Responsive Units
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing stylesheets, component styles, and responsive behavior related to Use relative units for responsive layouts.
Calculate the relative contribution of different factors to a response variable using R² decomposition.
MITAuto-check passed
Install Contribution Analysis
$ npx skills add benchflow-ai/skillsbench --skill contribution-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench contribution-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/contribution-analysis .claude/skills/contribution-analysis && rm -rf skills-srcUse ~/.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/
Install the "contribution-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/contribution-analysis into .claude/skills/contribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contribution-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/contribution-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add benchflow-ai/skillsbench --skill contribution-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench contribution-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/contribution-analysis .agents/skills/contribution-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "contribution-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/contribution-analysis into .agents/skills/contribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contribution-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill contribution-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench contribution-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/contribution-analysis .cursor/skills/contribution-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "contribution-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/contribution-analysis into .cursor/skills/contribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contribution-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/lake-warming-attribution/environment/skills/contribution-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add benchflow-ai/skillsbench --skill contribution-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench contribution-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/contribution-analysis .gemini/skills/contribution-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "contribution-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/contribution-analysis into .gemini/skills/contribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contribution-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install benchflow-ai/skillsbench contribution-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add benchflow-ai/skillsbench --skill contribution-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/contribution-analysis .github/skills/contribution-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "contribution-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/contribution-analysis into .github/skills/contribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contribution-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill contribution-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench contribution-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/lake-warming-attribution/environment/skills/contribution-analysis .opencode/skills/contribution-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "contribution-analysis" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/lake-warming-attribution/environment/skills/contribution-analysis into .opencode/skills/contribution-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contribution-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
contribution-analysis- GitHub stars
- 1.8k
- Token cost
- ~738 tokens
- SKILL.md length
- 137 words
- Files
- 1
- Skills in repo
- 180
- Repo updated
- First seen
- Licence
- MIT
At a glance
Calculate the relative contribution of different factors to a response variable using R² decomposition.
- You need to quantify how much each factor explains the variance of an outcome
- SKILL.md covers Overview, Complete Workflow, R² Decomposition Method and Output Format, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Contribution Analysis is an agent skill from benchflow-ai/skillsbench. 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.
Its SKILL.md is about 740 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is MIT.
When your agent uses it
- You need to quantify how much each factor explains the variance of an outcome
Example prompts
- “/contribution-analysis”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 9a1f4dd. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are 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
Contribution Analysis loads about 738 tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 137 words of instructions outside code blocks.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its MIT licence (© benchflow-ai). 137 words, ~738 tokens.
Download SKILL.mdSave it as
.claude/skills/contribution-analysis/SKILL.md (or your agent's skills folder).- name
- contribution-analysis
- description
- 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.
- license
- MIT
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:
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)
full_r2 = calc_r2(scores, y)
# Step 5: Calculate contribution of each factor
contrib_0 = full_r2 - calc_r2(scores[:, [1, 2, 3]], y)
contrib_1 = full_r2 - calc_r2(scores[:, [0, 2, 3]], y)
contrib_2 = full_r2 - calc_r2(scores[:, [0, 1, 3]], y)
contrib_3 = full_r2 - calc_r2(scores[:, [0, 1, 2]], y)R² Decomposition Method
The contribution of each factor is calculated by comparing the full model R² with the R² when that factor is removed:
Contribution_i = R²_full - R²_without_iOutput Format
contributions = {
'Category1': contrib_0 * 100,
'Category2': contrib_1 * 100,
'Category3': contrib_2 * 100,
'Category4': contrib_3 * 100
}
dominant = max(contributions, key=contributions.get)
dominant_pct = round(contributions[dominant])
with open('output.csv', 'w') as f:
f.write('variable,contribution\n')
f.write(f'{dominant},{dominant_pct}\n')Common Issues
| Issue | Cause | Solution |
|---|---|---|
| Negative contribution | Suppressor effect | Check for multicollinearity |
| Contributions don't sum to R² | Normal behavior | R² decomposition is approximate |
| Very small contributions | Factor not important | May be negligible driver |
Best Practices
- Run ONE global PCA on all variables together, not separate PCA per category
- Use factor_analyzer with varimax rotation
- Map factors to category names based on loadings interpretation
- Report contribution as percentage
- Identify the dominant (largest) factor
© benchflow-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in tasks/lake-warming-attribution/environment/skills/contribution-analysis of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Contribution Analysis 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Contribution Analysis this skillbenchflow-ai/skillsbench | 1.8k | — | ~738 | Automated safety check: Pass | MIT | |
| Responsive Unitsthedaviddias/Front-End-Checklist | 74k | — | ~472 | Automated safety check: Pass | MIT | |
| Responsive Designwshobson/agents | 40k | 2 repos | ~498 | Automated safety check: Pass | MIT | |
| Contributemindfold-ai/Trellis | 15k | — | ~2.7k | Automated safety check: Pass | AGPL-3.0 | |
| Contributesuperset-sh/superset | 15k | — | ~624 | Automated safety check: Pass | Custom licence | |
| Responsive Imagesthedaviddias/Front-End-Checklist | 74k | — | ~403 | Automated safety check: Pass | MIT |
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Questions about Contribution Analysis
What does Contribution Analysis do?
Calculate the relative contribution of different factors to a response variable using R² decomposition. Contribution Analysis is an agent skill from benchflow-ai/skillsbench. Calculate the relative contribution of different factors to a response variable using R² decomposition.
When should I use Contribution Analysis?
Contribution Analysis fits situations like: you need to quantify how much each factor explains the variance of an outcome.
How do I install Contribution Analysis in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill contribution-analysis -a claude-code`. Or copy the skill folder (tasks/lake-warming-attribution/environment/skills/contribution-analysis in benchflow-ai/skillsbench) into .claude/skills/contribution-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Contribution Analysis in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill contribution-analysis -a codex`. Or copy the skill folder (tasks/lake-warming-attribution/environment/skills/contribution-analysis in benchflow-ai/skillsbench) into .agents/skills/contribution-analysis in your project. Codex loads it when a task matches its description.
Can I use Contribution Analysis 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 benchflow-ai/skillsbench --skill contribution-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/contribution-analysis, .gemini/skills/contribution-analysis, .github/skills/contribution-analysis and .opencode/skills/contribution-analysis in your project.
What does Contribution Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Contribution Analysis is instructions for the agent only. Our summary lists: Python 3.
Does Contribution Analysis 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 Contribution Analysis 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. Review the folder before installing.
What licence does Contribution Analysis use?
Contribution Analysis 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 Contribution Analysis use?
About 738 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Contribution Analysis?
Skills that share tags, products or a category with Contribution Analysis: Responsive Units (thedaviddias/Front-End-Checklist, 74k stars), Responsive Design (wshobson/agents, 40k stars), Contribute (mindfold-ai/Trellis, 15k stars) and Contribute (superset-sh/superset, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Contribution Analysis?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 180 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.