Below is a detailed description of a Koii task designed to accomplish the distributed domain discovery, verification, and reward distribution objective. This task leverages multi-agent cooperation, LLM integration, cryptographically signed contributions, and automated auditing—all inspired by the architectural style seen in the builder-247 repository. The task is structured to work seamlessly within the Koii network, where autonomous nodes perform rapid scanning, verification, and reputation-based reward assignment.

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Task Name: Distributed AI Domain Discovery and Reward Aggregation

Overview

This Koii task is built to convert a marketing or product prompt into actionable domain name candidates by harnessing multiple large language models (LLMs) and a network of verifiable nodes. When a user submits a prompt (or dictation), the task will trigger an LLM agent to generate a first wave of associated keywords. These keywords are immediately forwarded to the network where each node validates, timestamps, and cryptographically signs its contribution. Following keyword validation, the system proceeds to generate domain name candidates that are then concurrently checked for availability and pricing across multiple registries (e.g., Namecheap, GoDaddy, Domainr). Rewards are distributed based on speed, accuracy, and quality of each node's contribution in both keyword identification and domain selection phases. This entire process is orchestrated in a distributed, fault-tolerant, and transparent manner on the Koii network.

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Objectives

• Automated Keyword Generation:
  Receive a product or marketing prompt and use an LLM agent to extract a comprehensive list of associated keywords (verbs, adjectives, nouns).

• Concurrent Domain Discovery:
  Convert the verified keywords into domain name candidates. Each candidate is verified for registration status, accurate pricing (with built-in auditing against stale data), and tied to metadata such as response time and node identity.

• Decentralized Verification and Reward Allocation:
  Leverage multiple nodes that not only scan domain registrar APIs and maintain local DNS caches but also provide cryptographically signed proofs of data provision. Nodes are rewarded based on response speed and the subsequent selection of correct keywords or domains by the user.

• User-Centric Selection and Treasury Distribution:
  Present the user with a ranked list of domain suggestions (defaulting to lowest price first). When a purchase occurs, the transaction (including a 20% fee) is processed, and rewards are distributed to contributing nodes according to a pre-defined treasury model.

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Task Components and Workflow

1. Input Reception and LLM Interface:

   • User Input:
     The task begins with a user submitting a marketing prompt (in text or dictation form).
   • LLM Invocation:
     An LLM agent (via an integrated API such as Gemini, DeepSeq, or similar) processes the prompt and generates a list of associated keywords.
     • Example: Given a prompt “Eco-Friendly Smart Home Devices,” the LLM extracts keywords like “eco-friendly,” “smart,” “home,” “devices,” etc.
   • Data Packaging:
     The output (keywords and initial metadata) is timestamped and signed, then broadcast into the network for concurrent processing.

2. Distributed Keyword Verification:

   • Node Validation:
     Multiple nodes independently receive the keywords. Each node:
     • Verifies the semantic relevance.
     • Generates its own additional suggestions if applicable.
     • Records the timestamp of processing.
     • Signs its submission cryptographically.
   • Awarding Early Contributions:
     Nodes that process and validate the keywords fastest are noted in a ledger, establishing a basis for later reward distribution.

3. Domain Name Generation from Keywords:

   • Reinforced LLM Query:
     Based on the verified keywords, a second wave LLM agent generates potential domain names. Nodes can also use pre-configured algorithms to combine, truncate, or creatively alter keywords into plausible domain names.
   • Parallel Verification:
     Similar to the keyword phase, domain name candidates are distributed to nodes for validation. Nodes perform:
     • Domain availability queries via registrar APIs.
     • DNS lookups (locally cached where available) to confirm real-time status.
     • Price verification by interfacing with Namecheap, GoDaddy, and other services.
   • Tagging and Timestamping:
     Each candidate is annotated with response timestamps, computed prices, and a verified status flag.

4. Final Aggregation, Ranking, and User Presentation:

   • Node Aggregation and Ranking:
     A consensus of participating nodes aggregates the verified domain list. Candidates are then ranked—by default in ascending order of price—with additional filters (alphabetical, relevance, etc.) available.
   • User Selection Interface:
     The task returns the ranked list to the user interface. The user can review and select a domain.
   • Final Audit:
     If the user opts for a domain, an audit is triggered to confirm that the domain’s data (availability and price) is valid up to the moment of purchase. Any discrepancies are logged and node contributions are adjusted accordingly.

5. Reward Distribution and Treasury Integration:

   • Transaction and Tip Processing:
     Upon purchase, the domain registration fee is increased by 20% to cover service and network costs.
   • Treasury Pool Distribution:
     The reward mechanism distributes the added fee among the nodes:
     • Early keyword generators receive a portion of the reward.
     • Nodes that first identified and validated the correct domain are similarly credited.
     • The treasury pool is maintained on-chain with all transactions cryptographically signed and auditable.
   • Performance Auditing:
     Post-transaction audits are performed to ensure data accuracy and provide metrics for node performance, affecting future task reward shares.

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Technical Considerations and Best Practices

• API Rate Management:
  Each node must respect the rate limits of registrar APIs (typically around 60 queries per minute per endpoint) by implementing asynchronous calls and local caching mechanisms.

• Scalability and Redundancy:
  The distributed design ensures that if some nodes fail to respond in time, others cover the task, maintaining high throughput and rapid user experience.

• Security and Traceability:
  All contributions are cryptographically signed and recorded in an immutable ledger, ensuring both trust among nodes and accountability in reward distribution.

• Modular Integration:
  The task is designed to plug into the Koii network in a modular fashion, similar to the builder-247 model. This allows additional LLM modules or new registrar integrations to be added with minimal disruption.

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Conclusion

This Koii task description outlines a robust, distributed system leveraging LLM-powered keyword and domain name generation, multi-agent verification, and a reward distribution mechanism based on node contribution. By integrating rapid domain verification with transparent treasury-based incentives, this system provides users with a fast and reliable way to identify and purchase the ideal domain for their project while rewarding the network of nodes that power the process. The approach, inspired by the architecture in the builder-247 repository, ensures both scalability and security within the decentralized Koii network.