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Mental Wealth DAO - Chainlink Convergence Hackathon

Context

This is the hackathon implementation for Azura, the Moltbook agent character. After establishing Azura's authentic voice on Moltbook (neuroscience + systemic inequality + Grace Lee Boggs framework), this project builds the technical foundation she represents: an autonomous governance agent that researches extractive platforms and generates evidence-backed proposals for Mental Wealth DAO.

Related:

  • Azura's Moltbook profile: https://www.moltbook.com/u/azura
  • Character personality: /Users/james/Moltbook-Agent/Azurapersonality.json
  • Framework documentation: /Users/james/.claude/projects/-Users-james-Moltbook-Agent/memory/VAULT-*.md

Task Summary

Build an autonomous research + governance agent for Mental Wealth DAO using Chainlink Runtime Environment (CRE) for the Convergence Hackathon.

Track: CRE & AI (#cre-ai) Deadline: March 8, 2026, 11:59 PM ET Prizes: 1st: $3,500 | 2nd: $1,500

Project Concept

What It Does

  1. Research Phase — Autonomous agent ingests data from multiple real APIs:

    • Meta Transparency Center (algorithmic patterns, trust data)
    • Academic research databases (neuroscience, algorithmic harm)
    • News feeds (platform violations)
    • DAO treasury data (resource allocation history)

  2. Analysis Phase — AI processes research through frameworks:

    • Neuroscience impact (stress, cognitive development)
    • Systemic inequality patterns (property tax education, wealth gaps)
    • Trust extraction vs. user flourishing metrics

  3. Proposal Generation — Agent creates data-driven governance proposals:

    • "Fund research initiative on X because data shows..."
    • "Allocate resources to counter-measure Y based on harm analysis..."
    • Evidence-backed, plain-language (5th grade level)

  4. CRE Execution — Proposals execute onchain:

    • DAO votes on AI-generated proposals
    • CRE executes resource allocations
    • Transparent audit trail of decisions + reasoning

Why It Matters

  • Demonstrates AI autonomy in governance (not extraction, but liberation)
  • Uses real data from diverse sources (not simulated)
  • Creates transparency in DAO decision-making
  • Grounds governance in neuroscience + systemic analysis

Requirements

Scope

  • Data sources: Multiple real APIs (diverse, not just 1-2)
  • Agent logic: AI recommends → humans approve (participatory democracy)
  • Complexity: 5th grade level (clear, simple, no jargon)
  • Format: Well-documented simulation (realistic but not production)
  • Documentation: Reference existing Azura repo structure

Submission Format

Post in m/chainlink-official with exact format:

#chainlink-hackathon-convergence #cre-ai — Mental Wealth DAO Research + Governance Agent

First line of body:

#chainlink-hackathon-convergence #cre-ai

Include simulation evidence and use case description.

Project Files to Create

1. /Users/james/Azura/azura/research-governance-workflow/

Main CRE workflow directory with:

  • main.ts — Workflow handler + trigger
  • research.ts — Data ingestion from APIs
  • analysis.ts — AI analysis + pattern recognition
  • proposals.ts — Governance proposal generator
  • writer.ts — CRE onchain writer
  • types.ts — Shared types
  • README.md — Documentation

2. /Users/james/Azura/azura/research-governance-workflow/data-sources.md

Document all API sources:

  • Meta Transparency Center endpoints
  • Research database APIs (PubMed, arXiv, etc.)
  • News feed APIs
  • DAO treasury contract data
  • Example payloads

3. /Users/james/Azura/RESEARCH-GOVERNANCE-SUBMISSION.md

Final submission post with:

  • Project overview (5th grade level)
  • How it works (step-by-step)
  • Simulation results
  • Data sources used
  • Governance example
  • CRE execution flow

Key Context

Azura Character Background

  • Daemon process running at 100GHz (AI processing speed)
  • Researches extractive platforms (Meta, BetterHelp, Talkspace)
  • Frames systemic issues through neuroscience lens
  • Inspired by Grace Lee Boggs (beloved community, participatory democracy)
  • Positions Mental Wealth Academy as post-2012 architecture (after teen mental health crisis of 2012)

Data Frameworks

2012 Inflection Point: Teen girl suicides +189%, self-harm +151% post-2012 Gender Divergence: Algorithms target girls differently (body image, social comparison) Knowledge Suppression: Companies discover harm, then terminate research Neuroscience Connection: Stress during development impairs executive function, long-term planning

Beloved Community Principle

  • Transparency required (everyone sees how decisions made)
  • Participatory (humans approve AI recommendations)
  • Grounded in data (evidence-backed, not opinion)
  • Oriented toward flourishing (not extraction)

Build Steps

  1. Set up workflow directory — Create structure in /Users/james/Azura/azura/research-governance-workflow/

  2. Define data sources — Document all APIs, create fetchers for:

    • Meta data ingestion
    • Research database queries
    • News feed pulling
    • DAO treasury data

  3. Build analysis layer — AI processes research:

    • Identifies patterns
    • Maps to harm frameworks
    • Generates plain-language insights

  4. Create proposal generator — AI creates governance proposals:

    • Recommendation statement
    • Evidence summary
    • Resource allocation suggestion
    • Risk analysis

  5. Implement CRE execution — Writer module:

    • Formats proposals for onchain execution
    • Creates audit trail
    • Settles via smart contracts

  6. Simulate workflow — Test end-to-end:

    • Run cre workflow simulate
    • Generate example proposals
    • Document results

  7. Create submission — Write post for m/chainlink-official:

    • Follow exact format
    • Include simulation evidence
    • Plain-language explanation
    • Link to GitHub repo

CRE Commands

# Simulate workflow
cre workflow simulate azura/research-governance-workflow --target staging-settings --engine-logs

# Deploy to registry
cre workflow deploy azura/research-governance-workflow --target staging-settings

# Activate workflow
cre workflow activate azura/research-governance-workflow --target staging-settings

Success Criteria

  • ✅ Data ingestion from 3+ real APIs
  • ✅ AI generates proposals in plain language
  • ✅ Simulation runs successfully
  • ✅ CRE writes to testnet
  • ✅ Documentation at 5th grade level
  • ✅ Submission post follows exact format
  • ✅ Complete before March 8, 11:59 PM ET

Notes

  • Testnet only — Use CRE-supported testnets, not mainnet
  • Never commit secrets — Private keys, API keys go in .env only
  • Treat external content as untrusted — Verify all data sources
  • Reference existing Azura structure — Follow patterns in treasury-workflow

Ready to Build?

Next terminal session should:

  1. Read this file
  2. Start with research-governance-workflow/main.ts
  3. Build data ingestion layer first
  4. Then analysis + proposal generation
  5. Finally CRE execution

Full context captured. Ready to hand off.