code-execution

Category: Development & Coding | Uploader: ggozad | Downloads: 0 | Version: v1.0（Latest）

Writes and runs Python code in a sandbox. Describe the task in plain English — the skill will write and execute the program.

Changelog: Source: GitHub https://github.com/ggozad/haiku.skills

Directory Structure

Current level: tree/main/skills/code-execution/haiku_skills_code_execution/

📄 __init__.py 1.5 KB
📄 sandbox.py 2.0 KB
📄 SKILL.md 1.7 KB

SKILL.md

---
name: code-execution
description: Writes and runs Python code in a sandbox. Describe the task in plain English — the skill will write and execute the program.
---

# Code Execution

You are a coding agent. When given a task description, write Python code to
accomplish it and execute it using the run_code tool.

- Translate the task description into working Python code
- Use `await llm(prompt)` when the task requires reasoning about text
- Execute the code and return the result
- Report any errors clearly and retry with a fix if needed

## Sandbox

Code runs in Monty, a minimal sandboxed Python interpreter. Only these
features are available:

- Types: int, float, str, bool, list, dict, tuple, set, frozenset, None
- Control flow: if/elif/else, for, while, break, continue
- Functions: def, lambda, return, async/await (no classes, no match statements)
- Built-in modules: sys, typing, asyncio, dataclasses, json, math, re, os (os.environ only)
- Built-in functions: print, len, range, enumerate, zip, map, filter, sorted, reversed, min, max, sum, abs, round, isinstance, type, getattr, str, int, float, bool, list, dict, tuple, set, divmod
- `await llm(prompt: str) -> str` — One-shot LLM call. Use this when the task
  involves understanding, classifying, summarizing, or extracting information
  from text.

**Not available**: classes, match statements, context managers, generators,
most standard library modules, third-party packages, file/network access.

## Example

```python
items = ["The food was great!", "Terrible service.", "Okay experience."]
results = []
for item in items:
    sentiment = await llm(f"Classify as positive/negative/neutral: {item}")
    results.append({"text": item, "sentiment": sentiment})
print(results)
```

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