REPOGEO REPORT · LITE

pnnl/neuromancer

Default branch master · commit e9456ffa · scanned 6/23/2026, 12:52:02 PM

GitHub: 1,344 stars · 181 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

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 pnnl/neuromancer, 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 to emphasize framework nature and SciML focus

Why:

CURRENT

Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations (NeuroMANCER) is an open-source differentiable programming (DP) library for solving parametric constrained optimization problems, physics-informed system identification, and parametric model-based optimal control.

COPY-PASTE FIX

NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations) is an open-source **PyTorch-based framework** for **scientific machine learning (SciML)**, specifically designed for **differentiable programming (DP)** to solve complex problems like parametric constrained optimization, physics-informed system identification, and parametric model-based optimal control.

mediumreadme#2

Add explicit license clarification to README

Why:

COPY-PASTE FIX

## License
NeuroMANCER is licensed under [insert specific license name(s) here, e.g., Apache 2.0 and MIT]. Please refer to the [LICENSE.md](LICENSE.md) file for full details.

lowreadme#3

Add 'Who is this for?' section to README

Why:

COPY-PASTE FIX

## Who is NeuroMANCER for?
NeuroMANCER is designed for researchers, scientists, and engineers working in scientific machine learning, control systems, optimization, and physics-informed AI. It's particularly useful for those looking to integrate deep learning with scientific computing to solve complex parametric problems.

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 pnnl/neuromancer

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

pytorch/pytorch

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

pytorch/pytorch · recommended 1×
cvxgrp/cvxpy · recommended 1×
Gurobi · recommended 1×
CPLEX · recommended 1×
tensorflow/tensorflow · recommended 1×

CATEGORY QUERY
What framework helps solve parametric constrained optimization problems using deep learning?
you: not recommended
AI recommended (in order):
1. PyTorch (pytorch/pytorch)
2. CVXPy (cvxgrp/cvxpy)
3. Gurobi
4. CPLEX
5. TensorFlow (tensorflow/tensorflow)
6. JAX (google/jax)
7. Optax (deepmind/optax)
8. Julia (JuliaLang/julia)
9. Flux.jl (FluxML/Flux.jl)
10. JuMP.jl (jump-dev/JuMP.jl)
11. CasADi (casadi/casadi)
AI recommended 11 alternatives but never named pnnl/neuromancer. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a PyTorch library for differentiable model predictive control and system identification.
you: not recommended
AI recommended (in order):
1. DiffTaichi
2. PyTorch-MPC
3. qpth
4. CVXPY
5. CasADi
AI recommended 5 alternatives but never named pnnl/neuromancer. 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 pnnl/neuromancer?
pass
AI named pnnl/neuromancer explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts pnnl/neuromancer in production, what risks or prerequisites should they evaluate first?
pass
AI named pnnl/neuromancer 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 pnnl/neuromancer solve, and who is the primary audience?
pass
AI did not name pnnl/neuromancer — 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?

Embed your GEO score

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MARKDOWN (README)

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pnnl/neuromancer — 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