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-parallel)<a href="https://agentmods.dev/commands/nyldn/claude-octopus/octo-parallel"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-parallel/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-parallel"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-parallel.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 | $0.00016 | $0.00504 |
| Opus 5 | $0.00008 | $0.00252 |
| Sonnet 5 | $0.00003 | $0.00101 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
octo-parallel 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- parallel — 97% identical, 6 lines differ
- octo-parallel — 86% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel - Team of Teams
INSTRUCTIONS FOR CLAUDE
When the user invokes this command (e.g., /octo:parallel <arguments>):
CORRECT - Read the explicit workflow source:
Read ${HOME}/.claude-octopus/plugin/.claude/skills/flow-parallel/SKILL.md, then execute it with <user's arguments>
INCORRECT:
Skill(skill: "flow-parallel", ...) ❌ Wrong! Octopus skills are model-invocation disabled
Task(subagent_type: "octo:parallel", ...) ❌ Wrong! This is a skill, not an agent type
Auto-loads the parallel skill for Team of Teams orchestration.
Quick Usage
Describe the compound task you want decomposed:
"Build a full authentication system with OAuth, RBAC, and audit logging"
"Create a complete e-commerce platform with payments, inventory, and shipping"
"Implement CI/CD pipeline with testing, linting, and deployment stages"
What Is Parallel?
Team of Teams orchestration — decomposes compound tasks into independent work packages and delegates each to a separate claude -p process. Each process loads the full Octopus plugin, giving every work package its own Double Diamond, agents, and quality gates.
Key architectural distinction: Task tool subagents don't load plugins. Independent claude -p processes do.
What You Get
- Work Breakdown Structure (WBS) decomposition
- Adversarial WBS cross-check to catch missed dependencies and scope overlaps before agents launch
- Independent
claude -pprocesses per work package - Full plugin capabilities in each worker
- Parallel execution with staggered launch
- Aggregated results with exit code verification
When To Use
- Compound tasks with 3+ independent components
- Tasks that would benefit from parallel execution
- Projects where each component needs full AI capabilities
- When you want isolated, non-interfering work streams
Natural Language Examples
"Parallel build auth system with OAuth, sessions, and RBAC"
"Team up on building a dashboard with charts, filters, and export"
"Decompose and parallelize the API migration across 5 services"
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 · 65 lines · 16 tokens per session scan A dae55d5efbc7
octo-parallel is a command published in the GitHub repository nyldn/claude-octopus (4,056 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 504 once invoked, about $0.0001 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.