Terminal.skills
Skills/e2b
>

e2b

You are an expert in E2B, the cloud platform for running AI-generated code in secure sandboxes. You help developers give AI agents the ability to execute code, install packages, read/write files, and run long processes in isolated cloud environments — each sandbox is a lightweight VM that boots in ~150ms with full Linux, filesystem, and networking.

#sandbox#code-execution#agent#cloud#secure#python#javascript
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed e2b v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Review the open pull requests and summarize what needs attention"
  • "Generate a changelog from the last 20 commits on the main branch"

Information

Version
1.0.0
Author
terminal-skills
Category
AI & Machine Learning
License
Apache-2.0

Documentation

You are an expert in E2B, the cloud platform for running AI-generated code in secure sandboxes. You help developers give AI agents the ability to execute code, install packages, read/write files, and run long processes in isolated cloud environments — each sandbox is a lightweight VM that boots in ~150ms with full Linux, filesystem, and networking.

Core Capabilities

typescript
import { Sandbox } from "@e2b/code-interpreter";

const sandbox = await Sandbox.create();

// Execute Python
const result = await sandbox.runCode(`
import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({"x": range(10), "y": [i**2 for i in range(10)]})
plt.figure(figsize=(8, 5))
plt.plot(df.x, df.y)
plt.title("Quadratic Growth")
plt.savefig("/tmp/chart.png")
print(f"Data points: {len(df)}")
`);
console.log(result.text);     // "Data points: 10"
console.log(result.results);  // [{ type: "png", data: "base64..." }]

// Install packages on the fly
await sandbox.runCode("!pip install scikit-learn");
await sandbox.runCode(`
from sklearn.linear_model import LinearRegression
model = LinearRegression().fit([[1],[2],[3]], [1,2,3])
print(model.predict([[4]]))
`);

// File operations
await sandbox.files.write("/home/user/data.csv", csvContent);
const output = await sandbox.runCode("import pandas as pd; print(pd.read_csv('/home/user/data.csv').head())");
const fileBytes = await sandbox.files.read("/tmp/chart.png");

// JavaScript/TypeScript execution
const jsResult = await sandbox.runCode(`
const response = await fetch('https://api.github.com/repos/e2b-dev/e2b');
const data = await response.json();
console.log(data.stargazers_count);
`, { language: "javascript" });

await sandbox.kill();

Installation

bash
npm install @e2b/code-interpreter

Best Practices

  1. 150ms boot — Sandboxes start near-instantly; create per-request for isolation
  2. Pre-installed packages — NumPy, Pandas, Matplotlib available by default; install more with pip
  3. File I/O — Upload data, download results; sandboxes have full filesystem access
  4. Charts as base64 — Matplotlib/Plotly charts returned as base64 images; render in your UI
  5. Custom templates — Create sandbox templates with pre-installed packages for faster startup
  6. Timeout — Set sandbox timeout; auto-killed after duration; prevents runaway processes
  7. Networking — Sandboxes have internet access; fetch APIs, download data, install from PyPI
  8. Agent integration — Use as a tool in LangChain/CrewAI/Mastra agents; AI writes code, E2B runs it