functional-area-resolver

Compress agent routing tables into functional-area dispatcher entries with verified routing accuracy.

Updated Aug 17, 2026
One-click install
npx skills add https://github.com/AMC-JTC/gbrain-1 --skill functional-area-resolver-amc-jtc
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: functional-area-resolver
Source: https://github.com/AMC-JTC/gbrain-1/tree/main/plugin/skills/functional-area-resolver
Command: npx skills add https://github.com/AMC-JTC/gbrain-1 --skill functional-area-resolver-amc-jtc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Agent routing files like AGENTS.md and RESOLVER.md grow to 25-30KB as skills are added, consuming context budget that should go to actual work. This Skill converts hundreds of per-skill routing rows into a dozen functional-area dispatcher entries, cutting file size roughly in half while preserving routing accuracy. ## Core Features & Use Cases - Functional-Area Compression: Groups skills by domain (Brain & Knowledge, Email & Comms, Research, etc.) and replaces N rows per area with one dispatcher entry containing a (dispatcher for: ...) sub-skill list. - Mandatory Verification Gates: Runs gbrain routing-eval --json plus an LLM A/B harness against your edited file before committing, requiring ≥95% lenient routing accuracy. - Eval-Backed Pattern: Ships with A/B results across Opus, Sonnet, and Haiku showing the dispatcher pattern beats both the 270-row baseline and resolver-of-resolvers compression. - Use Case: Your AGENTS.md hit 30KB and context-health checks flag it red. Run this Skill to compress it into 13 functional-area entries, verify routing accuracy with the bundled harness, and commit only after both gates pass. ## Quick Start Ask the agent to compress your oversized AGENTS.md or RESOLVER.md routing file into functional-area dispatchers and verify routing accuracy before committing.

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 AGENTS.md routing file?

Group skills into 12-15 functional areas and replace per-skill rows with one entry per area containing a `(dispatcher for: ...)` sub-skill list. Then run `gbrain routing-eval --json` and the LLM A/B harness to verify routing accuracy stays above 95% before committing.

What is the functional area dispatcher pattern for agent routing?

It is a two-layer dispatch pattern where the routing file routes intents to a functional area, and the area's dispatcher skill routes to the specific sub-skill. The `(dispatcher for: ...)` clause listing sub-skills is the load-bearing signal that lets the LLM drill into the right skill.

When should I not compress my resolver file?

Do not compress when the routing file is under 12KB, since compression overhead exceeds the benefit, or when git shows uncommitted changes to the file. Both gates can only be overridden with an explicit `--force` request.

Why does routing accuracy drop after compressing AGENTS.md?

Common causes are omitted sub-skills from the `(dispatcher for: ...)` list, trigger phrases too narrow for intent recognition, or collapsing into fewer than 5 areas. Removing the dispatcher clause entirely collapses accuracy to around 41.7% on Sonnet in the bundled eval.

Does the dispatcher pattern work with non-Anthropic models?

The published A/B eval covers only Anthropic models (Opus 4.7, Sonnet 4.6, Haiku 4.5), where functional-areas beat the baseline by 13-17 points. Cross-vendor verification with Gemini and GPT is documented as a planned follow-up, not a validated result.