Borrowing it
Nothing to install: this file belongs to lu-zhengda/mcp-python-exec-sandbox. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lu-zhengda/mcp-python-exec-sandbox/main/CLAUDE.mdgit clone --depth 1 https://github.com/lu-zhengda/mcp-python-exec-sandboxWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/lu-zhengda/mcp-python-exec-sandbox/claude-md)<a href="https://agentmods.dev/instructions/lu-zhengda/mcp-python-exec-sandbox/claude-md"><img src="https://agentmods.dev/badge/instructions/lu-zhengda/mcp-python-exec-sandbox/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/lu-zhengda/mcp-python-exec-sandbox/claude-md"><img src="https://agentmods.dev/badge/instructions/lu-zhengda/mcp-python-exec-sandbox/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00588 | $0.00588 |
| Opus 5.5 | $0.00235 | $0.00235 |
| Sonnet 5.5 | $0.00118 | $0.00118 |
| Haiku 4.5 | $0.00059 | $0.00059 |
Grade A, and why
mcp-python-exec-sandbox CLAUDE.md 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
Project
MCP server for sandboxed Python execution. Scripts run in ephemeral, isolated environments with inline dependencies (PEP 723) via uv. Zero host pollution.
Stack
- Python 3.13+, no runtime deps beyond
fastmcpandtomli-w uvfor script execution, dependency resolution, and Python version managementhatchlingbuild backend,src/layout
Structure
src/mcp_python_exec_sandbox/ # Package source
server.py # FastMCP server + tool definitions
executor.py # uv subprocess orchestration
script.py # PEP 723 metadata parsing/merging
sandbox.py # Sandbox ABC + factory
sandbox_linux.py # bubblewrap sandbox (Linux)
sandbox_docker.py # Docker sandbox (macOS/any)
config.py, cache.py, output.py, errors.py
tests/ # Unit + integration tests (mocked or local uv)
e2e_tests/ # End-to-end tests (require uv + network)
profiles/ # Dockerfile, warmup packages
Commands
uv sync --dev # Install deps
uv run pytest tests/ -v # Unit + integration tests
uv run pytest e2e_tests/ -v # E2E tests (slow, needs network)
Rules
- Run
uv run pytest tests/ -vbefore committing. All tests must pass. - Keep dependencies minimal. Do not add runtime deps without strong justification.
- Lint with
uv run ruff check .and format withuv run ruff format --check .before committing. Fix issues with--fix/ruff format .. - Tool docstrings in
server.pyare user-facing — they become the MCP tool descriptions that agents see. Write them for an LLM audience: include examples, avoid unexplained jargon, link PEPs. - Always pin versions in examples (e.g.
"pandas>=2.2"not"pandas"). - Sandbox backends must degrade gracefully: if the tool (bwrap, docker) is missing, fall back to
NoopSandboxwith a warning. Native sandbox is Linux-only (bwrap); macOS defaults to Docker.
Contribution format
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.
- 2mo ago First seen · 53 lines · 588 tokens per session scan A 7c2c223559e4
mcp-python-exec-sandbox CLAUDE.md is an instructions file published in the GitHub repository lu-zhengda/mcp-python-exec-sandbox (1 stars, last pushed 9mo ago), licensed MIT. It adds 588 tokens to every session, about $0.0024 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
mcp-python-exec-sandbox CLAUDE.md compared with instructions from other repositories
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