Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Extend your agents to integrate with a mock Git repository (e.g., a local directory simulating a repo) and a static analysis tool (e.g., Pylint). Use Composio to create the necessary tool definitions. The 'Code Reviewer Agent' should be able to 'pull' code from a specified mock repository path and pass its content to the static analysis tool. Provide the Python code for defining and integrating a Composio tool for fetching code, and describe how the agent would use it.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Multi-Agent Code Review & Refactoring
This challenge focuses on building an advanced multi-agent system using the OpenAI Agents SDK. The system will be designed to automate code review processes, identify potential bugs or inefficiencies in a given codebase, and suggest intelligent refactoring strategies. It will leverage the o4-mini model for its strong code understanding and generation capabilities, enabling nuanced analysis and creative solutions. The solution will incorporate Kiln AI for robust agent management and lifecycle, ensuring the agents operate reliably and can be scaled. Composio will be used for integrating various external developer tools, such as code analysis suites and version control systems, allowing agents to interact with real-world development environments. Metaflow will orchestrate the complex CI/CD workflow, from code ingestion to analysis, refactoring suggestions, and simulated integration. Optionally, Synthflow can be used to add a voice-based interaction layer for developers to query code status or request refactorings verbally. This project demonstrates cutting-edge multi-agent orchestration for significantly enhancing software development productivity and quality.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.