contribution-analysis SKILL.md skillA
contribution-analysis from benchflow-ai/benchflow is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add benchflow-ai/benchflow/contribution-analysis@git:20260610.e2a435fPins git:20260610.e2a435f in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- benchflow-ai/benchflow
- Path
tests/fixtures/skillsbench_slice/lake-warming-attribution/environment/skills/contribution-analysis/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260610.e2a435f, committed Jun 10, 2026
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- 2,959 bytes
- Repository stars
- 359
- License
- Apache-2.0
Calculate the relative contribution of different factors to a response variable using R² decomposition. Use when you need to quantify how much each factor explains the variance of an outcome.
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What the file says
# Contribution Analysis Guide
## Overview
Contribution analysis quantifies how much each factor contributes to explaining the variance of a response variable. This skill focuses on R² decomposition method.
## Complete Workflow
When you have multiple correlated variables that belong to different categories:
```python
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from factor_analyzer import FactorAnalyzer
# Step 1: Combine ALL variables into one matrix
pca_vars = ['Var1', 'Var2', 'Var3', 'Var4', 'Var5', 'Var6', 'Var7', 'Var8']
X = df[pca_vars].values
y = df['ResponseVariable'].values
# Step 2: Standardize
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Step 3: Run ONE global PCA on all variables together
fa = FactorAnalyzer(n_factors=4, rotation='varimax')
fa.fit(X_scaled)
scores = fa.transform(X_scaled)
# Step 4: R² decomposition on factor scores
def calc_r2(X, y):
model = LinearRegression()
model.fit(X, y)
y_pred = model.predict(X)
ss_res = np.sum((y - y_pred) ** 2)
ss_tot = np.sum((y - np.mean(y)) ** 2)
return 1 - (ss_res / ss_tot)
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How to install
mdr add benchflow-ai/benchflow/contribution-analysis@sha256:ec9531b6b06a4e7aThe line at the top of this page pins the label git:20260610.e2a435f, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_baouvfnqmzfeo64w)
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Versions
The registry has recorded 1 version of this file, committed on Jun 10, 2026.
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Source
benchflow-ai/benchflow · 359 stars · license Apache-2.0 · pushed 2026-10-30 · branch main
API
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