Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/aws-samples/sample-strands-agent-with-agentcore/code-interpreternpx skills add aws-samples/sample-strands-agent-with-agentcore --skill code-interpretergit clone --depth 1 https://github.com/aws-samples/sample-strands-agent-with-agentcoreWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00025 | $0.03607 |
| Opus 5 | $0.00013 | $0.01803 |
| Sonnet 5 | $0.00005 | $0.00721 |
| Haiku 4.5 | $0.00003 | $0.00361 |
Grade A, and why
code-interpreter scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Network:** Internet access available (can use `requests`, `urllib`, `curl`) How it starts
The opening of the file, as written. The whole thing — 406 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 mounted session workspace.
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 publish as a durable session file. Code must save a file with this exact name. |
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"
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 406 lines · 25 tokens per session scan A 2fbb189d72d4
code-interpreter is a skill published in the GitHub repository aws-samples/sample-strands-agent-with-agentcore (191 stars, last pushed 6d ago), licensed MIT. It adds 25 tokens to every session and 3,607 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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