# Skill Agent Topology

Audit whether a multi-agent setup earns its coordination cost, use before adding an...

- Repository: https://github.com/nyldn/claude-octopus (4351 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add nyldn/claude-octopus --skill skill-agent-topology -g -a claude-code -y`
Codex: `npx skills add nyldn/claude-octopus --skill skill-agent-topology -g -a codex -y`
Cursor: `npx skills add nyldn/claude-octopus --skill skill-agent-topology -g -a cursor -y`
Gemini CLI: `npx skills add nyldn/claude-octopus --skill skill-agent-topology -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add nyldn/claude-octopus`

## Description

Audit whether a multi-agent setup earns its coordination cost, use before adding an agent, or when a workflow feels slow or agents agree without adding signal

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

# Agent Topology Audit

Most advice about multi-agent systems is about how to add agents. This is about
whether to. It audits a setup you already have, counts what each boundary
between agents costs, and compares that against what the boundary buys. Removing
an agent is a valid, and often the correct, result.

The framing comes from Liu, Canhui (2026), *The Organizational Behavior of
Agentic AI* ([arXiv:2606.30986](https://arxiv.org/abs/2606.30986)), which models
coordination overhead as **contextual transaction cost**, the cost of making
task context usable across an agent boundary.

## When To Use

- Before adding another agent, seat, or phase to a workflow that already works.
- When a workflow is slow and it is not obvious which part is earning its time.
- When agents keep agreeing. Agreement that costs three dispatches and produces
  what one would have produced is overhead wearing the costume of consensus.
- When a handoff keeps losing something and the fix keeps being "add more
  context to the prompt".
- After a workflow produced a bad result and you want to know whether the
  topology or the models were at fault.

## When Not To Use

- To pick a workflow for a new task. That is `/octo:auto`, which already routes
  by intent, or `skill-decision-support` for a general option comparison.
- To decide whether to delegate a task to agents at all. That is the allocation
  step in `skill-intent-contract`.
- To choose between providers or models. See `skills/blocks/frontier-model-routing.md`.
- For a single-agent task. There are no boundaries to count.

## Inputs

- The workflow or setup under audit: which agents or seats, in what order, with
  what passing between them.
- What each agent receives and what it returns. Prompt and output shape matter
  more than model identity here.
- Optionally, a transcript or run directory, which turns estimates into
  observations.

The rest: https://github.com/nyldn/claude-octopus/blob/HEAD/.claude/skills/skill-agent-topology/SKILL.md