modular-skills

Solid

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

AI & Automation 294 stars 26 forks Updated today MIT

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Quality Score: 95/100

Stars 20%
82
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

## 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 to keep the architecture cohesive. Clear boundaries and well-defined interfaces prevent changes in one module from breaking others. This follows Anthropic's Agent Skills best practi...

Details

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

Integrates with

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