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npx skills add https://github.com/zby/llm-do --skill rethink-zby
Please help me install this Agent Skill. Skill: rethink Source: https://github.com/zby/llm-do/tree/main/arscontexta/skills/arscontexta-rethink Command: npx skills add https://github.com/zby/llm-do --skill rethink-zby
This Skill addresses the ossification of knowledge systems by systematically challenging assumptions against accumulated evidence, preventing calcification and ensuring continuous improvement.
/rethink can analyze these interactions, identify the underlying assumption causing the friction, and propose a change to the system's core logic or documentation.Run the rethink skill to analyze all pending observations and tensions for patterns and proposals.
High-intent search queries and answers about installing and using this skill.
To challenge system assumptions against accumulated evidence, you triage observations and tensions to detect behavioral drift, classify them into actionable categories, and generate proposals for system evolution. This requires a structured evidence-gathering mechanism and a human approval gate for all changes.
Evidence triage prevents knowledge system ossification by classifying accumulated observations and tensions into actionable categories like Promote, Implement, Methodology, Archive, or Keep Pending. This continuous validation ensures the system adapts rather than calcifying around outdated assumptions.
To detect methodology drift in AI agents, you analyze accumulated interaction tensions and observations to identify discrepancies between expected methodology and actual behavior. Pattern analysis then reveals recurring themes and contradictions within the system friction to generate actionable proposals.
Yes, a human approval gate is explicitly required for all proposed changes generated by the system evolution process. This ensures that evidence-backed proposals for modifying core logic or documentation are validated before implementation.
No, assumption testing for self-improving AI agents requires a structured evidence-gathering mechanism to function. The system relies on accumulated evidence from user interactions to identify friction points and generate evidence-backed proposals for core logic adaptation.
loriensleafsTraverse the Brain knowledge graph to surface notes, observations, and relations.
GiorgioRicciardielloDetect system drift and generate evolution proposals from configuration and tension notes.
niveshdandyanOrchestrate parallel agent swarms with per-agent handoffs and shared knowledge stores.
arolariuTriages observations and tensions to generate evidence-based improvement proposals.