RRepoGEO

REPOGEO REPORT · LITE

Jedward23/Tmux-Orchestrator

Default branch main · commit 71935302 · scanned 6/18/2026, 2:38:13 PM

GitHub: 1,785 stars · 335 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
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 Jedward23/Tmux-Orchestrator, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Orchestrates autonomous AI agents (e.g., Claude) using `tmux` sessions to enable persistent, scalable, and coordinated work across multiple projects without human intervention.
  • highreadme#2
    Reposition the README's opening sentence to clarify its core purpose

    Why:

    CURRENT
    **Run AI agents 24/7 while you sleepThe Tmux Orchestrator enables Claude agents to work autonomously, schedule their own check-ins, and coordinate across multiple projects without human intervention.
    COPY-PASTE FIX
    **The Tmux Orchestrator is a framework designed to enable autonomous AI agents (like Claude) to work 24/7, scheduling their own check-ins and coordinating across multiple projects without human intervention.**

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 Jedward23/Tmux-Orchestrator
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. Significant-Gravitas/AutoGPT · recommended 1×
  3. AgentGPT/AgentGPT · recommended 1×
  4. SuperAGI/SuperAGI · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I run AI agents autonomously and coordinate them across multiple coding projects?
    you: not recommended
    AI recommended (in order):
    1. AutoGPT (Significant-Gravitas/AutoGPT)
    2. AgentGPT (AgentGPT/AgentGPT)
    3. SuperAGI (SuperAGI/SuperAGI)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Open Interpreter (OpenInterpreter/open-interpreter)
    7. CrewAI (joaomdmoura/crewAI)
    8. GitHub Copilot Workspace

    AI recommended 8 alternatives but never named Jedward23/Tmux-Orchestrator. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable persistent AI agent workflows and scale them for multiple development teams?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Kubernetes (kubernetes/kubernetes)
    3. Docker Swarm
    4. PostgreSQL
    5. MongoDB (mongodb/mongo)
    6. Pinecone
    7. Weaviate (weaviate/weaviate)
    8. OpenAI Assistants API
    9. Kafka (apache/kafka)
    10. RabbitMQ (rabbitmq/rabbitmq-server)
    11. Microsoft Azure AI Studio
    12. Azure Machine Learning
    13. Google Cloud Vertex AI
    14. Vertex AI Pipelines
    15. AWS Step Functions
    16. AWS Lambda
    17. Amazon ECS
    18. Prefect (PrefectHQ/prefect)
    19. Apache Airflow (apache/airflow)
    20. AWS SQS
    21. Cassandra (apache/cassandra)

    AI recommended 21 alternatives but never named Jedward23/Tmux-Orchestrator. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Jedward23/Tmux-Orchestrator?
    pass
    AI named Jedward23/Tmux-Orchestrator explicitly

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

  • If a team adopts Jedward23/Tmux-Orchestrator in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Jedward23/Tmux-Orchestrator 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 Jedward23/Tmux-Orchestrator solve, and who is the primary audience?
    pass
    AI did not name Jedward23/Tmux-Orchestrator — likely talking about a different project

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

Embed your GEO score

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MARKDOWN (README)
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Jedward23/Tmux-Orchestrator — 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