RRepoGEO

REPOGEO REPORT · LITE

opral/lix

Default branch main · commit b284e2bf · scanned 6/15/2026, 6:17:52 AM

GitHub: 664 stars · 19 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 opral/lix, 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
    Add a prominent H1 to the README clearly stating the project's full name and purpose

    Why:

    CURRENT
    <h3 align="center">Embeddable version control system for AI agents</h3>
    COPY-PASTE FIX
    <h1 align="center">Lix: Embeddable Version Control for AI Agents and Non-Code Files</h1>
<p align="center">Lix gives AI agents versions, branches, checkpoints, reviewable changes, rollback, merging, immutable history, and SQL-queryable context for **non-code files** like PDF, DOCX, XLSX, CSV, JSON, and agent-generated work.</p>
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Choose and add a standard open-source license file, e.g., MIT, Apache-2.0, or GPL-3.0, to the repository root.)
  • mediumtopics#3
    Add more specific topics to improve category visibility

    Why:

    CURRENT
    ai, ai-agents, change-detection, version-control
    COPY-PASTE FIX
    ai, ai-agents, change-detection, version-control, data-versioning, non-code-files, mlops

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 opral/lix
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
iterative/dvc
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. iterative/dvc · recommended 2×
  2. mlflow/mlflow · recommended 2×
  3. pachyderm/pachyderm · recommended 2×
  4. git-lfs/git-lfs · recommended 1×
  5. treeverse/lakefs · recommended 1×
  • CATEGORY QUERY
    How can I implement version control for AI agent outputs and non-code files?
    you: not recommended
    AI recommended (in order):
    1. DVC (Data Version Control) (iterative/dvc)
    2. Git Large File Storage (Git LFS) (git-lfs/git-lfs)
    3. MLflow Tracking (mlflow/mlflow)
    4. LakeFS (treeverse/lakefs)
    5. Pachyderm (pachyderm/pachyderm)
    6. Quilt Data (quiltdata/quilt)

    AI recommended 6 alternatives but never named opral/lix. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are embeddable solutions for tracking changes in AI agent-generated data?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. DVC (iterative/dvc)
    3. Weights & Biases (wandb/wandb)
    4. Comet ML (comet-ml/comet-python-sdk)
    5. Pachyderm (pachyderm/pachyderm)
    6. Delta Lake (delta-io/delta)

    AI recommended 6 alternatives but never named opral/lix. 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 opral/lix?
    pass
    AI named opral/lix explicitly

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

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