RRepoGEO

REPOGEO REPORT · LITE

AgnostiqHQ/covalent

Default branch develop · commit 1e3da804 · scanned 6/3/2026, 4:27:01 AM

GitHub: 861 stars · 111 forks

AI VISIBILITY SCORE
33 /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
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 AgnostiqHQ/covalent, 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
    Reposition the README's main heading to emphasize heterogeneous and quantum aspects

    Why:

    CURRENT
    <div align="center"><b>Run AI, ML, and Scientific Research Code on Any Cloud or On-Prem Cluster with a Single Line</b></div>
    COPY-PASTE FIX
    <div align="center"><b>Orchestrate Hybrid Classical-Quantum, AI/ML, and HPC Workflows Across Heterogeneous Compute Environments</b></div>
  • mediumabout#2
    Update the repository description to explicitly mention 'hybrid classical-quantum workflows'

    Why:

    CURRENT
    Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
    COPY-PASTE FIX
    Pythonic tool for orchestrating hybrid classical-quantum, machine-learning, and high-performance computing workflows in heterogeneous compute environments.
  • lowtopics#3
    Add 'heterogeneous-computing' and 'hybrid-workflows' to the repository topics

    Why:

    CURRENT
    covalent, data-pipeline, data-science, deep-learning, hacktoberfest, hpc, hpc-applications, machine-learning, machinelearning, machinelearning-python, orchestration, parallelization, pipelines, python, quantum, quantum-computing, quantum-machine-learning, workflow, workflow-automation, workflow-management
    COPY-PASTE FIX
    covalent, data-pipeline, data-science, deep-learning, hacktoberfest, hpc, hpc-applications, heterogeneous-computing, hybrid-workflows, machine-learning, machinelearning, machinelearning-python, orchestration, parallelization, pipelines, python, quantum, quantum-computing, quantum-machine-learning, workflow, workflow-automation, workflow-management

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 AgnostiqHQ/covalent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Metaflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Metaflow · recommended 2×
  2. Apache Airflow · recommended 1×
  3. Prefect · recommended 1×
  4. Kubeflow Pipelines · recommended 1×
  5. Luigi · recommended 1×
  • CATEGORY QUERY
    How to orchestrate complex machine learning and HPC workflows in Python across heterogeneous environments?
    you: not recommended
    AI recommended (in order):
    1. Apache Airflow
    2. Prefect
    3. Kubeflow Pipelines
    4. Metaflow
    5. Luigi
    6. Dagster

    AI recommended 6 alternatives but never named AgnostiqHQ/covalent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python tool simplifies running AI/ML and scientific research on diverse cloud or on-prem clusters?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow
    2. MLflow
    3. Ray
    4. Dask
    5. Apache Spark
    6. Metaflow

    AI recommended 6 alternatives but never named AgnostiqHQ/covalent. 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 AgnostiqHQ/covalent?
    pass
    AI did not name AgnostiqHQ/covalent — 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?

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