Analyze episode data to refine instruction patterns with human approval.
npx skills add https://github.com/yiwei79/azoth --skill self-improve-yiwei79
Please help me install this Agent Skill. Skill: self-improve Source: https://github.com/yiwei79/azoth/tree/main/.opencode/skills/self-improve Command: npx skills add https://github.com/yiwei79/azoth --skill self-improve-yiwei79
Systematic self-improvement through reflexion, evidence gathering, and instruction refinement to help Azoth become more capable over time.
Provide an L2 refinement proposal and trigger the auto-pipeline using the instruction-refinement knowledge path after human approval.
High-intent search queries and answers about installing and using this skill.
Continuous self-improvement for agents is implemented through reflexion loops and evidence-based instruction refinement. The system analyzes episode data to identify patterns, generates concrete improvement proposals, and requires human approval before deploying changes.
Evidence-based instruction refinement is a governance mechanism that collects patterns from multiple agent episodes to propose concrete instruction changes. It logs all proposed refinements and gates them with human approval before deployment to ensure safe adaptation.
Reflexion workflows improve task execution through an in-session L1 loop where the agent self-evaluates and adapts its actions. For deeper improvements, an L2 workflow analyzes historical episode data to propose evidence-backed instruction variants.
Yes, you can automate instruction refinement pipelines after human approval. Once an L2 refinement proposal is reviewed and approved, the system supports auto-pipeline composition using the designated instruction-refinement knowledge path.
Agent self-improvement requires manual review for L2 changes. The governance system enforces logging and gates all evidence-based instruction refinements with human approval before any modifications are applied to the agent.
The best way to analyze agent episode data is using a systematic reflexion workflow that identifies recurring instruction patterns. It gathers evidence across multiple sessions to generate concrete refinement proposals rather than isolated fixes.
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