Operator-ready prompt for reuse, tuning, and workspace runs.
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Structured source with 19 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Integrate Sarvam AI's speech-to-text capabilities to allow users to verbally provide their `skill_description` and `name` to the 'SkillProofAgent'. The agent should then process this voice input to populate the document fields. Describe how Sarvam AI would convert speech to text and how your Mastra AI agent would then interpret and act upon this text input to start generating a SkillProof document. Provide a conceptual flow.
```typescript
// Conceptual integration with Sarvam AI and Mastra Agent
// import SarvamClient from 'sarvam-ai-sdk'; // Hypothetical SDK
// const sarvam = new SarvamClient({ apiKey: 'YOUR_SARVAM_API_KEY' });
// async function processVoiceInput(audioData: Buffer) {
// const transcription = await sarvam.speechToText(audioData); // Use Sarvam ASR
// // Then, send transcription to Mastra AI agent
// // const agentResponse = await agent.run(transcription);
// // Agent's turn handler would parse transcription, extract intent (e.g., 'create_skill_proof'),
// // and update the SkillProofDocument state based on extracted skillDescription and name.
// return 'SkillProof document started based on your voice input.';
// }
// Mastra AI agent would have a handler that understands intents from natural language:
// agent.onIntent('create_skill_proof', async ({ payload, state }) => {
// state.set('skillProofDocument', { skillName: payload.skill, descriptionGenerated: '', ... });
// return 'Got it. Generating your skill proof.';
// });
```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
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This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
SkillProof Document Agent
This challenge involves creating an AI agent using the Mastra AI TypeScript framework that generates and verifies 'SkillProof' documents – an AI-native standard designed to replace traditional credentials for displaying professional skills and 'vibe coding'. The agent will leverage Qwen 2 for nuanced skill description and personality assessment generation, while integrating custom tools for external verification of AI proficiency levels. A key aspect is the implementation of a conversational voice interface via Sarvam AI, allowing users to interact naturally to create and modify their SkillProof documents, demonstrating advanced natural language processing and structured output generation.
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.