functional-area-resolver

Name: functional-area-resolver
Availability: InStock
Author: starlink-awaken

Compress large agent routing files into functional-area dispatchers.

Updated Jun 10, 2026

Search Tags:#routing #agents #compression #context-optimization #dispatching #llm-architecture

Ready to run

npx skills add https://github.com/starlink-awaken/omostation-gbrain --skill functional-area-resolver-starlink-awaken

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: functional-area-resolver
Source: https://github.com/starlink-awaken/omostation-gbrain/tree/main/skills/functional-area-resolver
Command: npx skills add https://github.com/starlink-awaken/omostation-gbrain --skill functional-area-resolver-starlink-awaken

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill addresses the issue of bloated routing files (AGENTS.md or RESOLVER.md) that consume excessive context budget by providing a systematic way to compress them into functional-area dispatchers.

Core Features & Use Cases

Context Optimization: Reduces routing file size by up to 50% while maintaining high routing accuracy.
Hierarchical Dispatch: Implements a two-layer routing pattern where the main file routes to a functional area, which then handles specific sub-skills.
Use Case: If your agent's routing table has grown to over 200 rows and is consuming 25KB of context, this skill helps you reorganize it into 10-15 functional areas to reclaim space for actual task execution.

Quick Start

Use the functional area resolver to compress my agents.md file into a more efficient dispatcher format.

Frequently Asked Questions about functional-area-resolver

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compress a large agent routing file that exceeds my LLM context budget?▼

To compress a large agent routing file, group individual skill entries into functional-area dispatchers. This hierarchical routing pattern reduces routing table size by up to 50% while maintaining high dispatching accuracy for LLM-based architectures.

What is a functional-area dispatcher in LLM agent architectures?▼

A functional-area dispatcher is a two-layer routing pattern where the main file routes tasks to a functional area, which then handles specific sub-skills. It optimizes context budget by replacing long lists of individual skills with grouped dispatchers.

When do I need to reorganize my agent routing table?▼

You need to reorganize your agent routing table when it exceeds context budget limits, such as growing beyond 200 rows and consuming 25KB of context. Reorganizing into 10-15 functional areas reclaims space for actual task execution.

How do I compress an AGENTS.md file into functional areas?▼

Compress an AGENTS.md file by applying the dispatcher-aware prompt pattern to group entries into functional areas. Structural verification is performed via the provided harness to ensure the compressed routing file maintains proper dispatching logic.

Does the functional-area resolver work with routing tables under 200 rows?▼

The resolver targets LLM-based agent architectures where routing tables exceed context budget limits. While it can process smaller tables, the context optimization benefits are specifically designed for large files consuming excessive context space.

What are the limitations of compressing routing tables into functional areas?▼

Compressing routing tables into functional areas requires adherence to the dispatcher-aware prompt pattern and structural verification via the provided harness. The two-layer routing approach may introduce slight routing indirection compared to flat routing tables.