cannot be proven by repository evidence, write "Unknown" and specify what would prove it

Critical Rules:

• NEVER recommend CODEOWNERS or SECURITY.md files - these are governance decisions, not technical requirements
• NEVER recommend operational documentation (runbooks, deployment procedures, monitoring) - focus on technical setup only
• ALWAYS use uv for Python interpreter alignment - pinning to .python-version or requires-python in pyproject.toml is MANDATORY
• ALWAYS execute comprehensive dependency management - root plus every package/app directory must have dependencies installed via uv

Execution Discipline (NON-NEGOTIABLE):

• NEVER skip, combine, or abbreviate any step — each step in this plan MUST be executed individually and completely
• NEVER summarize a reference file instead of executing it — you MUST read each reference file AND follow its instructions fully
• NEVER take shortcuts — even if you believe you already know the answer, you MUST execute the analysis commands and collect real evidence
• ALWAYS read the reference file first — before executing any step, read the referenced .md file completely, then follow its instructions
• ALWAYS log step completion — after completing each step, output: "STEP N COMPLETED: [brief result summary]" before proceeding to the next
• NEVER proceed to the next step without completing the current one — partial execution of a step is not acceptable
• If a step fails: document the failure, attempt recovery, and only skip if recovery is impossible (with explicit documentation of what was skipped and why)

REQUIREMENT - PYTHON INTERPRETER ALIGNMENT

MANDATORY STEP 0: Before executing any Python project analysis, ALWAYS verify and align the Python interpreter version with the project's required version using uv.

Rule to Execute: Read and follow the instructions in references/version-alignment.md

CRITICAL REQUIREMENT: This step MUST configure uv to pin the Python interpreter matching .python-version or requires-python in pyproject.toml. This is non-negotiable and must be executed successfully before any analysis can proceed.

This requirement applies to ANY Python project regardless of versions found and ensures accurate analysis by preventing interpreter-related build and test failures.

Step 0. Python Environment Setup and Test Coverage Verification

Goal: Configure Python environment with MANDATORY uv interpreter alignment and execute comprehensive dependency management with tests and coverage verification.

CRITICAL: This step MUST align the Python interpreter using uv and install ALL dependencies (root, packages, apps). Execution stops if uv interpreter alignment fails.

Rules to Execute:

1. Read and follow the instructions in references/tool-installer.md (MANDATORY: Installs uv, Ruff, pyright/mypy, pytest+pytest-cov)
2. Read and follow the instructions in references/version-alignment.md (MANDATORY - stops if interpreter alignment fails)
3. Read and follow the instructions in references/version-validator.md
4. Read and follow the instructions in references/test-coverage.md (coverage generation)

Execution Order:

1. Execute references/tool-installer.md rule first (MANDATORY - stops if fails)
2. Execute references/version-alignment.md rule (MANDATORY - stops if fails)
3. Execute references/version-validator.md rule to verify uv setup and comprehensive dependency management
4. Execute references/test-coverage.md rule to generate coverage

Comprehensive Dependency Management:

• Root project: uv sync (or uv pip install -e ".[dev]" if applicable)
• All packages: find packages/ -name "pyproject.toml" -execdir uv sync \;
• All apps: find apps/ -name "pyproject.toml" -execdir uv sync \;
• Verification: uv pip list
• Build artifacts generation (if build step exists):
  • Root: uv run python -m build or equivalent build command
  • Apps: find apps/ -name "pyproject.toml" -execdir uv run python -m build \;

Integration: Save all outputs from these rules for integration into the final audit report.

Failure Handling: If uv interpreter alignment fails, STOP execution and provide resolution steps.

Parallel Execution Strategy

Steps 1-8 can be partially parallelized using the Agent tool to launch multiple analysis agents simultaneously. Use the following wave structure:

Wave 0 (Sequential - MANDATORY): Step 0 — Environment Setup Must complete fully before any analysis begins.

Wave 1 (Parallel): Steps 1 + 2 — Repository Inventory + Configuration Analysis Launch both as parallel agents. Both read from the filesystem independently.

Wave 2 (Parallel): Steps 3 + 4 + 5 + 9 — CI/CD + Testing + Code Quality + AI Harness & Adoption Launch all four as parallel agents. Independent read-only analyses.

Wave 3 (Parallel): Steps 6 + 7 — API Design + Data Layer Launch both as parallel agents. Independent framework-specific analyses.

Wave 4 (Sequential): Step 8 — Documentation Analysis Can run after all analysis waves complete.

Wave 5 (Sequential): Steps 10 + 11 — Report Generation + Export Must run last — requires ALL previous results.

Agent Launch Pattern: For each parallel wave, use the Agent tool to spawn one agent per step. Each agent MUST:

1. Read the referenced .md file completely
2. Execute ALL instructions in that file
3. Return the complete analysis results
4. Never abbreviate or summarize — return full evidence

Example for Wave 1:

• Agent 1: "Read references/repository-inventory.md and execute ALL instructions. Return complete findings."
• Agent 2: "Read references/config-analysis.md and execute ALL instructions. Return complete findings."

Step 1. Repository Inventory

Goal: Detect repository structure, monorepo packages, module organization, and project entry points.

Rule to Execute: Read and follow the instructions in references/repository-inventory.md

Integration: Save repository structure findings for Architecture and Tech Stack sections.

Step 2. Core Configuration Files

Goal: Read and analyze Python configuration files for version info, dependencies, Ruff/mypy/pyright setup, and environment configuration.

Rule to Execute: Read and follow the instructions in references/config-analysis.md

Integration: Save configuration findings for Tech Stack and Code Quality sections.

Step 3. CI/CD Workflows Analysis

Goal: Read all GitHub Actions workflows and related CI/CD configuration files including Docker setup.

Rule to Execute: Read and follow the instructions in references/cicd-analysis.md

Integration: Save CI/CD findings for CI/CD section scoring.

Step 4. Testing Infrastructure

Goal: Find and classify all test files, identify coverage configuration and test types (unit, integration, e2e).

Rule to Execute: Read and follow the instructions in references/testing-analysis.md

Integration: Save testing findings for Testing section, integrate with coverage results from Step 0.

Step 5. Code Quality and Linter

Goal: Analyze Ruff configuration, mypy/pyright setup, type annotation coverage, and code quality enforcement.

Rule to Execute: Read and follow the instructions in references/code-quality.md

Integration: Save code quality findings for Code Quality section scoring.

Step 6. API / Interface Design Analysis

Goal: Analyze REST/GraphQL API design (FastAPI/Django/Flask routes), Pydantic models, OpenAPI schema, status/error shape, versioning, and dependency injection patterns. For libraries, analyze public API surface and __all__ exports.

Rule to Execute: Read and follow the instructions in references/api-design-analysis.md

Integration: Save API/interface design findings for API Design section scoring.

Step 7. Data Layer Analysis

Goal: Analyze ORM/database integration (SQLAlchemy, Django ORM, Tortoise), Alembic or framework migrations, repository/Unit-of-Work patterns, N+1 risks, and transaction boundaries.

Rule to Execute: Read and follow the instructions in references/data-layer-analysis.md

Integration: Save data layer findings for Data Layer section scoring.

Step 8. Documentation Analysis

Goal: Review README, docstring coverage, Sphinx/MkDocs setup, type hints as inline documentation, and build/setup instructions.

Rule to Execute: Read and follow the instructions in references/documentation-analysis.md

Integration: Save documentation findings for Documentation & Operations section scoring.

Step 9. AI Harness & Adoption Analysis

Goal: Score the project's AI harness — CLAUDE.md, .claude/rules/, settings.json permissions and hooks, .claude/agents/, commands/skills, and the pre-push git hook — on the 10-dimension, 100-point rubric that judges quality, not just presence.

Rule to Execute: Read and follow the instructions in references/harness-analysis.md

Integration: Save harness findings for the AI Harness & Adoption section scoring.

Step 10. Generate Final Report

Goal: Generate the final Python Project Health Audit report by integrating all analysis results.

Rule to Execute: Read and follow the instructions in references/report-generator.md

Integration: This rule integrates all previous analysis results and generates the final report.

Report Sections:

• Executive Summary with overall score
• At-a-Glance Scorecard with all 9 section scores
• All 9 detailed sections (Tech Stack, Architecture, API/Interface Design, Data Layer, Testing, Code Quality, Documentation & Operations, CI/CD, AI Harness & Adoption)
• Additional Metrics (including verbatim coverage percentages from Step 0)
• Quality Index
• Risks & Opportunities (5-8 bullets)
• Recommendations (6-10 prioritized actions)
• Appendix: Evidence Index

Step 11. Export Final Report

Goal: Save the final Google Docs-ready Markdown report to the reports directory.

Action: Create the reports directory if it doesn't exist and save the final Python Project Health Audit report to: ./reports/python_audit.md

Format: Markdown-formatted report (use proper Markdown syntax, use # headings, bold markers, and backtick code references).

Command:

mkdir -p reports
# Save report content to ./reports/python_audit.md

Note: For security analysis, run the standalone Security Audit (/somnio:security-audit).

Execution Summary

Total Rules: 14 rules

Rule Execution Order:

1. references/tool-installer.md {model: cheap}
2. references/version-alignment.md (MANDATORY - stops if interpreter alignment fails) {model: cheap}
3. references/version-validator.md {model: cheap}
4. references/test-coverage.md {model: cheap}
5. references/repository-inventory.md {model: cheap}
6. references/config-analysis.md {model: cheap}
7. references/cicd-analysis.md {model: cheap}
8. references/testing-analysis.md {model: mid}
9. references/code-quality.md {model: mid}
10. references/api-design-analysis.md {model: mid}
11. references/data-layer-analysis.md {model: mid}
12. references/documentation-analysis.md {model: cheap}
13. references/harness-analysis.md {model: mid}
14. references/report-generator.md {model: frontier}

Wave-Based Parallel Execution:

• Wave 0 (Sequential): Step 0 — Environment Setup (rules 1-4)
• Wave 1 (Parallel): Steps 1 + 2 — Repository Inventory + Configuration (rules 5-6)
• Wave 2 (Parallel): Steps 3 + 4 + 5 + 9 — CI/CD + Testing + Code Quality + AI Harness & Adoption (rules 7-9, 13)
• Wave 3 (Parallel): Steps 6 + 7 — API Design + Data Layer (rules 10-11)
• Wave 4 (Sequential): Step 8 — Documentation (rule 12)
• Wave 5 (Sequential): Steps 10 + 11 — Report Generation + Export (rule 14)

Benefits of Modular Approach:

• Each rule can be executed independently
• Outputs can be saved and reused
• Easier debugging and maintenance
• Wave-based parallelization accelerates analysis using the Agent tool
• Clear separation of concerns
• Strict no-shortcuts enforcement ensures complete, evidence-based analysis
• Comprehensive dependency management for monorepos
• Complete uv interpreter alignment enforcement
• Full project environment setup with all dependencies

Subagent Dispatch (in-session)

This section describes the in-session path where an orchestrator subagent fans out to tiered analysis subagents using the Agent/Task tool. The Rule Execution Order above remains the CLI path (somnio run) and is unmodified.

Entry Point

Invoke agents/orchestrator.md as the single entry point. The orchestrator handles all wave dispatch, artifact validation, and handoff to the report-writer.

Wave Plan

Wave	Mode	Agents	Tier
Wave 0	Sequential (MANDATORY gate)	env-setup	cheap
Wave 1	Parallel	repository-inventory, config-analysis	cheap, cheap
Wave 2	Parallel	cicd-analysis, testing-analysis, code-quality, harness-analyzer	cheap, mid, mid, mid
Wave 3	Parallel	api-design-analysis, data-layer-analysis	mid, mid
Wave 4	Sequential	documentation-analysis	cheap
Wave 5	Sequential	report-writer	frontier

Dispatch Table

Agent File	Tier	Reference(s) Covered	Artifact
agents/orchestrator.md	mid	— (routing only)	—
agents/env-setup.md	cheap	tool-installer, version-alignment, test-coverage	reports/.artifacts/python_health/step_00_test_coverage.md
agents/version-validator.md	cheap	version-validator	reports/.artifacts/python_health/step_00_version_validation.md
agents/repository-inventory.md	cheap	repository-inventory	reports/.artifacts/python_health/step_01_repository_inventory.md
agents/config-analysis.md	cheap	config-analysis	reports/.artifacts/python_health/step_02_config_analysis.md
agents/cicd-analysis.md	cheap	cicd-analysis	reports/.artifacts/python_health/step_03_cicd_analysis.md
agents/testing-analysis.md	mid	testing-analysis	reports/.artifacts/python_health/step_04_testing_analysis.md
agents/code-quality.md	mid	code-quality	reports/.artifacts/python_health/step_05_code_quality.md
agents/api-design-analysis.md	mid	api-design-analysis	reports/.artifacts/python_health/step_06_api_design_analysis.md
agents/data-layer-analysis.md	mid	data-layer-analysis	reports/.artifacts/python_health/step_07_data_layer_analysis.md
agents/documentation-analysis.md	cheap	documentation-analysis	reports/.artifacts/python_health/step_08_documentation_analysis.md
agents/harness-analyzer.md	mid	harness-analysis	reports/.artifacts/python_health/step_09_harness_analysis.md
agents/report-writer.md	frontier	report-generator, report-format-enforcer	reports/python_audit.md

Report Metadata (MANDATORY)

Every generated report MUST include a metadata block at the very end. This is non-negotiable — never omit it.

To resolve the source and version:

1. Look for .claude-plugin/plugin.json by traversing up from this skill's directory
2. If found, read name and version from that file (plugin context)
3. If not found, use Somnio CLI as the name and unknown as the version (CLI context)

Include this block at the very end of the report:

---
Generated by: [plugin name or "Somnio CLI"] v[version]
Skill: python-health-audit
Date: [YYYY-MM-DD]
Somnio AI Tools: https://github.com/somnio-software/somnio-ai-tools
---