RRepoGEO

REPOGEO REPORT · LITE

tensorchord/envd

Default branch main · commit c5e6fd54 · scanned 5/12/2026, 10:26:57 AM

GitHub: 2,203 stars · 167 forks

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 tensorchord/envd, 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
  • highabout#1
    Update the repository description to explicitly lead with AI/ML focus

    Why:

    CURRENT
    🏕️ Reproducible development environment for humans and agents
    COPY-PASTE FIX
    🏕️ Reproducible AI/ML development environments for humans and agents
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## `envd` vs. Alternatives

`envd` is specifically designed for AI/ML development environments, offering streamlined setup for CUDA, Python, and common ML frameworks, unlike general-purpose tools such as Docker, Conda, or Nix which require more manual configuration for ML workflows. For example, while Docker provides containerization, `envd` abstracts away complex Dockerfile management for ML users. Similarly, Conda and Poetry manage Python dependencies but don't provide the full reproducible containerized environment with GPU support that `envd` offers out-of-the-box.
  • lowtopics#3
    Expand repository topics with more specific ML/AI environment keywords

    Why:

    CURRENT
    agent, buildkit, code-agent, codex, developer-tools, development-environment, docker, hacktoberfest, llmops, mlops, mlops-workflow, model-serving
    COPY-PASTE FIX
    agent, buildkit, code-agent, codex, developer-tools, development-environment, docker, hacktoberfest, llmops, mlops, mlops-workflow, model-serving, machine-learning-environments, deep-learning-environments, cuda-environments, python-environments, reproducible-ml, ml-dev-ops

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 tensorchord/envd
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Dev Containers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Dev Containers · recommended 1×
  2. Poetry · recommended 1×
  3. Conda · recommended 1×
  4. Nix · recommended 1×
  5. Pachyderm · recommended 1×
  • CATEGORY QUERY
    How to easily create reproducible containerized development environments for machine learning projects?
    you: not recommended
    AI recommended (in order):
    1. Dev Containers
    2. Poetry
    3. Conda
    4. Nix
    5. Pachyderm
    6. MLflow

    AI recommended 6 alternatives but never named tensorchord/envd. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to simplify managing complex AI/ML dependencies in a consistent development environment?
    you: not recommended
    AI recommended (in order):
    1. Conda (conda/conda)
    2. Poetry (python-poetry/poetry)
    3. Docker
    4. Nix (NixOS/nix)
    5. Virtualenv / venv (pypa/virtualenv)
    6. pip-tools (jazzband/pip-tools)

    AI recommended 6 alternatives but never named tensorchord/envd. 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 tensorchord/envd?
    pass
    AI named tensorchord/envd explicitly

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

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

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/tensorchord/envd.svg)](https://repogeo.com/en/r/tensorchord/envd)
HTML
<a href="https://repogeo.com/en/r/tensorchord/envd"><img src="https://repogeo.com/badge/tensorchord/envd.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

tensorchord/envd — 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