Review, promote, reject, or edit LLM-inferred behavioral hypotheses.
npx skills add https://github.com/emmahyde/memesis --skill hypotheses
Please help me install this Agent Skill. Skill: hypotheses Source: https://github.com/emmahyde/memesis/tree/main/skills/hypotheses Command: npx skills add https://github.com/emmahyde/memesis --skill hypotheses
This Skill enables users to review, promote, reject, or edit LLM-inferred behavioral hypotheses, streamlining hypothesis management.
Use the hypotheses skill to list all current hypotheses and promote a specific hypothesis by its ID.
High-intent search queries and answers about installing and using this skill.
Managing LLM-inferred behavioral hypotheses involves reviewing, promoting, rejecting, or editing them to streamline the hypothesis lifecycle. This maintains a clean hypothesis space by advancing validated items to stable memory and dismissing incorrect ones.
The best way to promote validated behavioral patterns into stable memory is to advance hypotheses with sufficient evidence by their specific ID. This updates the model's state and ensures validated patterns are available for future retrieval.
Yes, managing LLM behavioral hypotheses requires access to memory and reflection modules. These modules are necessary for the system to execute decision-making and accurately perform state updates during hypothesis lifecycle operations.
Yes, you can edit existing behavioral hypotheses to modify and correct invalid or outdated information. This editing function allows you to update hypothesis data directly, ensuring accuracy before promoting or rejecting the entry.
You can list all pending behavioral hypotheses to display them alongside their corresponding evidence and session data. This review process allows you to evaluate which behavioral patterns have sufficient support for promotion to stable memory.
You should reject LLM-inferred behavioral hypotheses when they are invalid, incorrect, or spurious. Rejecting these entries archives or dismisses them from the active queue, which maintains a clean and accurate hypothesis space.
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