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/octo-prd)<a href="https://agentmods.dev/commands/nyldn/claude-octopus/octo-prd"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-prd/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-prd"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-prd.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 | $0.00019 | $0.01466 |
| Opus 5 | $0.00010 | $0.00733 |
| Sonnet 5 | $0.00004 | $0.00293 |
| Haiku 4.5 | $0.00002 | $0.00147 |
Grade A, and why
octo-prd 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 7d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY COMPLIANCE — DO NOT SKIP
When the user explicitly invokes /octo:prd, you MUST follow the orchestrated PRD workflow below. You are PROHIBITED from writing the PRD directly without the required clarification, research, scoring, and orchestrate.sh steps.
EXECUTION MECHANISM — NON-NEGOTIABLE
You MUST execute this command by calling orchestrate.sh as documented below. You are PROHIBITED from:
- ❌ Doing the work yourself using only Claude-native tools (Agent, Read, Grep, Write)
- ❌ Using a single Claude subagent instead of multi-provider dispatch via orchestrate.sh
- ❌ Skipping orchestrate.sh because "I can do this faster directly"
Multi-LLM orchestration is the purpose of this command. If you execute using only Claude, you've violated the command's contract.
STOP - DO NOT INVOKE /skill OR Skill() AGAIN
This command is already executing. The feature to document is: $ARGUMENTS.feature
PHASE 0: CLARIFICATION (MANDATORY - DO THIS FIRST)
Before writing ANY PRD content, ask the user:
I'll create a PRD for: **$ARGUMENTS.feature**
To make this PRD highly targeted, please answer briefly:
1. **Target Users**: Who will use this? (developers, end-users, admins, agencies?)
2. **Core Problem**: What pain point does this solve? Any metrics on current impact?
3. **Success Criteria**: How will you measure success? (KPIs, adoption rate, time saved?)
4. **Constraints**: Any technical, budget, timeline, or platform constraints?
5. **Existing Context**: Greenfield project or integrating with existing systems?
(Type "skip" to proceed with assumptions, or answer inline)
WAIT for user response before proceeding.
PHASE 1: QUICK RESEARCH (Max 60 seconds)
Check provider availability first:
set -euo pipefail
OCTO_ROOT="${OCTO_ROOT:-${CLAUDE_PLUGIN_ROOT:-${HOME}/.claude-octopus/plugin}}"
# Check if multi-provider research is available
CODEX_AVAILABLE="false"
if command -v codex >/dev/null 2>&1; then
CODEX_AVAILABLE="true"
fi
AGY_AVAILABLE="false"
if command -v agy >/dev/null 2>&1; then
AGY_AVAILABLE="true"
fi
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.
- 7d ago First seen · 171 lines · 19 tokens per session scan A 4da06b736b38
octo-prd is a command published in the GitHub repository nyldn/claude-octopus (4,061 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,466 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.