Extract patterns, heuristics, and calibration data from course development artifacts.
npx skills add https://github.com/Andamio-Platform/coach --skill compound-andamio-platform
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
Extracts patterns, heuristics, and calibration data from course development artifacts to make future runs smarter and faster, reducing rework and accelerating iteration cycles.
Run the /compound command to start an interactive session and select a phase to extract and roll up knowledge.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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.
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