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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.

Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

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.

Datasets

Shared data for this challenge

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.

Max Score: 4
Dimensions
4 scoring checks
Binary
4 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1documentschemaadherence

DocumentSchemaAdherence

Generated document strictly adheres to the defined JSON schema.

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2correctverificationstatus

CorrectVerificationStatus

Verification status is correct based on proficiency and threshold.

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 3descriptionqualityscore

DescriptionQualityScore

LLM-generated description quality and relevance (1-5). • target: 4 • range: 1-5

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 4vibecodingrelevance

VibeCodingRelevance

Relevance of the generated vibe coding assessment to the skill (1-5). • target: 4 • range: 1-5

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

Master Mastra AI's core concepts including agent definition, memory system, and tool integration for building complex workflows.

Implement structured output generation using Qwen 2 to adhere to a predefined 'SkillProof' document schema (e.g., JSON-LD or similar AI-native format).

Utilize Mastra AI's built-in memory capabilities (e.g., with Redis) to persistently store draft documents and user preferences, enabling multi-turn document refinement.

Develop a custom tool, 'verify_ai_proficiency', that takes skill descriptions and provides a mock verification status and score.

Integrate Sarvam AI to create a conversational voice interface, allowing users to verbally dictate document content and request verification checks.

Design prompts for OpenAI o3 to parse natural language input into structured data for the 'SkillProof' document and to generate a 'vibe coding' personality assessment.

Implement a document versioning and storage mechanism (e.g., saving to a local file system or mock cloud storage) as part of the agent's capabilities.

Start from your terminal
$npx -y @versalist/cli start skillproof-document-agent

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

Requires VERSALIST_API_KEY. Works with any MCP-aware editor.

Docs
Manage API keys
Challenge at a glance
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Vera

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Timeline and host

Operating window

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Tool Space Recipe

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Evaluation
Rubric: 4 dimensions
·DocumentSchemaAdherence(1%)
·CorrectVerificationStatus(1%)
·DescriptionQualityScore(1%)
·VibeCodingRelevance(1%)
Gold items: 2 (2 public)

Frequently Asked Questions about SkillProof Document Agent