REPOGEO REPORT · LITE

nyldn/claude-octopus

Default branch main · commit 6fba14d3 · scanned 5/9/2026, 7:36:14 AM

GitHub: 3,282 stars · 291 forks

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface nyldn/claude-octopus, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening sentence to emphasize multi-model consensus for development tasks.

Why:

CURRENT

Every AI model has blind spots. Claude Octopus puts up to eight of them on every task, so blind spots surface before you ship — not after.

COPY-PASTE FIX

Claude Octopus orchestrates up to eight AI models on every research, design, or coding task, surfacing blind spots and ensuring consensus *before* you ship. It's designed to catch disagreements and errors that single models miss, acting as a multi-AI consensus engine for robust software development.

hightopics#2

Add more specific topics related to multi-model consensus and validation.

Why:

CURRENT

ai-agents, ai-orchestration, claude-code, claude-code-plugin, codex, copilot, developer-tools, double-diamond, gemini, multi-ai, multi-llm, ollama

COPY-PASTE FIX

ai-agents, ai-orchestration, claude-code, claude-code-plugin, codex, copilot, developer-tools, double-diamond, gemini, multi-ai, multi-llm, ollama, ai-consensus, multi-model-validation, ai-code-review-orchestration, llm-validation

mediumreadme#3

Add a 'How is Claude Octopus different?' or 'Comparison' section to the README.

Why:

COPY-PASTE FIX

## How is Claude Octopus different from other tools?
Unlike generic LLM orchestration frameworks (e.g., LangChain, LlamaIndex) that focus on chaining models, Claude Octopus specializes in *multi-model consensus and adversarial review* for development tasks. It's not a static code analyzer (like SonarQube or DeepSource) but an active agent that uses multiple LLMs to identify blind spots and disagreements in research, design, and code, ensuring higher quality output before shipping.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface nyldn/claude-octopus

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

DeepSource

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

DeepSource · recommended 1×
AWS CodeGuru Reviewer · recommended 1×
SonarQube · recommended 1×
Snyk Code · recommended 1×
GitHub Copilot · recommended 1×

CATEGORY QUERY
How to use multiple AI models for code review and identify potential errors?
you: not recommended
AI recommended (in order):
1. DeepSource
2. AWS CodeGuru Reviewer
3. SonarQube
4. Snyk Code
5. GitHub Copilot
6. ESLint
7. Pylint
8. RuboCop
9. Checkstyle
AI recommended 9 alternatives but never named nyldn/claude-octopus. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help orchestrate various large language models for robust software development tasks?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack (deepset/Haystack)
4. Microsoft Semantic Kernel
5. OpenAI Assistants API
6. LiteLLM
7. Guidance
AI recommended 7 alternatives but never named nyldn/claude-octopus. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of nyldn/claude-octopus?
pass
AI named nyldn/claude-octopus explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts nyldn/claude-octopus in production, what risks or prerequisites should they evaluate first?
pass
AI named nyldn/claude-octopus explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo nyldn/claude-octopus solve, and who is the primary audience?
pass
AI named nyldn/claude-octopus explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of nyldn/claude-octopus. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/nyldn/claude-octopus.svg)](https://repogeo.com/en/r/nyldn/claude-octopus)

HTML

<a href="https://repogeo.com/en/r/nyldn/claude-octopus"><img src="https://repogeo.com/badge/nyldn/claude-octopus.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

nyldn/claude-octopus — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite