Code Interpreter

A general-purpose code execution environment powered by AWS Bedrock AgentCore Code Interpreter. Run code, execute shell commands, and manage files in a secure sandbox.

Available Tools

• execute_code(code, language, output_filename): Execute Python, JavaScript, or TypeScript code.
• execute_command(command): Execute shell commands.
• file_operations(operation, paths, content): Read, write, list, or remove files in the sandbox.
• ci_push_to_workspace(paths): Save sandbox files to the shared workspace (S3). Omit paths to save all files in the sandbox root.

Tool Parameters

execute_code

Parameter	Type	Required	Default	Description
code	string	Yes		Code to execute. Use print() for text output.
language	string	No	"python"	"python", "javascript", or "typescript"
output_filename	string	No	""	File to download after execution. Code must save a file with this exact name. Saved to workspace automatically.

execute_command

Parameter	Type	Required	Description
command	string	Yes	Shell command to execute (e.g., "ls -la", "pip install requests").

file_operations

Parameter	Type	Required	Description
operation	string	Yes	"read", "write", "list", or "remove"
paths	list	For read/list/remove	File paths. read: ["file.txt"], list: ["."], remove: ["old.txt"]
content	list	For write	Entries with path and text: [{"path": "out.txt", "text": "hello"}]

tool_input Examples

execute_code — text output

{
  "code": "import pandas as pd\ndf = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})\nprint(df.describe())",
  "language": "python"
}

execute_code — generate chart

{
  "code": "import matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport numpy as np\nx = np.linspace(0, 10, 100)\nplt.figure(figsize=(10,6))\nplt.plot(x, np.sin(x))\nplt.title('Sine Wave')\nplt.savefig('sine.png', dpi=300, bbox_inches='tight')\nprint('Done')",
  "language": "python",
  "output_filename": "sine.png"
}

execute_command — install a package

{
  "command": "pip install yfinance"
}

execute_command — check environment

{
  "command": "python --version && pip list | head -20"
}

file_operations — write a file

{
  "operation": "write",
  "content": [{"path": "config.json", "text": "{\"key\": \"value\"}"}]
}

file_operations — list files

{
  "operation": "list",
  "paths": ["."]
}

file_operations — read a file

{
  "operation": "read",
  "paths": ["output.csv"]
}

When to Use This Skill

Use code-interpreter as a sandbox for testing and prototyping code. For production tasks (creating documents, charts, presentations), prefer specialized skills.

Do NOT use for:

• Formatting or displaying code examples (respond directly with markdown code blocks)
• Explaining code or algorithms (respond directly with text)
• Simple calculations you can do mentally (just provide the answer)
• Any task that doesn't require actual code execution

Task	Recommended Skill	Notes
Create charts/diagrams	visual-design	Use this first for production charts
Create Word documents	word-documents	Has template support and styling
Create Excel spreadsheets	excel-spreadsheets	Has formatting pipeline and validation
Create PowerPoint	powerpoint-presentations	Has layout system and design patterns
Test code snippets	code-interpreter	Debug, verify logic, check output
Prototype algorithms	code-interpreter	Experiment before implementing
Install/test packages	code-interpreter	Check compatibility, test APIs
Debug code logic	code-interpreter	Isolate and test specific functions
Verify calculations	code-interpreter	Quick math or data checks

Code Interpreter vs Code Agent

	Code Interpreter	Code Agent
Nature	Sandboxed execution environment	Autonomous agent (Claude Code)
Best for	Quick scripts, data analysis, prototyping	Multi-file projects, refactoring, test suites
File persistence	Only when output_filename is set	All files auto-synced to S3
Session state	Variables persist within session	Files + conversation persist across sessions
Autonomy	You write the code	Agent plans, writes, runs, and iterates
Use when	You need to run a specific piece of code	You need an engineer to solve a problem end-to-end

Workspace Integration

All files go to the code-interpreter/ namespace — a flat, session-isolated space separate from office documents.

Sandbox → Workspace (save outputs):

// Save a specific file after execution
{ "tool": "ci_push_to_workspace", "paths": ["chart.png", "results.json"] }

// Save everything in the sandbox root
{ "tool": "ci_push_to_workspace" }

// Alternative: save a single file inline during execute_code
{ "tool": "execute_code", "output_filename": "chart.png", "code": "..." }

Uploaded files (auto-preloaded):

Files uploaded by the user (e.g. ZIP archives) are automatically available in the sandbox — no manual loading needed. Just use them directly in execute_code.

Read saved files via workspace skill:

workspace_read("code-interpreter/chart.png")
workspace_read("code-interpreter/results.json")
workspace_list("code-interpreter/")

Text files (.py, .csv, .json, .txt, etc.) are transferred as-is. Binary files (.png, .pdf, .xlsx, etc.) are handled via base64 encoding automatically.

Environment

• Languages: Python (recommended, 200+ libraries), JavaScript, TypeScript
• Shell: Full shell access via execute_command
• File system: Persistent within session; use file_operations to manage files
• Session state: Variables and files persist across multiple calls within the same session
• Network: Internet access available (can use requests, urllib, curl)

Supported Languages

• Python (recommended) — 200+ pre-installed libraries covering data science, ML, visualization, file processing
• JavaScript — Node.js runtime, useful for JSON manipulation, async operations
• TypeScript — TypeScript runtime with type checking

Pre-installed Python Libraries

Data Analysis & Visualization

Library	Common Use
pandas	DataFrames, CSV/Excel I/O, groupby, pivot
numpy	Arrays, linear algebra, random, statistics
matplotlib	Line, bar, scatter, histogram, subplots
plotly	Interactive charts, 3D plots
bokeh	Interactive visualization
scipy	Optimization, interpolation, signal processing
statsmodels	Regression, time series, hypothesis tests
sympy	Algebra, calculus, equation solving

Machine Learning & AI

Library	Common Use
scikit-learn	Classification, regression, clustering, pipelines
torch / torchvision / torchaudio	Deep learning, computer vision, audio
xgboost	High-performance gradient boosting
spacy / nltk / textblob	NLP, tokenization, NER, sentiment
scikit-image	Image processing, filters, segmentation

Mathematical & Optimization

Library	Common Use
cvxpy	Convex optimization, portfolio optimization
ortools	Scheduling, routing, constraint programming
pulp	Linear programming
z3-solver	SAT solving, formal verification
networkx / igraph	Graph algorithms, network analysis

File Processing & Documents

Library	Common Use
openpyxl / xlrd / XlsxWriter	Excel read/write with formatting
python-docx	Word document creation/modification
python-pptx	PowerPoint creation/modification
PyPDF2 / pdfplumber / reportlab	PDF read/write/generate
lxml / beautifulsoup4	XML/HTML parsing
markitdown	Convert various formats to Markdown

Image & Media

Library	Common Use
pillow (PIL)	Image resize, crop, filter, conversion
opencv-python (cv2)	Computer vision, feature detection
imageio / moviepy	Image/video I/O and editing
pydub	Audio manipulation
svgwrite / Wand	SVG creation, ImageMagick

Data Storage & Formats

Library	Common Use
duckdb	SQL queries on DataFrames and files
SQLAlchemy	SQL ORM and database abstraction
pyarrow	Parquet and Arrow format processing
orjson / ujson / PyYAML	Fast JSON/YAML parsing

Web & API

Library	Common Use
requests / httpx	HTTP requests, API calls
beautifulsoup4	Web scraping
fastapi / Flask / Django	Web frameworks

Utilities

Library	Common Use
pydantic	Data validation, schema definition
Faker	Test data generation
rich	Pretty printing, tables
cryptography	Encryption, hashing
qrcode	QR code generation
boto3	AWS SDK

For the full list of 200+ libraries with versions, run: execute_command(command="pip list")

Usage Patterns

Pattern 1: Data Analysis

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=100),
    'revenue': np.random.normal(1000, 200, 100),
    'costs': np.random.normal(700, 150, 100),
})
df['profit'] = df['revenue'] - df['costs']

print("=== Summary Statistics ===")
print(df.describe())
print(f"\nTotal Profit: ${df['profit'].sum():,.2f}")
print(f"Profit Margin: {df['profit'].mean() / df['revenue'].mean() * 100:.1f}%")

Pattern 2: Visualization (with output_filename)

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

categories = ['Q1', 'Q2', 'Q3', 'Q4']
values = [120, 150, 180, 210]
axes[0,0].bar(categories, values, color='#2196F3')
axes[0,0].set_title('Quarterly Revenue')

x = np.linspace(0, 10, 50)
axes[0,1].plot(x, np.sin(x), 'b-', linewidth=2)
axes[0,1].set_title('Trend')

sizes = [35, 30, 20, 15]
axes[1,0].pie(sizes, labels=['A','B','C','D'], autopct='%1.1f%%')
axes[1,0].set_title('Market Share')

x = np.random.normal(50, 10, 200)
y = x * 1.5 + np.random.normal(0, 15, 200)
axes[1,1].scatter(x, y, alpha=0.5, c='#FF5722')
axes[1,1].set_title('Correlation')

plt.tight_layout()
plt.savefig('dashboard.png', dpi=300, bbox_inches='tight')
print('Dashboard saved')

Pattern 3: Machine Learning

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.datasets import load_iris

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
    iris.data, iris.target, test_size=0.3, random_state=42
)

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(classification_report(y_test, y_pred, target_names=iris.target_names))

Pattern 4: SQL with DuckDB

import duckdb
import pandas as pd

orders = pd.DataFrame({
    'order_id': range(1, 101),
    'customer': [f'Customer_{i%20}' for i in range(100)],
    'amount': [round(50 + i * 3.5, 2) for i in range(100)],
})

result = duckdb.sql("""
    SELECT customer, COUNT(*) as cnt, ROUND(SUM(amount), 2) as total
    FROM orders GROUP BY customer
    HAVING COUNT(*) >= 3 ORDER BY total DESC LIMIT 10
""").df()
print(result.to_string(index=False))

Pattern 5: Fetch Data from Web

import requests
import pandas as pd

response = requests.get("https://api.example.com/data")
data = response.json()
df = pd.DataFrame(data)
print(df.head())

Pattern 6: Multi-step Workflow (session state persists)

Call 1: execute_code → load and clean data, store in variable `df`
Call 2: execute_code → analyze `df`, generate chart, save as PNG
Call 3: execute_code → export results to CSV
Call 4: file_operations(operation="read") → download the CSV

Variables (df) and files persist across calls in the same session.

Important Rules

1. matplotlib.use('Agg') before import matplotlib.pyplot — sandbox has no display.
2. Use print() for text output — stdout is how results are returned.
3. output_filename must match exactly — the filename in plt.savefig() or wb.save() must match the output_filename parameter.
4. Use execute_command for shell tasks — ls, pip install, curl, etc.
5. Use file_operations for file management — read/write/list/remove files explicitly.
6. Session state persists — variables and files remain across calls. Use this for multi-step workflows.

Common Mistakes to Avoid

• Forgetting matplotlib.use('Agg') before import matplotlib.pyplot as plt
• Using plt.show() instead of plt.savefig() — there is no display
• Typo in output_filename — must match the file saved by the code exactly
• Using execute_code for shell tasks — use execute_command instead
• Writing binary files via file_operations — use execute_code to generate binary files, then download with output_filename