functional-area-resolver

Compress agent routing tables into functional-area dispatchers with regression testing.

45|11|Updated Mar 17, 2026

Search Tags:#routing #agents #compression #context-optimization #dispatcher #llm-routing

Ready to run

npx skills add https://github.com/beyonai/ByClaw --skill functional-area-resolver-beyonai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: functional-area-resolver
Source: https://github.com/beyonai/ByClaw/tree/main/middleware/openclaw/skills/gbrain/references/functional-area-resolver
Command: npx skills add https://github.com/beyonai/ByClaw --skill functional-area-resolver-beyonai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill addresses the context bloat caused by large routing files (RESOLVER.md or AGENTS.md) in agent frameworks, which consume valuable token budget by listing hundreds of individual skills.

Core Features & Use Cases

Functional Dispatching: Collapses granular skill rows into functional area entries, reducing file size by up to 50% while maintaining routing accuracy.
Two-Layer Routing: Implements a hierarchical dispatch pattern where the routing file points to an area, and the area skill routes to the specific sub-skill.
Use Case: If your agent's routing table has grown to 200+ rows and is consuming 25KB of context, use this skill to reorganize it into a compact, high-performance dispatcher format.

Quick Start

Use the functional area resolver to compress my agents.md file into a functional area 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 table to save context tokens?▼

Compress large agent routing tables by converting granular skill-per-row entries into functional area dispatchers. This hierarchical routing pattern reduces file size by up to 50% while maintaining routing accuracy for complex agent systems.

Why does my AGENTS.md file consume so much context token budget?▼

Agent routing files cause context bloat by listing hundreds of individual skills as separate rows. When a routing table grows to 200+ rows, it consumes significant token budget that can be reclaimed through functional area compression.

What is functional area dispatching in LLM agent systems?▼

Functional area dispatching is a two-layer routing pattern where the main routing file points to a functional area, and the area skill routes to the specific sub-skill. This collapses granular skill rows into compact area entries.

How many rows should my routing file have before I need to reorganize it into a dispatcher format?▼

Reorganize routing files into a dispatcher format when your agent routing table grows to 200 or more rows. At this scale, files typically consume around 25KB of context, making functional area compression necessary for optimal context management.

Does compressing routing tables affect the accuracy of agent routing?▼

Compressing routing tables into functional area dispatchers maintains routing accuracy. The skill validates routing integrity through automated structural and LLM-based regression testing to ensure the compressed format correctly dispatches to sub-skills.

Can I use functional area routing with my existing agent framework?▼

Functional area routing works with agent frameworks that use RESOLVER.md or AGENTS.md routing files. It reorganizes these files into a two-layer hierarchical dispatch pattern without requiring changes to the underlying framework structure.