compound

Extract patterns, heuristics, and calibration data from course development artifacts.

5|Updated Mar 19, 2026

Search Tags:#education #feedback #patterns #calibration #knowledge #compound #course-development

Ready to run

npx skills add https://github.com/Andamio-Platform/coach --skill compound-andamio-platform

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: compound
Source: https://github.com/Andamio-Platform/coach/tree/main/skills/compound
Command: npx skills add https://github.com/Andamio-Platform/coach --skill compound-andamio-platform

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Extracts patterns, heuristics, and calibration data from course development artifacts to make future runs smarter and faster, reducing rework and accelerating iteration cycles.

Core Features & Use Cases

Pattern extraction: identifies recurring SLT improvements, quality issues, and verb effectiveness to feed back into drafting and assessment.
Calibration and readiness: tracks self-assessed readiness versus actual outcomes to calibrate future planning.
Knowledge feedback loops: updates /draft-slts, /assess-slts, /self-assess-readiness, and /classify-lesson-types to improve subsequent runs.

Quick Start

Run the /compound command to start an interactive session and select a phase to extract and roll up knowledge.

Frequently Asked Questions about compound

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

FAQPage Schema

How do I capture knowledge from course development artifacts to improve future runs?▼

To capture knowledge from course development artifacts, you extract patterns, heuristics, and calibration data from past runs. This feedback loop updates knowledge bases to make subsequent course iterations smarter and faster.

What are course development feedback loops and how do they work?▼

Course development feedback loops work by capturing past course data and applying it to future runs. They update drafting, assessment, readiness, and classification phases to ensure continuous improvement and reduce rework.

How do I calibrate self-assessed readiness versus actual outcomes for course planning?▼

You calibrate self-assessed readiness versus actual outcomes by tracking readiness data from previous course runs. This calibration data is then applied to enhance future planning and predict learner success more accurately.

What is the best way to extract recurring patterns and quality issues from course materials?▼

The best way to extract recurring patterns and quality issues is to analyze course development artifacts for verb effectiveness and SLT improvements. These extracted patterns feed directly back into drafting and assessment phases.

Can I use extracted course knowledge to update drafting and assessment phases automatically?▼

Yes, you can use extracted course knowledge to update drafting and assessment phases. The process rolls up identified heuristics and calibration data to directly enhance the knowledge bases used by these phases.

When do I need a knowledge feedback process for course development?▼

You need a knowledge feedback process for course development when you want to accelerate iteration cycles and reduce rework. It is essential for applying past learnings to subsequent runs across drafting and classification phases.