# Aymenfurter Octoclaw Foundry Code Interpreter > Foundry Code Interpreter Agent - Skill: `tomevault-io/aymenfurter-octoclaw-foundry-code-interpreter` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/aymenfurter-octoclaw-foundry-code-interpreter` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/aymenfurter-octoclaw-foundry-code-interpreter/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/aymenfurter-octoclaw-foundry-code-interpreter --- # Foundry Code Interpreter Agent Create an ad-hoc Foundry agent with Code Interpreter enabled using the new v2 Responses API. The agent can write and execute Python code in a sandboxed environment to solve data analysis tasks, generate visualizations, and perform computations. ## Prerequisites The user must have completed the Foundry setup (the `setup-foundry` skill). They need: - `FOUNDRY_PROJECT_ENDPOINT` -- the project endpoint URL - `FOUNDRY_MODEL_DEPLOYMENT_NAME` -- the model deployment name (e.g. `gpt-4.1-mini`) If these are not set, ask the user for the values. ## Steps ### 1. Determine the Task Ask the user what they want the code interpreter agent to do. Examples: - "Analyze this CSV file and create a bar chart" - "Solve this equation: sin(x) + x^2 = 42" - "Generate a histogram of this data" - "Calculate statistics for this dataset" If the user provides a file, note the file path for upload. ### 2. Run the Code Interpreter Agent Use the following Python script. Replace placeholders with actual values. **Without file upload (computation / code generation):** ```bash python3 << 'PYEOF' import os from azure.identity import DefaultAzureCredential from azure.ai.projects import AIProjectClient from azure.ai.projects.models import PromptAgentDefinition, CodeInterpreterTool, CodeInterpreterToolAuto endpoint = os.environ.get("FOUNDRY_PROJECT_ENDPOINT", "") model = os.environ.get("FOUNDRY_MODEL_DEPLOYMENT_NAME", "") project_client = AIProjectClient( endpoint=endpoint, credential=DefaultAzureCredential(), ) with project_client: openai_client = project_client.get_openai_client() # Create agent with code interpreter agent = project_client.agents.create_version( agent_name="CodeInterpreterAgent", definition=PromptAgentDefinition( model=model, instructions="You are a data analysis and computation assistant. Write and execute Python code to solve the user's problem. Show results clearly.", tools=[CodeInterpreterTool(container=CodeInterpreterToolAuto())], ), description="Ad-hoc code interpreter agent.", ) print(f"Agent created: {agent.name} v{agent.version}") # Create a conversation conversation = openai_client.conversations.create() print(f"Conversation: {conversation.id}") # Send the user's request response = openai_client.responses.create( conversation=conversation.id, input="USER_PROMPT_HERE", extra_body={"agent": {"name": agent.name, "type": "agent_reference"}}, ) # Print the response for item in response.output: if item.type == "message": for content in item.content: if content.type == "output_text": print(content.text) # Clean up project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version) print("Agent cleaned up.") PYEOF ``` **With file upload (data analysis):** ```bash python3 << 'PYEOF' import os from azure.identity import DefaultAzureCredential from azure.ai.projects import AIProjectClient from azure.ai.projects.models import PromptAgentDefinition, CodeInterpreterTool, CodeInterpreterToolAuto endpoint = os.environ.get("FOUNDRY_PROJECT_ENDPOINT", "") model = os.environ.get("FOUNDRY_MODEL_DEPLOYMENT_NAME", "") file_path = "FILE_PATH_HERE" project_client = AIProjectClient( endpoint=endpoint, credential=DefaultAzureCredential(), ) with project_client: openai_client = project_client.get_openai_client() # Upload the file with open(file_path, "rb") as f: uploaded_file = openai_client.files.create(purpose="assistants", file=f) print(f"File uploaded: {uploaded_file.id}") # Create agent with code interpreter and file access agent = project_client.agents.create_version( agent_name="DataAnalysisAgent", definition=PromptAgentDefinition( model=model, instructions="You are a data analysis assistant. Analyze the uploaded file and fulfil the user's request. Generate charts or output files when appropriate.", tools=[CodeInterpreterTool(container=CodeInterpreterToolAuto(file_ids=[uploaded_file.id]))], ), description="Ad-hoc data analysis agent with file.", ) print(f"Agent created: {agent.name} v{agent.version}") # Create a conversation conversation = openai_client.conversations.create() # Send the user's request response = openai_client.responses.create( conversation=conversation.id, input="USER_PROMPT_HERE", extra_body={"agent": {"name": agent.name, "type": "agent_reference"}}, ) # Print response and download generated files for item in response.output: if item.type == "message": for content in item.content: if content.type == "output_text": print(content.text) # Check for file citations if hasattr(content, "annotations") and content.annotations: for ann in content.annotations: if ann.type == "container_file_citation": file_content = openai_client.containers.files.content.retrieve( file_id=ann.file_id, container_id=ann.container_id ) safe_name = os.path.basename(ann.filename) with open(safe_name, "wb") as out: out.write(file_content.read()) print(f"Downloaded: {safe_name}") # Clean up project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version) print("Agent cleaned up.") PYEOF ``` ### 3. Present Results After running the script: - Show the text output from the agent - If files were generated (charts, CSVs), tell the user where they were saved - If there were errors, explain what went wrong and suggest fixes ### 4. Follow-up The conversation is stateful. If the user wants to continue the analysis, reuse the same conversation ID and agent. Only clean up when the user is done. ## Supported File Types The code interpreter supports: `.csv`, `.json`, `.xlsx`, `.txt`, `.pdf`, `.py`, `.md`, `.html`, `.png`, `.jpg`, `.gif`, and more. ## Notes - Code Interpreter runs Python in a sandboxed container with common data science packages (pandas, matplotlib, numpy, etc.) - Each session is active for 1 hour with a 30-minute idle timeout - Additional charges apply beyond standard token-based fees - For custom packages, a custom code interpreter container can be configured --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/aymenfurter) — claim your Tome and manage your conversions.