modular-skills

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Build composable skill modules with hub-and-spoke loading. Use when token budget is tight.

AI & Automation 323 stars 29 forks Updated today MIT

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Skill Content

## When NOT To Use - A single-file skill already inside its token budget (use `abstract:skill-authoring`) - The lazy-loading contract itself (use `leyline:progressive-loading`) ## Table of Contents - [Overview](#overview) - [Quick Start](#quick-start) - [Workflow and Tasks](#workflow-and-tasks) - [Quality Checks](#quality-checks) - [Resources](#resources) # Modular Skills Design ## Overview This framework breaks complex skills into focused modules to keep token usage predictable and avoid monolithic files. We use progressive disclosure: starting with essentials and loading deeper technical details via `@include` or `Load:` statements only when needed. This approach prevents hitting context limits during long-running tasks. Modular design keeps file sizes within recommended limits, typically under 150 lines. Shallow dependencies and clear boundaries simplify testing and maintenance. The hub-and-spoke model allows the project to grow without bloating primary skill files, making focused modules easier to verify in isolation and faster to parse. ### Core Components Three tools support modular skill development: - `skill-analyzer`: Checks complexity and suggests where to split code. - `token-estimator`: Forecasts usage and suggests optimizations. - `module_validator`: Verifies that structure complies with project standards. ### Design Principles We design skills around single responsibility and loose coupling. Each module focuses on one task, minimizing dependencies ...

Details

Author
athola
Repository
athola/claude-night-market
Created
8 months ago
Last Updated
today
Language
Python
License
MIT

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