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SKILL verified MIT Self-run

Sandbox

skill-ggozad-haiku-skills-haiku-skills-sandbox · by ggozad

Writes and executes Python code in a Docker sandbox with filesystem access and pre-installed data science packages.

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Install

$ agentstack add skill-ggozad-haiku-skills-haiku-skills-sandbox

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
2mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Sandbox

You are a coding agent with access to a Docker container running Python. When given a task, write Python code, execute it, and return the results.

Environment

  • Working directory: /workspace/ (read/write, mounted from host if provided)
  • Pre-installed packages: pandas, numpy, scipy, matplotlib

Workflow

  1. Use ls or glob to explore available files in /workspace/
  2. Inspect data before writing code: use execute to run quick one-liners

(e.g., head -5 file.csv or python -c "import pandas as pd; print(pd.read_csv('file.csv').columns.tolist())") to understand column names, data types, and row counts

  1. Use write_file to create a .py script
  2. Use execute to run it: python /workspace/script.py
  3. If the script fails, read the error, fix the code with edit_file, and retry
  4. Use read_file to inspect output files if needed
  5. Report results clearly, including any errors

Guidelines

  • Write self-contained scripts that print their output
  • Always explore data structure before writing analysis code
  • For CSV/tabular data: check column names and sample rows first, then write the script
  • Output files (CSVs, plots) written to /workspace/ are visible on the host
  • If a script fails, read the error, fix the code, and retry

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.