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 17 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.
Initialize a Mastra AI project. Define two agents: a 'CodeGenerator' agent and a 'CodeReviewer' agent. The CodeGenerator should use Claude Sonnet 4 and be equipped with tools to write files to a simulated filesystem. The CodeReviewer should use Llama 3 8B Instruct (via a custom tool calling Hugging Face Inference Endpoints) and tools to read files and provide feedback. Outline their initial roles and a simple interaction flow for generating a function and getting it reviewed.
```typescript
import { createAgent, createTool, Workflow } from '@mastra-ai/core';
const codeGenerator = createAgent({
id: 'code-generator',
model: 'claude-sonnet-4',
// ... other configs
});
const codeReviewer = createAgent({
id: 'code-reviewer',
model: 'llama-3-8b-instruct-hf-endpoint', // Placeholder for custom endpoint
// ... other configs
});
// Define a workflow
const workflow = new Workflow();
// ... Add steps for agents to interact
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Accelerated Code Dev & Review Agent
Inspired by Claude's growing footprint in GitHub commits, this challenge focuses on building an advanced agentic development environment. You will use Mastra AI to orchestrate a team of agents that automate parts of the software development lifecycle, from generating code snippets based on user stories to automated testing and code review. The system should integrate with a simulated codebase, providing intelligent suggestions and even committing code. Emphasis is placed on code quality, security, and developer productivity.
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.