relf/egobox/egobox-gpx-cli

Name: egobox-gpx-cli
Author: relf

Claude Code and agent skill by relf/egobox

Trust tier 3

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Use this skill whenever the user is working with the `gpx` command-line tool from the EGObox project. Triggers on any mention of: the gpx CLI, `gpx fit`, `gpx predict`, `gpx qa`, `gpx spec`, `gpx py`, training a surrogate model from CSV or npy files on the command line, assessing GP model quality from the terminal, generating a Python helper from a saved .gpx/.bin/.json model, or using gpx to run inference on tabular data without writing Python code. Also use when the user wants to inspect or u…

Install

npx vskill@latest install relf/egobox/egobox-gpx-cli

SKILL.md

EGObox `gpx` CLI Skill

gpx is a standalone command-line tool bundled with the egobox Python package (≥ 0.37.6). It lets you fit, inspect, evaluate and run GP surrogate models entirely from the shell — no Python code required.

Installation

gpx ships with the Python package:

bash

pip install egobox          # gpx binary lands in PATH automatically
gpx --version               # gpx 0.5.0

Alternatively, install the standalone binary directly:

bash

# macOS (Homebrew)
brew install relf/tap/gpx
 
# Linux / macOS (shell installer)
curl --proto '=https' --tlsv1.2 -LsSf \
  https://github.com/relf/egobox/releases/download/gpx-0.4.0/gpx-installer.sh | sh
 
# Windows (PowerShell)
powershell -ExecutionPolicy Bypass -c \
  "irm https://github.com/relf/egobox/releases/download/gpx-0.4.0/gpx-installer.ps1 | iex"

Subcommands Overview

Subcommand	Purpose
`fit`	Train a GP surrogate from a CSV or npy data file
`spec`	Inspect a saved model's input/output dimensions and configuration
`qa`	Assess predictive quality (Q2, PVA, IAEalpha) via LOO or k-fold CV
`predict`	Run inference on new input samples; writes CSV or npy
`py`	Generate a self-contained Python helper script with the model embedded

Data File Formats

gpx accepts two tabular formats, auto-detected by file extension:

Format	Extension	Notes
CSV	`.csv`	One row per sample, comma-separated, optional header row (skipped)
NumPy	`.npy`	2D float64 array saved with `np.save()`

Training file layout (fit): columns are [x1, x2, ..., xn, y1, y2, ..., ym]. By default the last 1 column is treated as output; use --outputs N for multi-output.

Predict input file: columns are just inputs [x1, x2, ..., xn].

`gpx fit` — Train a Surrogate

gpx fit [OPTIONS] <INPUT>

Arguments:

<INPUT> — training data file (CSV or npy)

Key options:

Flag	Default	Description
`--outputs N`	`1`	Number of output columns taken from the end of each row
`--regression-spec`	`constant`	`constant`, `linear`, `quadratic`, `all`
`--correlation-spec`	`squared-exponential`	`squared-exponential`, `absolute-exponential`, `matern32`, `matern52`, `all`
`--kpls-dim N`	(none)	PLS dimension reduction to N components; recommended when input dim ≥ 9
`--n-clusters N`	`1`	Number of local expert clusters (mixture of GPs)
`--recombination`	`smooth`	`smooth` or `hard` — how experts are combined
`-o, --output FILE`	`surrogate_model.gpx`	Output model file path

Examples:

bash

# Minimal — single output, default settings
gpx fit data.csv
 
# Multi-output with Matérn 5/2 kernel
gpx fit data.csv --outputs 2 --correlation-spec matern52 -o model.gpx
 
# High-dimensional input with PLS reduction
gpx fit data.csv --kpls-dim 3 -o model_kpls.gpx
 
# Try all regression × correlation combinations (cross-validation picks best)
gpx fit data.csv --regression-spec all --correlation-spec all -o model_best.gpx
 
# Mixture of 3 local experts
gpx fit data.csv --n-clusters 3 --recombination smooth -o model_moe.gpx

`gpx spec` — Inspect a Model

gpx spec [-m FILE] [-i INDEX]

Shows: surrogate type, input/output dimensions, supported I/O formats, expected array shapes, training data summary.

bash

gpx spec                              # inspect surrogate_model.gpx
gpx spec -m my_model.gpx
gpx spec -m model.gpx -i 0           # single surrogate in a multi-output file

Model file sources — gpx can read models saved by:

gpx fit → .gpx binary
Python egx.Gpx.save("model.json") → .json
Egor optimizer outdir → egor_gp.bin, egor_initial_gp.bin

`gpx qa` — Quality Assessment

gpx qa [-m FILE] [-i INDEX] [-k KFOLD]

Reports three metrics (computed on training data via leave-one-out by default, or k-fold):

Metric	Goal	Meaning
Q2	Maximise, close to 1	Predictive accuracy (like R²). Q2 ≤ 0.5 is poor.
PVA	Minimise, close to 0	Predictive Variance Adequacy — are uncertainty estimates well-calibrated?
IAEalpha	Minimise, in [0, 0.5]	Interval reliability — trustworthiness of prediction intervals

Also prints an alpha/empirical coverage table (the α-PI plot).

bash

gpx qa                          # LOO cross-validation on surrogate_model.gpx
gpx qa -m model.gpx
gpx qa -m model.gpx -k 5       # 5-fold cross-validation (better for larger datasets)
gpx qa -m model.gpx -i 0       # assess only surrogate 0 of a multi-output model

`gpx predict` — Run Inference

gpx predict [OPTIONS] <INPUT>

Arguments:

<INPUT> — input samples file (CSV or npy, shape (n_samples, input_dim))

Key options:

Flag	Default	Description
`--model FILE`	`surrogate_model.gpx`	Model file
`-o, --output FILE`	`surrogate_predictions.csv`	Output file; extension sets format (`.csv` or `.npy`)
`--with-variance`	off	Also output predictive variance alongside the mean
`-m, --model-index N`	(all)	Predict only surrogate N from a multi-output model

Output layout:

Format	Columns (CSV) / Shape (npy)
CSV, no variance	`x1,...,xn, y_pred1,...,y_predm`
CSV, with variance	`x1,...,xn, y_pred1,...,y_predm, y_var1,...,y_varm`
npy, no variance	`(n_samples, n_outputs)`
npy, with variance	`(n_samples, 2 * n_outputs)` — predictions then variances interleaved

bash

# Predict from CSV, output CSV
gpx predict xtest.csv -m model.gpx -o predictions.csv
 
# Include predictive variance
gpx predict xtest.csv -m model.gpx --with-variance -o predictions.csv
 
# NumPy in, NumPy out
gpx predict xtest.npy -m model.gpx -o predictions.npy
 
# Single output from a 2-output model
gpx predict xtest.csv -m model2.gpx -i 1 -o pred_y2.csv

`gpx py` — Generate a Python Helper Script

Embeds the model as a hex-encoded string and generates a pure-Python predict() function. The generated script calls the gpx binary under the hood, so gpx must be in PATH.

gpx py [--model FILE] [-o OUTPUT_SCRIPT]

bash

gpx py --model model.gpx -o surrogate.py   # generates surrogate.py

The generated script exposes:

python

predict(x, with_variance=False, model_index=None)
# x: np.ndarray of shape (n_samples, input_dim) or (input_dim,)
# returns: np.ndarray of shape (n_samples, n_outputs)

Import and use it like any Python function — no need to manage model files separately:

python

import numpy as np
from surrogate import predict
xtest = np.array([[0.5], [1.5], [2.5]])
y = predict(xtest)
y_mean, y_var = predict(xtest, with_variance=True)

Typical Workflows

Train → Inspect → Predict

bash

# 1. Prepare training data as CSV: [inputs | outputs]
#    e.g. data.csv: x1,x2,y
 
# 2. Fit
gpx fit data.csv --correlation-spec matern52 -o model.gpx
 
# 3. Check model spec
gpx spec --model model.gpx
 
# 4. Assess quality
gpx qa --model model.gpx -k 5
 
# 5. Predict new points
gpx predict xtest.csv --model model.gpx --with-variance -o results.csv

Inspect a Model from Egor Optimizer

When Egor is run with outdir, it saves GP models alongside the DOE:

bash

# After: egx.Egor(..., outdir="./run").minimize(f, ...)
gpx spec --model ./run/egor_gp.bin
gpx predict xtest.csv --model ./run/egor_gp.bin -o predictions.csv

Export Surrogate for Deployment

bash

gpx fit data.csv -o surrogate.gpx
gpx py --model surrogate.gpx -o surrogate.py
# ship surrogate.py + gpx binary; call predict() directly in your app

Common Pitfalls

Header row in CSV: gpx skips the first row if it contains non-numeric data (auto-detected). No explicit flag needed.
Column order matters: outputs must be the last N columns. --outputs N counts from the end.
npy files must be 2D: use np.save() with an array of shape (n_samples, n_dims), not 1D.
Model file extension doesn't matter: gpx detects format by content, not extension (.gpx, .bin, .json all work).
`gpx py` needs `gpx` in PATH at runtime: the generated script shells out to the gpx binary.

Quick Reference

bash

gpx fit data.csv [-o model.gpx] [--outputs N] [--kpls-dim N] [--correlation-spec matern52]
gpx spec [-m model.gpx] [-i INDEX]
gpx qa [-m model.gpx] [-k KFOLD] [-i INDEX]
gpx predict xtest.csv [-m model.gpx] [-o out.csv] [--with-variance] [-i INDEX]
gpx py [-m model.gpx] [-o script.py]

Security

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Source

SKILL.md in the repositoryCheckingpython/skills/egobox-gpx-cli/SKILL.md

Popularity

Repository stars: 193
Repository forks: 13
7-day trend score: 26

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 20 Sep 2026, a month ago.

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Install

SKILL.md

EGObox gpx CLI Skill

Installation

Subcommands Overview

Data File Formats

gpx fit — Train a Surrogate

gpx spec — Inspect a Model

gpx qa — Quality Assessment

gpx predict — Run Inference

gpx py — Generate a Python Helper Script

Typical Workflows

Train → Inspect → Predict

Inspect a Model from Egor Optimizer

Export Surrogate for Deployment

Common Pitfalls

Quick Reference

Security

Quality evaluation

Source

Popularity

Works with

EGObox `gpx` CLI Skill

`gpx fit` — Train a Surrogate

`gpx spec` — Inspect a Model

`gpx qa` — Quality Assessment

`gpx predict` — Run Inference

`gpx py` — Generate a Python Helper Script