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 21 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.
Enhance your 'CodeReviewer' agent or create a new 'ContextAgent'. Implement a tool within Mastra AI that uses Cohere's embedding API to perform semantic search over a small, pre-indexed set of 'best practice' code snippets or internal documentation. The agent should use this tool to retrieve relevant context *before* performing a code review, thereby improving the quality and relevance of its feedback. Provide code for initializing the Cohere client and creating the embedding tool.
```typescript
// Example Cohere integration (pseudo-code)
import cohere from 'cohere-ai';
const cohereClient = new cohere.CohereClient({ token: process.env.COHERE_API_KEY });
const semanticSearchTool = createTool({
id: 'semantic_code_search',
description: 'Searches for relevant code examples or documentation based on a query.',
schema: { "type": "object", "properties": { "query": { "type": "string" } }, "required": ["query"] },
async execute({ query }) {
const response = await cohereClient.embed({
texts: [query],
model: 'embed-english-v3.0',
inputType: 'search_query',
});
// ... then search Qdrant/vector store with embeddings (mocked here)
return 'Relevant code snippets found';
},
});
// ... add tool to agent
```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 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.