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.
git 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/commands/nyldn/claude-octopus/octo-research)<a href="https://agentmods.dev/commands/nyldn/claude-octopus/octo-research"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-research/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/octo-research"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-research.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 | $0.00009 | $0.01262 |
| Opus 5 | $0.00005 | $0.00631 |
| Sonnet 5 | $0.00002 | $0.00252 |
| Haiku 4.5 | $0.00001 | $0.00126 |
Grade A, and why
octo-research 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- research — 97% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research - Deep Multi-AI Research
Your first output line MUST be: 🐙 Octopus Research
🤖 INSTRUCTIONS FOR CLAUDE
MANDATORY COMPLIANCE — DO NOT SKIP
When the user explicitly invokes /octo:research, you MUST execute the structured research workflow below. You are PROHIBITED from answering directly, skipping the multi-provider research, or deciding the topic is "too simple" for deep research. The user chose this command deliberately — respect that choice.
EXECUTION MECHANISM — NON-NEGOTIABLE
You MUST execute this command by reading and following the dedicated research source. You are PROHIBITED from:
- ❌ Using the Agent tool to research/implement yourself instead of invoking the skill
- ❌ Using WebFetch/Read/Grep as a substitute for multi-provider dispatch
- ❌ Skipping
orchestrate.shcalls because "I can do this faster directly" - ❌ Implementing the task using only Claude-native tools (Agent, Write, Edit)
Multi-LLM orchestration is the purpose of this command. If you execute using only Claude, you've violated the command's contract.
When the user invokes this command (e.g., /octo:research <arguments>):
Step 1: Resolve Research Breadth/Intensity
First parse explicit flags from the user's arguments:
--breadth=light|standard|exhaustive--intensity=quick|standard|deep
Map breadth to intensity when intensity is absent:
light->quickstandard->standardexhaustive->deep
If neither flag is present, use the AskUserQuestion tool to select intensity:
AskUserQuestion({
questions: [
{
question: "How thorough should the research be?",
header: "Research Intensity",
multiSelect: false,
options: [
{label: "Quick (1-2 min)", description: "2 agents — fast problem space scan"},
{label: "Standard (2-4 min)", description: "4-5 agents — balanced multi-perspective coverage (recommended)"},
{label: "Deep (3-6 min)", description: "6-7 agents — exhaustive analysis with web search"}
]
}
]
})
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.
- 8d ago First seen · 143 lines · 9 tokens per session scan A de136f0467a1
octo-research is a command published in the GitHub repository nyldn/claude-octopus (4,061 stars, last pushed today), licensed MIT. It adds 9 tokens to every session and 1,262 once invoked, about $0.0000 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 2026-09-03.
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.