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meta-learn

Analyze telemetry patterns to generate YAML implementation plans for plugin improvements.

2|3|Updated Oct 20, 2025
Search Tags:#automation#improvement#meta-learning#telemetry#ROI#compound_history
One-click install
npx skills add https://github.com/psd401/psd-claude-coding-system --skill meta-learn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: meta-learn
Source: https://github.com/psd401/psd-claude-coding-system/tree/main/plugins/psd-claude-coding-system/skills/meta-learn
Command: npx skills add https://github.com/psd401/psd-claude-coding-system --skill meta-learn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The meta-learning skill converts raw telemetry and historical data into actionable improvement opportunities, accelerating continuous optimization of development workflows.

Core Features & Use Cases

  • ROI-driven suggestion generation: uses telemetry and compound_history to prioritize improvements.
  • Auto-implementation planning: produces structured plans for implementation and validation.
  • Historical precedent referencing: cites past outcomes to guide decisions.

Quick Start

Use /meta-analyze to identify patterns and /meta-learn to generate improvements from those patterns.

Frequently Asked Questions about meta-learn

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

FAQPage Schema
How do I use telemetry data to identify workflow improvements?▼

Telemetry analysis converts raw usage patterns into prioritized improvement opportunities by examining historical outcomes and ROI impact. Use /meta-analyze to identify patterns in your telemetry data, then /meta-learn to generate actionable suggestions ranked by potential return on investment for your development workflow.

What's the best way to generate implementation plans from telemetry patterns?▼

Meta-learn generates structured YAML implementation plans by analyzing telemetry and compound_history data to produce executable steps. Plans are organized into quick wins, medium-term improvements, and experimental ideas, each with validation steps and ROI estimates based on historical precedent.

Can I automate improvement suggestions based on plugin development history?▼

Yes. The Skill references compound_history.json to cite past outcomes and guide decisions, enabling automated generation of improvement suggestions tailored to your plugin development workflow without manual precedent review.

What data do I need to run telemetry-driven optimization?▼

You need access to meta/telemetry.json containing usage patterns and compound_history.json containing historical outcomes. These inputs enable ROI estimation and historical precedent referencing to generate high-confidence improvement recommendations.

How does ROI estimation work when prioritizing improvements?▼

ROI-driven suggestion generation uses telemetry patterns and compound_history outcomes to estimate impact and prioritize improvements. The Skill weighs past results against current patterns to rank quick wins highest, followed by medium-term and experimental improvements.

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