Core Modules

🧩 Core Modules

The PolinaOS architecture is modular and extensible. You can adopt a single module or deploy the full loop — depending on your project’s needs. Each module is powered by specialized agents that collaborate to automate data collection, mission generation, impact assessment, and reward distribution.

1. 📊 Project Intelligence Layer

Purpose: Collect and analyze project context, trends, goals, and market positioning.

Data sources include on-chain events, token metadata, and recent Twitter activity.
The Analysis Agent is responsible for synthesizing this information into a real-time project profile — understanding your token’s “vibe,” ideal shilling narratives, and evolving market momentum.
This layer creates the intelligence foundation for task generation and contributor evaluation.

2. 🎯 AI Mission Engine

Purpose: Automatically generate community tasks aligned with project objectives and current market signals.

The Creator Agent leverages the intelligence layer to draft weekly quests: tweet prompts, meme formats, engagement drives, etc.
Tasks are optimized for viral growth, meaningful action, and verifiable outcomes.
An Admin Interface allows manual editing, review, or rejection before publishing to the public dashboard.
Tasks support rich metadata including deadlines, hashtags, KPIs, and expected behavior.

3. 🔍 Community Data Scanner

Purpose: Monitor, filter, and archive all community contributions across Twitter and the blockchain.

The Data Agent operates a Puppeteer-based scanning infrastructure (via ctScreener) to track:
- Who is tweeting (screen_name / wallet)
- What’s being shared (contract mentions, hashtags, memes)
- When it happened (timestamp accuracy)
It indexes everything via job ID, tweet ID, and token keyword.
The agent works in tandem with by-id, retweet, and user APIs to build a verifiable activity graph for each contributor.

4. 🧮 Task Validation & Impact Scoring

Purpose: Evaluate contribution quality, detect sybil behavior, and assign reputational scores.

The Evaluator Agent cross-references task data with scanner output.
It uses content similarity checks, engagement metrics, and behavioral heuristics to:
- Eliminate fake or duplicated submissions
- Flag low-effort engagement (e.g., bot retweets)
- Score based on reach, quality, timeliness, and originality
Outputs include individual reputation scores and ranked contributor lists — per task or weekly.

5. 🎁 Reward Distribution Layer

Purpose: Automatically distribute tokens or points to contributors based on impact.

The Reward Agent reads evaluation output and applies allocation rules.
- Example: “Top 50 wallets by weekly score get 2% of allocation.”
It generates Merkle claim proofs and supports Solana-based claim contracts, with EVM support planned.
Everything is auditable and trustless — no need for manual approval or centralized payout systems.

🖥️ Admin & Community Dashboard

PolinaOS comes with a built-in Dashboard for both team members and contributors:

Admin Panel: Review missions, scan logs, leaderboard data, and token balances.
Mission Board: Public-facing quest interface for contributors (optionally token-gated).
Analytics View: Insight into performance over time, task completion rates, and community trends.

Together, these modules — orchestrated by dedicated AI agents — allow any project to bootstrap and scale a contributor ecosystem with little to no manual oversight. Each component can run independently or as part of a unified mission-to-reward loop.