What is the primary input for the Module Planner skill?

The skill requires a concept graph file located at outline/concept_graph.yml, which defines the relationships and dependencies between different topics.

How does the skill determine the order of the modules?

It uses a topological sorting algorithm to analyze the concept graph, ensuring that all prerequisite concepts are covered in earlier modules before dependent topics are introduced.

Can I customize the number of modules generated?

Yes, if the plan is too granular, the skill is designed to merge adjacent modules with shared prerequisites to target a standard 5–12 module range.

What happens if my concept graph isn't ready yet?

The skill will prompt you to run the 'concept-graph' skill first, as it requires the dependency data to generate an accurate and logical module plan.

Tutorial Module Planner

Name: Tutorial Module Planner
Author: WILLOSCAR

byWILLOSCAR

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Learning & Documentation

Transforms conceptual dependency graphs into structured, teachable tutorial sequences with measurable objectives and concrete learner outputs.

About

The Module Planner skill is a specialized tool for educators and technical writers that automates the transition from raw concept mapping to structured curriculum design. By analyzing a Directed Acyclic Graph (DAG) of concepts, it performs a topological sort to ensure a logical learning progression where prerequisites are always addressed before advanced topics. It clusters related concepts into coherent modules, generates verifiable learning objectives, and defines specific artifacts that learners must produce, ensuring a high-quality, evidence-first educational experience.

Key Features

Smart clustering of concepts into manageable, coherent learning modules
Integration of 'running example' steps to maintain narrative consistency
Generation of measurable, verifiable learning objectives for every module
88 GitHub stars
Standardized YAML output including module IDs, titles, and concept mapping
Automatic topological sorting of concept dependencies for logical sequencing

Use Cases

Designing a curriculum for internal engineering onboarding based on required skills
Converting a complex technical knowledge base into a step-by-step programming tutorial
Structuring documentation into a logical learning path for new software users

About

Key Features

Smart clustering of concepts into manageable, coherent learning modules
Integration of 'running example' steps to maintain narrative consistency
Generation of measurable, verifiable learning objectives for every module
88 GitHub stars
Standardized YAML output including module IDs, titles, and concept mapping
Automatic topological sorting of concept dependencies for logical sequencing

Use Cases

Designing a curriculum for internal engineering onboarding based on required skills
Converting a complex technical knowledge base into a step-by-step programming tutorial
Structuring documentation into a logical learning path for new software users