Delegation

Route user queries to domain experts and return routing decisions with confidence scores.

Updated Dec 20, 2025
One-click install
npx skills add https://github.com/jvalenzano/ranger-twin --skill delegation-jvalenzano
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Delegation
Source: https://github.com/jvalenzano/ranger-twin/tree/main/agents/coordinator/skills/delegation
Command: npx skills add https://github.com/jvalenzano/ranger-twin --skill delegation-jvalenzano

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Delegation routes user queries to the most appropriate specialist or coordinator, reducing handoffs and ensuring timely, accurate routing decisions.

Core Features & Use Cases

  • Route queries to burn-analyst, trail-assessor, cruising-assistant, or nepa-advisor based on domain keywords.
  • Detect when a query requires synthesis and orchestrate a coordinated multi-agent response.
  • Return structured outputs including target_agent, confidence, reasoning, matched_keywords, and fallback options.

Quick Start

Use the route_query script to route a sample query (for example, "Which trails are closed due to hazard trees?") and observe the assigned target_agent and confidence.

Frequently Asked Questions about Delegation

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

FAQPage Schema
How do I route user queries to specialized domain experts in a multi-agent system?

Query routing analyzes keywords and context to direct questions to specialized domain experts. It returns a routing decision specifying the target agent, a confidence score, and synthesis requirements.

What is the best way to coordinate a multi-agent response when a query requires synthesis?

Multi-agent coordination detects when a query requires synthesis and orchestrates a coordinated response. The routing mechanism assigns a coordinator to manage the specialized agents and aggregate their outputs.

How does keyword matching assign a target agent and confidence score?

Keyword matching assigns a target agent by analyzing query context against domain-specific keywords. It outputs a confidence score, matched keywords, reasoning, and fallback options to ensure accurate specialist routing.

Can I route queries to specific experts like a trail assessor or cruising assistant?

Yes, queries can route to specific experts like trail-assessor, cruising-assistant, burn-analyst, or nepa-advisor. The routing decision is based on matching domain keywords within the user's query context.

What happens if a query does not match the primary target agent?

When a query lacks a clear primary match, the routing decision provides fallback options. It returns structured output including target_agent, confidence, reasoning, and alternative agents to handle the query.