RRepoGEO

REPOGEO REPORT · LITE

av/harbor

Default branch main · commit ff993fb4 · scanned 5/23/2026, 4:26:44 AM

GitHub: 2,958 stars · 201 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 av/harbor, 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
  • highreadme#1
    Explicitly position av/harbor against common local LLM deployment tools

    Why:

    COPY-PASTE FIX
    ### Why choose av/harbor over Ollama, LM Studio, or text-generation-webui?
While tools like Ollama and LM Studio simplify running individual models, av/harbor provides a *complete, pre-wired LLM stack* with hundreds of integrated services, allowing you to deploy an entire AI development environment with a single command, not just a model runner.
  • mediumtopics#2
    Expand repository topics with more specific AI environment keywords

    Why:

    CURRENT
    ai, automation, bash, cli, container, docker, docker-compose, homelab, llm, local, mcp, npm, package, pypi, safetensors, self-hosted, server, tool, tools
    COPY-PASTE FIX
    ai, automation, bash, cli, container, docker, docker-compose, homelab, llm, local, mcp, npm, package, pypi, safetensors, self-hosted, server, tool, tools, ai-development, llm-stack, ai-orchestration, local-llm-platform, ai-platform

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 av/harbor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ollama
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 1×
  2. LM Studio · recommended 1×
  3. Jan · recommended 1×
  4. LocalAI · recommended 1×
  5. oobabooga/text-generation-webui · recommended 1×
  • CATEGORY QUERY
    How can I quickly deploy a local LLM development environment without extensive configuration?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Jan
    4. LocalAI
    5. text-generation-webui (oobabooga/text-generation-webui)

    AI recommended 5 alternatives but never named av/harbor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are options for a self-hosted platform to experiment with various AI models and services?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes (kubernetes/kubernetes)
    2. Kubeflow (kubeflow/kubeflow)
    3. OpenShift
    4. Open Data Hub (opendatahub-io/opendatahub-operator)
    5. MLflow (mlflow/mlflow)
    6. Hugging Face Inference Endpoints
    7. text-generation-inference (huggingface/text-generation-inference)
    8. transformers (huggingface/transformers)
    9. Pachyderm (pachyderm/pachyderm)
    10. LocalStack (localstack/localstack)
    11. Docker (docker/docker-ce)
    12. Docker Compose (docker/compose)

    AI recommended 12 alternatives but never named av/harbor. 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 av/harbor?
    pass
    AI named av/harbor explicitly

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

  • If a team adopts av/harbor in production, what risks or prerequisites should they evaluate first?
    pass
    AI named av/harbor 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 av/harbor solve, and who is the primary audience?
    pass
    AI named av/harbor 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 av/harbor. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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Pro

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av/harbor — 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
av/harbor — RepoGEO report