RRepoGEO

REPOGEO REPORT · LITE

nyldn/claude-octopus

Default branch main · commit fae86c21 · scanned 6/19/2026, 2:36:19 AM

GitHub: 3,646 stars · 340 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 README opening to emphasize multi-model blind spot detection and consensus

    Why:

    CURRENT
    Every AI model has blind spots. Claude Octopus puts up to nine of them on every task, so blind spots surface before you ship — not after. It orchestrates Codex, Gemini, Antigravity CLI, Copilot, Qwen, Ollama, Perplexity, OpenRouter, and OpenCode alongside Claude Code, with consensus gates that flag any disagreements.
    COPY-PASTE FIX
    Every AI model has blind spots. Claude Octopus is a multi-AI orchestration framework designed to surface these blind spots *before* you ship, by putting up to nine models on every task and using consensus gates to flag disagreements. It orchestrates Codex, Gemini, Antigravity CLI, Copilot, Qwen, Ollama, Perplexity, OpenRouter, and OpenCode alongside Claude Code, ensuring robust code review, error detection, and design validation.
  • mediumtopics#2
    Add more specific topics for AI reliability and multi-model 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-reliability, model-validation, code-review-ai, multi-agent-systems, consensus-ai
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives
    
    Unlike generic LLM orchestration frameworks such as LangChain or LlamaIndex, Claude Octopus is specifically designed for multi-tool use with Anthropic's Claude 3 models, focusing on parallel tool execution and complex tool chaining within a single turn. Its core differentiator is its robust support for surfacing AI blind spots and ensuring reliability through multi-model consensus, rather than just general-purpose agentic workflows.

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
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Haystack · recommended 2×
  4. GPT-4 (OpenAI) · recommended 1×
  5. Claude 3 Opus (Anthropic) · recommended 1×
  • CATEGORY QUERY
    How to use multiple large language models for robust code review and error detection?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. GPT-4 (OpenAI)
    5. Claude 3 Opus (Anthropic)
    6. Gemini 1.5 Pro (Google)
    7. GPT-3.5 Turbo (OpenAI)
    8. Code Llama (Meta)
    9. Mistral Large (Mistral AI)

    AI recommended 9 alternatives but never named nyldn/claude-octopus. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for orchestrating multiple AI agents to prevent single model blind spots?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen
    4. Haystack
    5. CrewAI
    6. Marvin

    AI recommended 6 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?

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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