Claude Octopus is an orchestration project that sends research, design, and coding tasks to Claude Code and other AI model providers so their results can be compared. Developers use it for multi-model work, disagreement detection, reviews, persistent context, and an optional workflow that moves from discovery through delivery. The catalogue entries are its commands, skills, agents, instructions, hooks, plugins, and settings.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nyldn/claude-octopus --skill skill-agent-topologygit clone --depth 1 https://github.com/nyldn/claude-octopusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nyldn/claude-octopus/skill-agent-topology)<a href="https://agentmods.dev/skills/nyldn/claude-octopus/skill-agent-topology"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-agent-topology/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nyldn/claude-octopus/skill-agent-topology"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-agent-topology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.01777 |
| Opus 5 | $0.00017 | $0.00889 |
| Sonnet 5 | $0.00007 | $0.00355 |
| Haiku 4.5 | $0.00003 | $0.00178 |
Grade A, and why
skill-agent-topology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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), 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, orskill-decision-supportfor 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.
If the setup is only described rather than run, say so in the output. An audit of a described topology is a prediction; an audit of a transcript is a measurement.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 173 lines · 34 tokens per session scan A 079d650e11e5
skill-agent-topology is a skill published in the GitHub repository nyldn/claude-octopus (4,062 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 1,777 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
ai-context
Generates, updates, and audits AGENTS-first AI IDE context files. Builds canonical AGENTS.md plus thin bridges for Claude Code, Cursor (modern .cursor/rules/.mdc), Copilot, Cline (.clinerules/ directory), Windsurf, Gemini CLI, Codex CLI, and OpenCode. Use for creating, regenerating, fixing, or promoting context files…
architecture
This skill should be used when managing Architecture Decision Records or C4 diagrams.
linear-fetch
This skill should be used when a user input contains a Linear issue reference (e.g., SOL-39 or linear.app/.../issue/ ) and the downstream agent needs the screenshots embedded in the issue as visual context.
invoice
This skill should be used when the founder wants to get paid through their own Stripe account: list who owes them, create and send an invoice behind a human-approval preview, or chase an overdue one. Test-mode only in v1.
legal-generate
This skill should be used when generating draft legal documents for a project or company. It gathers company context interactively, invokes the legal-document-generator agent, and writes markdown output.
agent-native-audit
This skill should be used when conducting a scored agent-native architecture review. It launches 8 parallel sub-agents to audit action parity, context injection, CRUD completeness, capability discovery, and prompt-native features.