statistics-math

Mathematical foundations for data science, machine learning, and statistical analysis.

pluginagentmarketplace/custom-plugin-data-engineer|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

Installation Guide

Select your AI agent

How to use statistics-math on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add statistics-math

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/pluginagentmarketplace/custom-plugin-data-engineer --skill statistics-math

Fetches statistics-math from pluginagentmarketplace/custom-plugin-data-engineer and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/statistics-math

Restart Cursor to activate statistics-math. Access via /statistics-math in your agent's command palette.

⚠

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

List & Monetize Your Skill

Submit your Claude Code skill and start earning

Get started →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Quick Start

import numpy as np import scipy.stats as stats from sklearn.linear_model import LinearRegression # Descriptive Statistics data = np.array([23, 45, 67, 32, 45, 67, 89, 12, 34, 56]) print(f"Mean: {np.mean(data):.2f}") print(f"Median: {np.median(data):.2f}") print(f"Std Dev: {np.std(data, ddof=1):.2f}") print(f"IQR: {np.percentile(data, 75) - np.percentile(data, 25):.2f}") # Hypothesis Testing sample_a = [23, 45, 67, 32, 45] sample_b = [56, 78, 45, 67, 89] t_stat, p_value = stats.ttest_ind(sample_a, sample_b) print(f"T-statistic: {t_stat:.4f}, p-value: {p_value:.4f}") if p_value < 0.05: print("Reject null hypothesis: significant difference") else: print("Fail to reject null hypothesis")

Core Concepts

1. Probability Distributions

import numpy as np import scipy.stats as stats import matplotlib.pyplot as plt # Normal Distribution mu, sigma = 100, 15 normal_dist = stats.norm(loc=mu, scale=sigma) x = np.linspace(50, 150, 100) # PDF, CDF calculations print(f"P(X < 85): {normal_dist.cdf(85):.4f}") print(f"P(X > 115): {1 - normal_dist.cdf(115):.4f}") print(f"95th percentile: {normal_dist.ppf(0.95):.2f}") # Binomial Distribution (discrete) n, p = 100, 0.3 binom_dist = stats.binom(n=n, p=p) print(f"P(X = 30): {binom_dist.pmf(30):.4f}") print(f"P(X <= 30): {binom_dist.cdf(30):.4f}") # Poisson Distribution (events per time) lambda_param = 5 poisson_dist = stats.poisson(mu=lambda_param) print(f"P(X = 3): {poisson_dist.pmf(3):.4f}") # Central Limit Theorem demonstration population = np.random.exponential(scale=10, size=100000) sample_means = [np.mean(np.random.choice(population, 30)) for _ in range(1000)] print(f"Sample means are approximately normal: mean={np.mean(sample_means):.2f}")

2. Hypothesis Testing Framework

from scipy import stats import numpy as np class HypothesisTest: """Framework for statistical hypothesis testing.""" @staticmethod def two_sample_ttest(group_a, group_b, alpha=0.05): """Independent samples t-test.""" t_stat, p_value = stats.ttest_ind(group_a, group_b) effect_size = (np.mean(group_a) - np.mean(group_b)) / np.sqrt( (np.var(group_a) + np.var(group_b)) / 2 ) return { "t_statistic": t_stat, "p_value": p_value, "significant": p_value < alpha, "effect_size_cohens_d": effect_size } @staticmethod def chi_square_test(observed, expected=None, alpha=0.05): """Chi-square test for categorical data.""" if expected is None: chi2, p_value, dof, expected = stats.chi2_contingency(observed) else: chi2, p_value = stats.chisquare(observed, expected) dof = len(observed) - 1 return { "chi2_statistic": chi2, "p_value": p_value, "degrees_of_freedom": dof, "significant": p_value < alpha } @staticmethod def ab_test_proportion(conversions_a<

statistics-math

Installation Guide

How to use statistics-math on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

User Story & Requirements Generation

Competitive Analysis

Roadmap Prioritization

Install Skill

Statistics & Mathematics

Quick Start

Core Concepts

1. Probability Distributions

2. Hypothesis Testing Framework

`Stakeholder Communication`

`Implementation Guide`

`Best Practices`

`When to Use This`

`Learning Path`

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`Reviews`

`Discussion`