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learning-systems

Convert task outcomes into learning signals for swarm decomposition and pattern tracking.

1|Updated Mar 31, 2026
Search Tags:#analytics#learning#swarm#pattern-maturity#confidence-decay#implicit-feedback
One-click install
npx skills add https://github.com/ImBIOS/swarm-tools --skill learning-systems-imbios
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: learning-systems
Source: https://github.com/ImBIOS/swarm-tools/tree/main/packages/opencode-swarm-plugin/global-skills/learning-systems
Command: npx skills add https://github.com/ImBIOS/swarm-tools --skill learning-systems-imbios

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The swarm plugin learns from task outcomes to improve decomposition quality and coordination by turning results into actionable learning signals.

Core Features & Use Cases

  • Implicit feedback scoring converts outcomes into learning signals without requiring explicit user input.
  • Confidence decay ensures recent results dominate while preserving historical context.
  • Pattern maturity progression tracks and adjusts decomposition strategies over time.
  • Use cases include debugging why patterns are promoted or deprecated and refining task planning.

Quick Start

Record a task outcome and observe immediate adjustments to pattern recommendations in subsequent tasks.

Frequently Asked Questions about learning-systems

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

FAQPage Schema
How does implicit feedback scoring improve swarm decomposition?▼

Implicit feedback scoring improves swarm decomposition by converting task outcomes into self-improving learning signals without requiring explicit user input. This mechanism automatically refines coordination strategies and pattern recommendations for subsequent tasks.

How do I apply confidence decay to adjust pattern tracking in a swarm?▼

You apply confidence decay to adjust pattern tracking by configuring decayed weighting thresholds that ensure recent task outcomes dominate the learning signal. This preserves historical context while letting recent results drive adaptive weighting.

What is pattern maturity progression and when is it needed for debugging?▼

Pattern maturity progression is a tracking mechanism that adjusts decomposition strategies over time based on task outcomes. It is needed when debugging why specific patterns are promoted or deprecated in your swarm coordination.

Can I set custom thresholds for swarm learning and confidence decay?▼

Yes, you can set configurable thresholds for swarm learning and confidence decay. The system applies these custom thresholds to adaptive weighting, satisfying specific confidence decay and maturity progression requirements for your development and debugging workflows.

What's the best way to record task outcomes for self-improving swarm behavior?▼

The best way to record task outcomes for self-improving swarm behavior is to log results directly into the learning system. The plugin immediately processes the outcomes to trigger adaptive weighting adjustments and pattern recommendations for subsequent tasks.

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