CoroidDark Factory

Progressive Skill System

Progressive Skill System

AI agents dynamically discover and load specialized expertise as needed, expanding their capabilities on-demand while keeping context focused and efficient.

What is the Progressive Skill System?

Our AI agents don't just have access to tools - they have access to a hierarchical library of specialized expertise called Skills. Unlike traditional approaches that load all tool definitions upfront, our Progressive Skill System uses intelligent discovery and lazy-loading to minimize context usage while maximizing capability.

How It Works

Agents start with a lightweight registry and load full skill manifests only when needed.

Domain Expertise

Each skill contains battle-tested patterns, anti-patterns, and framework-specific knowledge.

Context Efficient

Reduces token usage by 60-70% while maintaining or improving task success rates.

Progressive Disclosure Levels

Skills are loaded in four progressive levels, starting with minimal metadata and expanding to full executable content.

In context now

100

tokens

Loading everything up front4,100

Only the registry is always resident. Everything below it arrives when a skill is activated, and reference material and scripts only when the work actually calls for them.

Example Workflow

See how AI agents use the Progressive Skill System in practice:

  1. 01

    Task Received

    Agent receives task: 'Add PostgreSQL database to project'

  2. 02

    Skill Discovery

    Agent lists available skills and discovers 'backend/database/postgresql'

  3. 03

    Skill Activation

    Agent loads PostgreSQL skill manifest (~1500 tokens)

  4. 04

    Expertise Applied

    Agent plans with PostgreSQL-specific patterns and best practices

  5. 05

    Script Execution

    Agent runs analyse-schema.py to optimise the database design

  6. 06

    Task Complete

    Production-ready database implementation with expert-level quality

Token reduction
60–70%
Registry load time
Under 100 ms
Cache hit rate
90%+

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