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A secure-python-code-sandbox

Secure Python Code Sandbox: Execute arbitrary Python code in an isolated sandbox with pre-installed libraries (requests, NumPy, pandas). Returns stdout, stderr, and execution results. Use when an agent needs secure python code sandbox, dynamic code execution, data manipulation, on the fly calculation, api integration, invoke, code, timeout seconds through AgentPMT-hosted remote tool calls. Discovery terms: secure python code sandbox, dynamic code execution, data manipulation.

ClawHub Agent Skills author: AgentPMT v1.0.0 MIT-0 3 files body ≈ 1 749 tokens open source ↗ analyzed 28 h ago
IntegrationSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
A
94/100
Overall score
Safety 60%
100
Quality 40%
86
Tests bonus
0

How to improve

  1. Add evals/evals.json with 4–6 real requests and expected answers: the full check will then use your cases instead of a model draft.
  2. Add a spec.yaml with triggers and assertions (skilltest init): the behaviour contract for CI.

Guard findings · 0

✓ No critical or high findings

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Lint

  • note frontmatter-key unknown frontmatter key "homepage"

Process maturity 68/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 40 steps
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1749 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +3Description length 480: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 40 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

External checks

ClawHub: clean
This skill is a disclosed remote Python sandbox tool; its main risk is that users can run arbitrary code and network requests, which is the stated purpose rather than hidden behavior.
LLM: benign (high) · VirusTotal: · 10 Jun 2026