RRepoGEO

REPOGEO REPORT · LITE

OpenNSWM-Lab/FAROS

Default branch main · commit e531fa90 · scanned 6/8/2026, 3:07:53 PM

GitHub: 1,025 stars · 172 forks

AI VISIBILITY SCORE
30 /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
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 OpenNSWM-Lab/FAROS, 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:

    CURRENT
    Description: (none)
    COPY-PASTE FIX
    FAROS is a blueprint-driven AutoResearch runtime for the LLM domain, enabling users to define, bind, and run automated research workflows.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    License: (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen open-source license text (e.g., MIT, Apache-2.0). Also, add a line to the README, for example: `## License\nThis project is licensed under the [MIT License](LICENSE).`

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 OpenNSWM-Lab/FAROS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MLflow · recommended 2×
  2. DVC · recommended 2×
  3. Apache Airflow · recommended 2×
  4. Weights & Biases · recommended 1×
  5. Hydra · recommended 1×
  • CATEGORY QUERY
    How can I automate research workflows for large language model experiments?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Weights & Biases
    3. Hydra
    4. DVC
    5. Ray Tune
    6. Prefect
    7. Apache Airflow
    8. argparse
    9. logging

    AI recommended 9 alternatives but never named OpenNSWM-Lab/FAROS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a system to define and run AI research workflows using blueprints.
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Kubeflow Pipelines
    3. Metaflow
    4. Apache Airflow
    5. DVC
    6. Pachyderm

    AI recommended 6 alternatives but never named OpenNSWM-Lab/FAROS. 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 OpenNSWM-Lab/FAROS?
    pass
    AI named OpenNSWM-Lab/FAROS explicitly

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

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

    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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OpenNSWM-Lab/FAROS — 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