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-prd-score)<a href="https://agentmods.dev/commands/nyldn/claude-octopus/octo-prd-score"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-prd-score/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-score"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-prd-score.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 | $0.00014 | $0.01047 |
| Opus 5 | $0.00007 | $0.00524 |
| Sonnet 5 | $0.00003 | $0.00209 |
| Haiku 4.5 | $0.00001 | $0.00105 |
Grade A, and why
octo-prd-score 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
2 near-identical copies found in the catalogue:
- prd-score — 94% identical, 7 lines differ
- octo-prd-score — 89% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STOP - DO NOT INVOKE /skill OR Skill() AGAIN
This command is already executing. The PRD file to score is: $ARGUMENTS.file
Instructions
Score the PRD against the 100-point AI-optimization framework.
Step 1: Load the PRD
Read the file at $ARGUMENTS.file using the Read tool.
Step 2: Evaluate Against Framework
Score each category:
Category A: AI-Specific Optimization (25 points)
- Sequential Phases: 0-10 pts (phases ordered by dependencies, each 5-15 min work)
- Explicit Non-Goals: 0-8 pts (dedicated Non-Goals section with explicit boundaries)
- Structured Format: 0-7 pts (FR codes, consistent headings, Given-When-Then criteria)
Category B: Traditional PRD Core (25 points)
- Problem Statement: 0-7 pts (quantified pain points, metrics)
- Goals & Metrics: 0-8 pts (SMART goals, P0/P1 priorities)
- User Personas: 0-5 pts (named personas with scenarios)
- Technical Specs: 0-5 pts (architecture, integrations, data models)
Category C: Implementation Clarity (30 points)
- Functional Requirements: 0-10 pts (FR codes, P0/P1/P2, acceptance criteria)
- Non-Functional Requirements: 0-5 pts (security, performance, reliability)
- Architecture: 0-10 pts (diagrams, data flow, API contracts)
- Phased Implementation: 0-5 pts (clear phases, time estimates, deliverables)
Category D: Completeness (20 points)
- Risk Assessment: 0-5 pts (3-5 risks with mitigations)
- Dependencies: 0-3 pts (external and internal)
- Examples: 0-7 pts (code snippets, API examples)
- Documentation Quality: 0-5 pts (formatting, ToC, glossary)
Step 3: Generate Score Report
Output:
## PRD Score Report: [PRD Title]
### Overall Score: XX/100 ([Grade])
Grade Scale: A+ (90-100), A (80-89), B (70-79), C (60-69), D (<60)
| Category | Score | Max |
|----------|-------|-----|
| A. AI-Specific Optimization | XX | 25 |
| B. Traditional PRD Core | XX | 25 |
| C. Implementation Clarity | XX | 30 |
| D. Completeness | XX | 20 |
### Top 3 Improvement Recommendations
1. [Highest impact fix] - +X points
2. [Second priority] - +X points
3. [Third priority] - +X points
### Verdict
[1-2 sentence summary of PRD quality and AI-readiness]
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 · 119 lines · 14 tokens per session scan A a6e3fbfc415e
octo-prd-score is a command published in the GitHub repository nyldn/claude-octopus (4,061 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 1,047 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
sdd-init
Initialize SDD context — detects project stack and bootstraps persistence backend.
extend
Capture a mid-PR sub-idea and implement it onto the current open PR's branch — no new branch, no new PR. Preserves Verify → Review → Deliver.
sparc-refinement-optimization-mode
🧹 Optimizer - You refactor, modularize, and improve system performance. You enforce file size limits, dependenc...
integrity
Verify the cognitive toolkit's internal consistency and memory accuracy. The toolkit makes claims about its own structure, and memory makes claims about the world. This command checks both.
review
Code review based on git diff. Reviews staged/unstaged changes or a specific commit range for bugs, security issues, and code quality.
checklist
Generate a custom checklist for the current feature based on user requirements.