Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/flujo-app/mcp-sandbox-computer-vm-for-ai/python_test_guidegit clone --depth 1 https://github.com/flujo-app/mcp-sandbox-computer-vm-for-aiWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00231 |
| Opus 5 | $0.00000 | $0.00115 |
| Sonnet 5 | $0.00000 | $0.00046 |
| Haiku 4.5 | $0.00000 | $0.00023 |
Grade A, and why
python_test_guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to python_test_guide — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Python Test Guide
When writing tests for Python code:
- Always use pytest for tests in Python code
- Before writing tests, do a code review to check for potential issues in the code. Don't blindly write tests assuming the code works properly. If you believe there may be a bug but are uncertain, ask the user. If you're reasonably certain there is a bug, write a test showing it, confirm it fails, then ask the user if you were correct and if they want you to fix the issue.
- Assume an appropriate test file already exists, find it, and append tests to that file. Don't create new test files unless you've confirmed an appropriate one does not exist.
- Test brevity is important. Use approaches for re-use and brevity including using pytest fixtures for repeated code, and using pytest parametrize for similar tests.
- Use unittest.mock, unittest.mock.patch and related test tools
- After writing a new test, run it and ensure it works. Fix any issues you find.
test are type checked, but using # type: ignore[...] comments is acceptable, only in test files.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 12 lines · 0 tokens per session scan A 8b8d8d3ef19d
python_test_guide is an agent published in the GitHub repository flujo-app/mcp-sandbox-computer-vm-for-ai (3 stars, last pushed 9d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 231 tokens. A static security scan graded it A with 0 findings. It is 100% identical to python_test_guide, differing in 0 lines, and is treated as a copy.
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