RRepoGEO

REPOGEO REPORT · LITE

edison7009/EchoBird

Default branch main · commit 0597406a · scanned 5/30/2026, 8:06:59 PM

GitHub: 1,365 stars · 87 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 edison7009/EchoBird, 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

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

OVERALL DIRECTION
  • highabout#1
    Update repository description to clarify AI agent deployment

    Why:

    CURRENT
    One-Click Install All
    COPY-PASTE FIX
    Unified platform for one-click installation and management of local AI agents.
  • mediumlicense#2
    Add a section to README clarifying the project's license(s)

    Why:

    COPY-PASTE FIX
    ## License

This project includes a LICENSE file. Please refer to it for specific terms and conditions. If you have questions about the applicable license(s), please open an issue.

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 edison7009/EchoBird
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Docker Compose
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Docker Compose · recommended 1×
  2. Kubernetes · recommended 1×
  3. Minikube · recommended 1×
  4. K3s · recommended 1×
  5. Ansible · recommended 1×
  • CATEGORY QUERY
    How to simplify the deployment and management of multiple local AI agents?
    you: not recommended
    AI recommended (in order):
    1. Docker Compose
    2. Kubernetes
    3. Minikube
    4. K3s
    5. Ansible
    6. Supervisor
    7. PM2
    8. Poetry
    9. Conda
    10. Miniconda

    AI recommended 10 alternatives but never named edison7009/EchoBird. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a unified platform for easily installing and running various AI models locally.
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. Ollama
    3. Jan
    4. Pinokio
    5. LocalAI
    6. InvokeAI
    7. ComfyUI

    AI recommended 7 alternatives but never named edison7009/EchoBird. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 edison7009/EchoBird?
    pass
    AI named edison7009/EchoBird explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts edison7009/EchoBird in production, what risks or prerequisites should they evaluate first?
    pass
    AI named edison7009/EchoBird 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 edison7009/EchoBird solve, and who is the primary audience?
    pass
    AI named edison7009/EchoBird explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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edison7009/EchoBird — 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