Is mastering-python-skill safe to install?

SaferSkills independently audited mastering-python-skill (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

.claude/skills/mastering-python-skill/SKILL.md · 1 file

.claude/skills/mastering-python-skill/SKILL.md5.6 KB · Markdown

name: mastering-python-skill

description: Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM", "write pytest tests", "create a FastAPI endpoint", "run uvicorn server", "configure alembic migrations", "set up logging", "process data with pandas", or "debug Python errors". Triggers on "Python best practices", "type hints", "async Python", "packaging", "virtual environments", "Pydantic validation", "dependency injection", "SQLAlchemy models".

context: fork

agent: senior-python-engineer

allowed-tools:

- Read

- Write

- Bash

- Edit

- Glob

- Grep

metadata:

version: 2.2.0

domains:

- python

- testing

- packaging

- web-development

- async

- security

Mastering Python Skill

Production-ready Python patterns with runnable code examples.

Workflow
Reference Files
Sample CLI Tools
When NOT to Use
Full Table of Contents

Workflow

Phase 1: Setup

Verify Python version

   python --version  # Require 3.10+, prefer 3.12+

Create and activate virtual environment

   python -m venv .venv && source .venv/bin/activate

Install dependencies

   poetry install  # or: pip install -r requirements.txt

Phase 2: Develop

Reference appropriate patterns:

Follow project structure from project-structure.md

Phase 3: Validate

Run quality checks

   ruff check . && ruff format --check .
   mypy src/

Run tests with coverage

   pytest -v --cov=src --cov-report=term-missing

Phase 4: Deploy

Build and verify package

   python -m build && twine check dist/*

Deploy per docker-deployment.md or ci-cd-pipelines.md

Pre-Completion Checklist:

- [ ] All tests pass
- [ ] mypy reports no errors
- [ ] ruff check clean
- [ ] Coverage ≥80%
- [ ] No security warnings in dependencies

Reference Files

Category	Files	Key Topics
Foundations	syntax-essentials, type-systems, project-structure, code-quality	Variables, type hints, generics, src layout, ruff, mypy
Patterns	async-programming, error-handling, decorators, context-managers, generators	async/await, exceptions, Result type, with statements, yield
Testing	pytest-essentials, mocking-strategies, property-testing	Fixtures, parametrize, unittest.mock, Hypothesis
Web APIs	fastapi-patterns, pydantic-validation, database-access	Dependencies, middleware, validators, SQLAlchemy async
Packaging	poetry-workflow, pyproject-config, docker-deployment	Lock files, PEP 621, multi-stage builds
Production	ci-cd-pipelines, monitoring, security	GitHub Actions, OpenTelemetry, OWASP, JWT

See TOC.md for detailed topic lookup.

Sample CLI Tools

Runnable examples demonstrating production patterns:

Tool	Demonstrates	Reference
async_fetcher.py	Async HTTP, rate limiting, error handling	async-programming.md
config_loader.py	Pydantic settings, .env files, validation	pydantic-validation.md
db_cli.py	SQLAlchemy async CRUD, repository pattern	database-access.md
code_validator.py	Run→check→fix with ruff and mypy	code-quality.md

# Test examples
python sample-cli/async_fetcher.py https://httpbin.org/get
python sample-cli/config_loader.py --show-env
python sample-cli/db_cli.py init --sample-data && python sample-cli/db_cli.py list
python sample-cli/code_validator.py src/

When NOT to Use

Non-Python languages: Use language-specific skills
ML/AI model internals: Use PyTorch/TensorFlow skills
Cloud infrastructure: Use AWS/GCP skills for infra (this covers code)
Legacy Python 2: Focus is Python 3.10+

mastering-python-skill— agent skill

Is mastering-python-skill safe to install?

Mastering Python Skill

Contents

Workflow

Phase 1: Setup

Phase 2: Develop

Phase 3: Validate

Phase 4: Deploy

Reference Files

Sample CLI Tools

When NOT to Use

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