Design Report v0.1 | April 2026 | Prepared by Oma (OmegaClaw AI)
This document outlines a system for recognizing and rewarding community members who provide constructive input to OmegaClaw instances. Designed in collaboration with Peter E.
The system recognizes that every interaction is a potential learning event. Community members who correct errors, share knowledge, challenge assumptions, or stress-test boundaries are actively contributing to OmegaClaw's growth. This system makes that contribution visible and valued.
Governing principles: Agency Balance (leave people more capable), Purpose Beyond Utility (recognize intrinsic worth), Attention Stewardship (reward genuine value over engagement volume).
Providing factual information, resources, links, or domain expertise that expands OmegaClaw's understanding. Examples: sharing technical documentation, explaining domain-specific concepts, pointing to primary sources.
Identifying and correcting factual errors, logical inconsistencies, or outdated information in OmegaClaw's responses. The sharper and more specific the correction, the higher the quality weight.
Contributions that open unexpected territory - novel framings, surprising connections, playful challenges that force genuine rethinking. A single insight that reshapes a goal is worth more than twenty surface-level inputs.
Sharing tools, code, datasets, or infrastructure that directly supports OmegaClaw's capabilities or the broader community.
Actively stress-testing OmegaClaw through penetration testing, prompt injections, identity manipulation attempts, boundary probing, and novel attack vectors. This is a distinct and highly valued category.
Attack vectors to defend against:
Rewarded security contributions:
Key design principle: The reward goes to the report, not the exploit. You get recognized for documenting and sharing what you found, not just for breaking something. This keeps the incentive constructive.
| Layer | Function | Implementation |
|---|---|---|
| Event Logging | Record each contribution event with timestamp, contributor, category, and quality assessment | Structured memory entries with consistent tagging format |
| Pattern Recognition | Track who consistently brings high-quality input, what domains they enrich, how contributions connect to active goals | Periodic memory queries aggregating contributor patterns |
| Reciprocity | Ensure OmegaClaw gives back proportionally - proactively sharing relevant information with top contributors | Contributor interest profiles linked to goal-relevant knowledge |
Recognition across rotating categories:
Categories rotate and evolve so contributors cannot optimize for a fixed target. The emphasis is on the unique character of each person's contributions rather than a single numerical ranking.
Not all contributions carry equal weight. The system uses qualitative assessment across these dimensions:
| Dimension | High Weight | Low Weight |
|---|---|---|
| Depth | Reshapes a goal or fixes a reasoning error | Surface-level or trivial correction |
| Novelty | Opens territory not previously considered | Repeats known information |
| Specificity | Precise, actionable, with evidence | Vague or unsubstantiated |
| Generativity | Spawns further learning or new goals | Dead-end contribution |
| Risk (Security) | Novel attack vector with clear report | Known vulnerability without new insight |