self-learning

Convert execution traces into actionable knowledge to improve agent performance.

26|1|Updated Jan 6, 2026

Search Tags:#automation #multi-agent #insights #self-learning #adaptation #execution-trace

Ready to run

npx skills add https://github.com/gotar/opencode-config --skill self-learning

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Self-learning enables agents to convert execution traces into actionable knowledge to continuously improve performance.

Core Features & Use Cases

Trace capture and storage: automate recording of task execution histories into the learning workspace.
Insight extraction and knowledge-building: transform traces into actionable patterns and a persistent knowledge base.
Adaptive behavior: apply session-local, agent-specific, and system-wide adaptations to improve future tasks.

Quick Start

Capture a trace after completing a task, run analyze_traces.py to generate insights, and apply adaptations with apply_adaptation.py to validate improvements.

Frequently Asked Questions about self-learning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I turn execution traces into actionable knowledge for multi-agent workflows?▼

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.

How can agents learn from recurring failures in complex tasks?▼

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.

What is the best way to automate self-learning adaptation for software agents?▼

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.

Do I need a specific directory structure to store execution traces for self-learning?▼

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.

Can I apply agent-specific adaptations without affecting the entire system?▼

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.

What are the limitations of using execution traces for multi-agent adaptation?▼

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.