pca-decomposition SKILL.md skillA
pca-decomposition 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/pca-decomposition@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/pca-decomposition/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260610.e2a435f, committed Jun 10, 2026
- Size
- 3,961 bytes
- Repository stars
- 359
- License
- Apache-2.0
Reduce dimensionality of multivariate data using PCA with varimax rotation. Use when you have many correlated variables and need to identify underlying factors or reduce collinearity.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# PCA Decomposition Guide ## Overview Principal Component Analysis (PCA) reduces many correlated variables into fewer uncorrelated components. Varimax rotation makes components more interpretable by maximizing variance. ## When to Use PCA - Many correlated predictor variables - Need to identify underlying factor groups - Reduce multicollinearity before regression - Exploratory data analysis ## Basic PCA with Varimax Rotation ```python from sklearn.preprocessing import StandardScaler from factor_analyzer import FactorAnalyzer # Standardize data first scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # PCA with varimax rotation fa = FactorAnalyzer(n_factors=4, rotation='varimax') fa.fit(X_scaled) # Get factor loadings loadings = fa.loadings_ # Get component scores for each observation scores = fa.transform(X_scaled) ``` ## Workflow for Attribution Analysis When using PCA for contribution analysis with predefined categories: 1. **Combine ALL variables first**, then do PCA together: ```python # Include all variables from all categories in one matrix all_vars = ['AirTemp', 'NetRadiation', 'Precip', 'Inflow', 'Outflow', …
Read the whole file at its exact version.
How to install
mdr add benchflow-ai/benchflow/pca-decomposition@sha256:651fe7e01b2bc9abThe 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_psy3kwfbtc3ebcmv)
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Versions
The registry has recorded 1 version of this file, committed on Jun 10, 2026.
Audit of the latest version
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- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
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- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
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- pass: No curl or wget piped into a shell
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Source
benchflow-ai/benchflow · 359 stars · license Apache-2.0 · pushed 2026-10-30 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_psy3kwfbtc3ebcmv GET https://markdownregistry.com/api/v1/resolve?ref=benchflow-ai/benchflow/pca-decomposition GET https://markdownregistry.com/api/v1/blob/651fe7e01b2bc9abcf21a51d213398ef27c225ad2ea1a49b5bd681e934015ac2
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.