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Agent skill

Classical Estimators

by VectorSpaceLab in VectorSpaceLab/AREX-Skill

Use this sub-skill for MLAlgorithms supervised tabular estimators: linear/logistic regression, KNN, Naive Bayes, SVM kernels, random forests, gradient boosting, and experimental factorization…

MITAuto-check passedData & Analytics

Install Classical Estimators

skills CLI
$ npx skills add VectorSpaceLab/AREX-Skill --skill classical-estimators -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install VectorSpaceLab/AREX-Skill classical-estimators --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/ml-algorithms/sub-skills/classical-estimators .claude/skills/classical-estimators && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
classical-estimators
GitHub stars
328
Token cost
~1.2k tokens
SKILL.md length
435 words
Files
5 (incl. scripts, references)
Skills in repo
157
Repo updated
First seen
Licence
MIT

At a glance

Use this sub-skill for MLAlgorithms supervised tabular estimators: linear/logistic regression, KNN, Naive Bayes, SVM kernels, random forests, gradient boosting, and experimental factorization…

  • Works in 5 steps: Confirm the environment imports the… → Pick the estimator family from the data… → Convert inputs to NumPy arrays before… → …
  • Tasks that involve Machine learning
  • SKILL.md covers Route elsewhere, Start here, Primary workflows and Bundled references and helpers
  • Runs Python scripts from its folder; calls python

What it does

Classical Estimators is an agent skill from VectorSpaceLab/AREX-Skill. Use this sub-skill for MLAlgorithms supervised tabular estimators: linear/logistic regression, KNN, Naive Bayes, SVM kernels, random forests, gradient boosting, and experimental factorization machines.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/troubleshooting.md` and `references/workflows.md`).

It sits in Data & Analytics, covering Machine learning. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is MIT.

When your agent uses it

  • Tasks that involve Machine learning

Example prompts

  • “/classical-estimators”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Confirm the environment imports the package and scientific stack
  2. Pick the estimator family from the data and output shape
  3. Convert inputs to NumPy arrays before calling fit. The shared BaseEstimator converts array-like inputs, but explicit arrays make shapes…
  4. After fit, call predict(X_test) and validate with mla.metrics or an external metric such as sklearn.metrics.roc_auc_score.
  5. For a deterministic smoke check, run scripts/run_classical_smoke.py from this sub-skill. It uses small synthetic datasets and does not…

What it can do on your machine

Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Classical Estimators loads about 1.2k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 435 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its MIT licence (© VectorSpaceLab). 435 words, ~1,215 tokens.

Download SKILL.mdSave it as .claude/skills/classical-estimators/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
classical-estimators
description
Use this sub-skill for MLAlgorithms supervised tabular estimators: linear/logistic regression, KNN, Naive Bayes, SVM kernels, random forests, gradient boosting, and experimental factorization machines.
disable-model-invocation
true
metadata.disco-role
operating
license
MIT

Classical Estimators

Use this sub-skill when a task asks for MLAlgorithms' CPU-only supervised learning APIs, estimator selection, fit/predict examples, or debugging for tabular models. The package exposes scikit-learn-like classes but is intentionally minimal and educational; prefer explicit NumPy arrays, small datasets, and direct metric checks.

Route elsewhere

  • Clustering, Gaussian mixtures, PCA, t-SNE, RBM, and package demo dataset loaders belong in ../unsupervised-and-reduction/SKILL.md.
  • The custom NeuralNet stack, MLP/CNN/RNN/LSTM recipes, optimizers, activations, and DQN belong in ../neural-network-building-blocks/SKILL.md.
  • Repo-wide install/import checks, package compatibility, provenance, and cross-cutting metrics are summarized in the root skill.

Start here

  1. Confirm the environment imports the package and scientific stack:

    bash
    python - <<'PY'
import mla, numpy, scipy, sklearn, autograd
from mla.linear_models import LinearRegression, LogisticRegression
print("mla import ok")
PY

  2. Pick the estimator family from the data and output shape:

    • Continuous targets: LinearRegression, KNNRegressor, RandomForestRegressor, GradientBoostingRegressor; treat FMRegressor as experimental in this version.
    • Binary class labels {0, 1}: LogisticRegression, NaiveBayesClassifier, tree/boosting classifiers, KNN.
    • SVM: convert labels to {-1, 1} before fitting.

  3. Convert inputs to NumPy arrays before calling fit. The shared BaseEstimator converts array-like inputs, but explicit arrays make shapes and dtype errors easier to diagnose.

  4. After fit, call predict(X_test) and validate with mla.metrics or an external metric such as sklearn.metrics.roc_auc_score.

  5. For a deterministic smoke check, run scripts/run_classical_smoke.py from this sub-skill. It uses small synthetic datasets and does not read original repository examples.

Primary workflows

Linear and logistic regression

Use mla.linear_models.LinearRegression for continuous targets and LogisticRegression for binary probabilities. Both use gradient descent, so lr, max_iters, tolerance, penalty, and C directly affect convergence.

python
import numpy as np
from mla.linear_models import LogisticRegression
from mla.metrics.metrics import accuracy

X = np.asarray([[0.0], [0.2], [1.0], [1.2]])
y = np.asarray([0, 0, 1, 1])
model = LogisticRegression(lr=0.01, max_iters=200, penalty="l1", C=0.01)
model.fit(X, y)
proba = model.predict(np.asarray([[0.1], [1.1]]))
labels = (proba >= 0.5).astype(int)
K-nearest neighbors

Use KNNClassifier or KNNRegressor for simple instance-based baselines. The default distance function is scipy.spatial.distance.euclidean; pass another two-argument distance callable when needed. Set k=0 to use all training examples.

Show full SKILL.md (167 more words)Show less
Naive Bayes

NaiveBayesClassifier implements Gaussian Naive Bayes for binary labels exactly [0, 1]. It returns normalized class-probability rows. Avoid constant-variance features unless you add preprocessing, because the Gaussian PDF divides by per-class variance.

SVM kernels

Import kernels from the repository's real module spelling, mla.svm.kernerls:

python
from mla.svm.svm import SVM
from mla.svm.kernerls import Linear, Poly, RBF

signed_y = (binary_y * 2) - 1
model = SVM(C=0.6, kernel=RBF(gamma=0.05), max_iter=200)
model.fit(X_train, signed_y)
pred = model.predict(X_test)
Tree ensembles and boosting

Use RandomForestClassifier/Regressor for bagged decision trees and GradientBoostingClassifier/Regressor for additive trees. Keep max_features less than the number of columns when you pass it explicitly.

Factorization machines

FMRegressor and FMClassifier exist in the package and expose the BaseFM constructor, but the current source initializes loss functions after the base training call. Treat these classes as experimental and verify a focused smoke before relying on fit.

Bundled references and helpers

  • Read references/api-reference.md for import paths, signatures, outputs, and version-specific caveats.
  • Read references/workflows.md for supervised recipes and model-family selection guidance.
  • Read references/troubleshooting.md when fitting fails, scores are unstable, labels have the wrong encoding, or dependency versions drift.
  • Run scripts/run_classical_smoke.py --workflow all to test a small installed-package baseline without original examples or downloads.

© VectorSpaceLab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/ml-algorithms/sub-skills/classical-estimators of VectorSpaceLab/AREX-Skill.

  • SKILL.md
  • references/api-reference.md
  • references/troubleshooting.md
  • references/workflows.md
  • scripts/run_classical_smoke.py

Open the folder on GitHubat commit ac3fe1a

Compare with similar skills

Classical Estimators next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Classical Estimators compared with similar skills
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Senior Data ScientistRaidriar7170/hermes-skilleval1256 repos~1.4kAutomated safety check: PassMIT
Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill188—~4kAutomated safety check: PassMIT
Retention Analysisliangdabiao/claude-data-analysis-ultra-main2901 repos~1.3kAutomated safety check: NotesNone
Geomlitalo-goncalves/geoML109—~4.2kAutomated safety check: PassGPL-3.0

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Categories

Questions about Classical Estimators

What does Classical Estimators do?

Use this sub-skill for MLAlgorithms supervised tabular estimators: linear/logistic regression, KNN, Naive Bayes, SVM kernels, random forests, gradient boosting, and experimental factorization…. Classical Estimators is an agent skill from VectorSpaceLab/AREX-Skill. Use this sub-skill for MLAlgorithms supervised tabular estimators: linear/logistic regression, KNN, Naive Bayes, SVM kernels, random forests, gradient boosting, and experimental factorization machines.

When should I use Classical Estimators?

Classical Estimators fits situations like: tasks that involve Machine learning.

How do I install Classical Estimators in Claude Code?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill classical-estimators -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/ml-algorithms/sub-skills/classical-estimators in VectorSpaceLab/AREX-Skill) into .claude/skills/classical-estimators in your project. Claude Code loads it when a task matches its description.

How do I install Classical Estimators in Codex?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill classical-estimators -a codex`. Or copy the skill folder (skills/repositories/repo-skills/ml-algorithms/sub-skills/classical-estimators in VectorSpaceLab/AREX-Skill) into .agents/skills/classical-estimators in your project. Codex loads it when a task matches its description.

Can I use Classical Estimators in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill classical-estimators -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/classical-estimators, .gemini/skills/classical-estimators, .github/skills/classical-estimators and .opencode/skills/classical-estimators in your project.

What does Classical Estimators need to run?

Going by SKILL.md and its folder, Classical Estimators needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Classical Estimators access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Classical Estimators safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Classical Estimators use?

Classical Estimators is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Classical Estimators use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4k tokens, read only when the agent opens those files.

What are the alternatives to Classical Estimators?

Skills that share tags, products or a category with Classical Estimators: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars) and Retention Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Classical Estimators?

VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 157 skills in this directory. The repository was last updated on September 3, 2026.

Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.