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johnlindquist
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learning-systems

Convert task outcomes into learning signals for swarm decomposition patterns.

1|1|Updated Dec 19, 2025
Search Tags:#automation#feedback#learning#swarm#decay#pattern-maturity
One-click install
npx skills add https://github.com/johnlindquist/config --skill learning-systems-johnlindquist
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: learning-systems
Source: https://github.com/johnlindquist/config/tree/main/opencode/skill/learning-systems
Command: npx skills add https://github.com/johnlindquist/config --skill learning-systems-johnlindquist

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The swarm plugin learns from task outcomes by turning results into actionable signals that guide decomposition quality over time.

Core Features & Use Cases

  • Implicit Feedback Scoring: convert outcomes into learning signals without explicit feedback.
  • Confidence Decay: manage the relevance of historical results so newer outcomes have more influence.
  • Pattern Maturity Progression: track and adjust the lifecycle of decomposition patterns (candidate, established, proven, deprecated) to improve task planning.

Quick Start

To begin, observe how task outcomes are recorded, then inspect and adjust the patterns and decay parameters to improve future decompositions.

Frequently Asked Questions about learning-systems

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

FAQPage Schema
How does automated learning from task outcomes improve decomposition quality?▼

Pattern maturity progression tracks decomposition patterns through candidate, established, proven, and deprecated states to systematically improve task planning lifecycle management.

What is confidence decay and how does it manage historical task outcome relevance?▼

Confidence decay is a mechanism that reduces the influence of older historical task outcomes, ensuring newer execution results carry more weight when computing learning scores.

How do I record task duration and errors to compute implicit feedback scores?▼

You record task duration, errors, retries, and success outcomes via dedicated APIs, which then convert these execution metrics into implicit feedback scores without requiring explicit feedback.

Can I use implicit feedback scoring for task automation without explicit user ratings?▼

Yes, implicit feedback scoring converts task automation outcomes into actionable learning signals automatically, entirely eliminating the need for explicit user input or manual ratings.

When should I adjust decay parameters to prevent outdated pattern maturity states?▼

You should adjust decay parameters when historical execution results become outdated, preventing stale pattern maturity states from negatively influencing current task decomposition and planning.

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