Convert execution traces into actionable knowledge to improve agent performance.
npx skills add https://github.com/gotar/opencode-config --skill self-learning
Please help me install this Agent Skill. Skill: self-learning Source: https://github.com/gotar/opencode-config/tree/main/skills/self-learning Command: npx skills add https://github.com/gotar/opencode-config --skill self-learning
Self-learning enables agents to convert execution traces into actionable knowledge to continuously improve performance.
Capture a trace after completing a task, run analyze_traces.py to generate insights, and apply adaptations with apply_adaptation.py to validate improvements.
High-intent search queries and answers about installing and using this skill.
To turn execution traces into actionable knowledge, you capture task execution histories into a learning workspace and run analysis scripts to extract insights that improve multi-agent workflow performance.
Agents learn from recurring failures by capturing execution traces after complex tasks, extracting failure patterns, and applying session-local or system-wide adaptations to prevent similar issues.
The best way to automate self-learning adaptation is to record execution traces, generate insights with analysis scripts, and apply behavioral changes with safeguarding rules across orchestrator, worker, or reviewer tasks.
Yes, you need to store execution traces in the .tmp/learning directory, which serves as the learning workspace for capturing histories before running scripts to extract insights and apply adaptations.
Yes, you can apply agent-specific adaptations independently, as the self-learning mechanism supports session-local, agent-specific, and system-wide changes with safeguarding rules to isolate behavioral updates.
Limitations include relying on execution trace quality and requiring manual validation of extracted insights, as adaptations are constrained by safeguarding rules to prevent unintended system-wide behavioral changes.
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