Route each request to the agent best suited to it, with a fallback and an explicit unknown path, instead of one generalist handling everything. Use when tasks vary enough that one prompt serves none of them well.
Scanned 9/5/2026
Install to Claude Code
npx -y skills add Amey-Thakur/AI-SKILLS --skill agent-specialist-router --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: agent-specialist-router
description: Route each request to the agent best suited to it, with a fallback and an explicit unknown path, instead of one generalist handling everything. Use when tasks vary enough that one prompt serves none of them well.
---
# Agent specialist router
One agent covering many task types is mediocre at each, because the
instructions that help one hurt another. A router classifies first and
dispatches to a specialist, which improves quality and cost at the price
of one extra step and a new failure mode: misrouting.
## Method
1. **Define the categories by how work differs, not by topic.** If two
categories would take identical instructions, they are one category.
2. **Classify cheaply.** The router does not need the strongest model;
it needs a reliable one with a small, well-specified job.
3. **Give the router an explicit unknown.** Forcing every input into a
category is what produces confidently wrong routing. Unknown goes to
a generalist or a human.
4. **Make each specialist genuinely specialised.** Tailored
instructions, examples, and tools. A router in front of identical
agents adds latency and nothing else.
5. **Log the routing decision with the input.** Misrouting is invisible
otherwise, and the log is what lets you fix the categories (see
agent-eval-design).
6. **Handle multi-category requests deliberately.** Either split them or
send to the broadest relevant specialist, decided in advance rather
than per case.
7. **Let specialists reject.** A specialist that recognises the work is
not its own should return it rather than attempt it (see
agent-handoff-protocol).
## Boundaries
Routing improves fit; it adds a step that can itself be wrong, and a
misroute is usually worse than a generalist attempt. It suits stable
task distributions and needs recategorising as work changes. Maintaining
many specialists costs more than one generalist, so the quality gain
must justify it.
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