This executes example scripts using uv with custom configurations and provides tools to monitor, manage, and rerun tasks.
Install
mkdir -p .claude/skills/examples-auto-run && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2591" && unzip -o skill.zip -d .claude/skills/examples-auto-run && rm skill.zipInstalls to .claude/skills/examples-auto-run
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Run python examples in auto mode with logging, rerun helpers, and background control.Key capabilities
- →Execute Python examples in automated mode
- →Manage background test processes with pidfiles
- →Tail and collect logs from example runs
- →Generate and rerun failed test cases
- →Configure dependency extras for example execution
How it works
The skill wraps the uv runner to execute examples with specific environment overrides, logging configurations, and automated rerun capabilities.
Inputs & outputs
When to use examples-auto-run
- →Run automated Python examples
- →Manage background test processes
- →Tail logs for debugging example failures
- →Rerun failed Python test cases
About examples-auto-run
Runs example scripts using uv with specific configuration overrides for interactive/auto-mode. It provides shell utilities to start, stop, tail logs, and perform rerun operations on failures.
Run python examples in auto mode with logging, rerun helpers, and background control.
When not to use it
- →Running examples that require manual user interaction
- →Executing sensitive code within the Codex sandbox
Prerequisites
Limitations
- →Requires manual validation of exit-0 results
- →Excludes specific modules like vercel_runner due to credentials
How it compares
It provides a persistent, automated test runner with log management and rerun helpers instead of manual script execution.
Compared to similar skills
examples-auto-run side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| examples-auto-run (this skill) | 2 | 4mo | Review | Intermediate |
| verify-local | 0 | 3mo | Review | Beginner |
| python-testing-patterns | 77 | 4mo | Review | Intermediate |
| python-repl | 6 | 6mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by openai
View all by openai →You might also like
verify-local
minuum
MoNaVLA 로컬 검증 대시보드를 127.0.0.1:9001 에 띄우고 링크 안내. CH57 사진 확인, 반증 테스트 판단, GitHub Pages 배포 확인, 학습 로그 등을 브라우저에서 체크리스트로 검증. "검증 페이지", "verify", "로컬 대시보드" 등의 요청에 사용.
python-testing-patterns
wshobson
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
python-repl
gptme
Interactive Python REPL automation with common helpers and best practices
python-playground
pydantic
Run and test Python code in a dedicated playground directory. Use when you need to execute Python scripts, test code snippets, investigate CPython behavior, or experiment with Python without affecting the main codebase.
mflux-manual-testing
filipstrand
Manually validate mflux CLIs by exercising the changed paths and reviewing output images/artifacts.
dev
atopile
LLM-focused workflow for working in this repo: compile Zig, run the orchestrated test runner, consume test-report.json/html artifacts, and discover/debug ConfigFlags.