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
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add nyldn/claude-octopus/plugin install octoWrote 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/commands/nyldn/claude-octopus/multi)<a href="https://agentmods.dev/commands/nyldn/claude-octopus/multi"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/multi/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/commands/nyldn/claude-octopus/multi"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/multi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00015 | $0.02213 |
| Opus 5 | $0.00008 | $0.01107 |
| Sonnet 5 | $0.00003 | $0.00443 |
| Haiku 4.5 | $0.00002 | $0.00221 |
Grade A, and why
multi 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 5d 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.
This is a copy
97% identical to octo-multi — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi - Multi-Provider Override
Forces multi-provider execution for any task using all available AI providers.
🤖 INSTRUCTIONS FOR CLAUDE
EXECUTION MECHANISM — NON-NEGOTIABLE
You MUST dispatch work to external providers (Codex, Antigravity, etc.) for this command. You are PROHIBITED from:
- ❌ Executing the entire task using only Claude-native tools
- ❌ Using a single Agent subagent instead of multi-provider dispatch
- ❌ Skipping provider dispatch because "I can handle this alone"
Multi-LLM orchestration is the purpose of this command. Single-model execution defeats its purpose.
When the user invokes this command (e.g., /octo:multi <task>):
Step 1: Cost Awareness & Intent Confirmation
CRITICAL: Before forcing multi-provider execution, use AskUserQuestion to confirm intent and cost awareness:
AskUserQuestion({
questions: [
{
question: "Why do you need multiple AI perspectives?",
header: "Intent",
multiSelect: false,
options: [
{label: "High-stakes decision", description: "Critical choice requiring comprehensive analysis"},
{label: "Quality validation", description: "Cross-check important work for accuracy"},
{label: "Learning different approaches", description: "See how different models think"},
{label: "Comparing perspectives", description: "Want to see model-specific insights"},
{label: "Just exploring", description: "Curious about multi-AI capabilities"}
]
},
{
question: "Are you aware this uses external API credits?",
header: "Cost",
multiSelect: false,
options: [
{label: "Yes, proceed", description: "I understand this may use external provider credits or subscriptions"},
{label: "Tell me more about costs", description: "Explain what I'll be charged"},
{label: "Use included/local providers only", description: "Use Claude plus providers that do not add per-query API charges"}
]
}
]
})
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.
- 5d ago First seen · 243 lines · 15 tokens per session scan A b141b137b326
multi is a command published in the GitHub repository nyldn/claude-octopus (4,061 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 2,213 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to octo-multi, differing in 3 lines, and is treated as a copy.
Other commands, from other repositories
ai-context
Generate, update, or audit AI IDE context files with AGENTS.md as the canonical shared context and tool-specific bridge files. Signal Gate principle — only what agents cannot discover: $ARGUMENTS.
sync
Analyze codebase and populate knowledge-base with conventions, patterns, and technical debt.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.