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.
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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.
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Decide which failure mode you want to evaluate first before you branch the prompt.
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Structured source with 26 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement a continuous loop or event listener where the `MarketMonitorAgent` is triggered by new simulated DAU data for 'Threads' and 'X'. For each update:
1. The agent should use `fetchDauData` to get the latest numbers.
2. Analyze the data using `Mistral Large 2` to determine if a 'positive_shift', 'negative_shift', or 'stable' trend exists.
3. If a significant shift is detected, use `storeTrendInPinecone` to save the trend and its explanation to Pinecone. This should relate to the `TrendDetection` evaluation task.
```typescript
// ... (previous MarketMonitorAgent setup)
async function analyzePlatformTrend(platform: string) {
console.log(`Running analysis for ${platform}...`);
const result = await MarketMonitorAgent.run({
prompt: `Analyze the latest DAU data for ${platform} and identify any significant trends or shifts. Use the fetchDauData tool. If a significant trend is found, store it in Pinecone using storeTrendInPinecone.`,
context: {
platform: platform // Provide context for the agent to use in its tools
},
// Mastra AI can allow specifying a 'goal' or 'workflow'
});
console.log(`Analysis for ${platform} completed:`, result);
// You would extract the trend_change, magnitude, explanation from result.response
// and log it or pass it to an evaluation function.
}
// Simulate new data arrival for evaluation
// In a real system, this would be an event or scheduled task.
// For evaluation, you might call analyzePlatformTrend with specific simulated data points.
// Example of a simulated data stream trigger:
// analyzePlatformTrend('Threads');
// analyzePlatformTrend('X');
```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.
Real-time Market Trend Agent
Develop a real-time market trend analysis agent using Mastra AI, designed to monitor specific metrics (e.g., user engagement on social platforms, e-commerce order volumes) and identify significant shifts. The agent will leverage Mistral Large 2 via the Hugging Face Inference API for advanced pattern recognition and sentiment analysis. It will utilize Pinecone as a vector store to maintain a historical context of trends and associated data points, preventing information overload by focusing on novel insights. A Bito AI-like interface component will allow users to interact with the agent for on-demand reports and trend explanations, requiring efficient, scalable processing to deliver timely insights.
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.