RRepoGEO

REPOGEO REPORT · LITE

vinilana/dotcontext

Default branch main · commit 70e09ec2 · scanned 6/16/2026, 12:41:48 AM

GitHub: 502 stars · 92 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 vinilana/dotcontext, 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 'About' description for better AI categorization

    Why:

    COPY-PASTE FIX
    A harness engineering runtime for AI-assisted software delivery, providing a structured operating environment for AI coding agents to manage project context, workflows, and auditable execution.
  • mediumhomepage#2
    Set the project homepage to the npm package URL

    Why:

    COPY-PASTE FIX
    https://www.npmjs.com/package/@dotcontext/cli

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 vinilana/dotcontext
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MLflow · recommended 1×
  2. Kubeflow · recommended 1×
  3. Weights & Biases · recommended 1×
  4. DVC · recommended 1×
  5. Azure Machine Learning · recommended 1×
  • CATEGORY QUERY
    How can I manage AI agent execution, project context, and workflow for auditable software delivery?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Kubeflow
    3. Weights & Biases
    4. DVC
    5. Azure Machine Learning
    6. Google Cloud AI Platform
    7. Amazon SageMaker
    8. Dagster

    AI recommended 8 alternatives but never named vinilana/dotcontext. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide a structured runtime environment for AI coding agents to ensure consistent execution?
    you: not recommended
    AI recommended (in order):
    1. Farama Gymnasium (Farama-Foundation/Gymnasium)
    2. MLflow (mlflow/mlflow)
    3. Docker
    4. Kubernetes (kubernetes/kubernetes)
    5. Ray (ray-project/ray)
    6. Metaflow (Netflix/metaflow)
    7. Airflow (apache/airflow)

    AI recommended 7 alternatives but never named vinilana/dotcontext. 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 vinilana/dotcontext?
    pass
    AI named vinilana/dotcontext explicitly

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

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

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

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vinilana/dotcontext — 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