REPOGEO REPORT · LITE

tensorchord/envd

Default branch main · commit c5e6fd54 · scanned 6/22/2026, 4:36:54 PM

GitHub: 2,212 stars · 169 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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

highreadme#1

Strengthen README's opening statement to highlight ML-centric differentiation

Why:

CURRENT

<p>Development environment for AI/ML</p>

COPY-PASTE FIX

envd is a specialized tool for creating reproducible, container-based development environments specifically optimized for AI/ML workflows, offering a higher-level abstraction than general-purpose tools like Docker or Nix.

mediumtopics#2

Add more specific ML environment topics

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, ml-environments, reproducible-ml, cuda-environments, gpu-development, data-science-environments

mediumreadme#3

Add a 'Why envd?' or 'Comparison' section to the README

Why:

COPY-PASTE FIX

## Why envd? (Compared to Docker, Nix, or Conda)

envd provides a higher-level, ML-centric abstraction for defining and building reproducible development environments. While tools like Docker offer general containerization, Nix provides declarative system configurations, and Conda manages Python packages, envd is specifically designed to simplify the complexities of AI/ML dependency management, CUDA setup, and environment sharing with a Python-native declaration.

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

Of all named tools, what % are you?

Top rival

Docker

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Docker · recommended 2×
NixOS/nix · recommended 1×
conda/conda · recommended 1×
python-poetry/poetry · recommended 1×
mlflow/mlflow · recommended 1×

CATEGORY QUERY
How to create reproducible containerized development environments for machine learning projects?
you: not recommended
AI recommended (in order):
1. Docker
2. Nix/NixOS (NixOS/nix)
3. Conda (conda/conda)
4. Poetry (python-poetry/poetry)
5. MLflow (mlflow/mlflow)
6. Pachyderm (pachyderm/pachyderm)
7. Dev Containers (microsoft/vscode-remote-release)
AI recommended 7 alternatives but never named tensorchord/envd. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Tool to simplify setting up complex AI/ML development environments with various dependencies?
you: not recommended
AI recommended (in order):
1. Conda
2. Docker
3. Poetry
4. Nix
5. Dev Containers
6. Virtualenv
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?

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MARKDOWN (README)

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Pro

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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