Back to Prompt Library
implementation
Initial Code Generation and Linting Integration
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: Agentic Code Generation & Refinement
Format
Code-aware
Lines
1
Sections
1
Linked challenge
Agentic Code Generation & Refinement
Prompt source
Original prompt text with formatting preserved for inspection.
1 lines
1 sections
No variables
0 checklist items
Implement the agent's ability to generate an initial Python function based on a given prompt (e.g., 'Generate a prime number checker'). Integrate your mock linter tool. The agent should call the linter after generating the code and analyze its output. Your implementation should use `client.beta.assistants.create` and `client.beta.threads.create` for the agent's lifecycle, and handle `requires_action` for tool calls.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.