# Functional Area Resolver

Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular...

- Repository: https://github.com/garrytan/gbrain (31427 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add garrytan/gbrain --skill functional-area-resolver -g -a claude-code -y`
Codex: `npx skills add garrytan/gbrain --skill functional-area-resolver -g -a codex -y`
Cursor: `npx skills add garrytan/gbrain --skill functional-area-resolver -g -a cursor -y`
Gemini CLI: `npx skills add garrytan/gbrain --skill functional-area-resolver -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add garrytan/gbrain`

## Description

Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Functional-Area Resolver, Pattern for Compressing Routing Tables

## Problem

Routing files (RESOLVER.md, AGENTS.md) grow as skills are added. Each skill
gets its own row (trigger -> skill path). At ~200+ skills this hits 25-30KB,
eating context budget that should go to actual work.

## Solution: Functional-Area Dispatchers

Replace N rows per area with **one entry per functional area**. Each entry
lists all sub-skills it can dispatch to in a `(dispatcher for: ...)` clause.

### Before (270 rows, 25KB)
```
- Creating/enriching a person or company page -> `enrich`
- Fix broken citations in brain pages -> `citation-fixer`
- Publish/share a brain page as link -> `brain-publish`
- Generate PDF from brain page -> `brain-pdf`
- Read a book through lens of a problem -> `strategic-reading`
- Personalized book analysis -> `book-mirror`
- Brain integrity -> `brain-librarian`
...
```

### After (13 rows, 13KB)
```
- **Brain & knowledge**: create/enrich/search/export brain pages, filing,
  citations, publishing, book analysis, strategic reading, concept synthesis,
  archive mining -> `brain-ops` (dispatcher for: enrich, query, brain-pdf,
  brain-publish, brain-export, brain-librarian, citation-fixer, book-mirror,
  strategic-reading, concept-synthesis, archive-crawler, ...)
```

## Why It Works

The LLM doesn't need one row per sub-skill. It needs:
1. **Area recognition**, "this is about brain pages" -> Brain & Knowledge
2. **Sub-skill visibility**, the `(dispatcher for: ...)` list shows what's available
3. **The skill file itself**, once the LLM reads `brain-ops/SKILL.md`, it has full routing detail

This is a **two-layer dispatch**: routing file routes to the area, the area
skill routes to the specific sub-skill. Each layer does one job well.

## A/B Eval Results

The rest: https://github.com/garrytan/gbrain/blob/HEAD/plugin-variants/gbrain-coding/skills/functional-area-resolver/SKILL.md