Agent skill

Pca Dimensionality Reduction

by aipoch in aipoch/medical-research-skills

A skill your agent uses when performing PCA principal component dimensionality reduction on tabular numeric data.

Install Pca Dimensionality Reduction

skills CLI

$ npx skills add aipoch/medical-research-skills --skill pca-dimensionality-reduction -a claude-code

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

GitHub CLI

$ gh skill install aipoch/medical-research-skills pca-dimensionality-reduction --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction' .claude/skills/pca-dimensionality-reduction && 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/

Paste into Claude Code

Install the "pca-dimensionality-reduction" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/pca-dimensionality-reduction into .claude/skills/pca-dimensionality-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pca-dimensionality-reduction", 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

$skill-installer install https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/pca-dimensionality-reduction

Type 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.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill pca-dimensionality-reduction -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills pca-dimensionality-reduction --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction' .agents/skills/pca-dimensionality-reduction && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "pca-dimensionality-reduction" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/pca-dimensionality-reduction into .agents/skills/pca-dimensionality-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pca-dimensionality-reduction", 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.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill pca-dimensionality-reduction -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills pca-dimensionality-reduction --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction' .cursor/skills/pca-dimensionality-reduction && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "pca-dimensionality-reduction" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/pca-dimensionality-reduction into .cursor/skills/pca-dimensionality-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pca-dimensionality-reduction", 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

$ gemini skills install https://github.com/aipoch/medical-research-skills.git --path 'awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction'

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill pca-dimensionality-reduction -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills pca-dimensionality-reduction --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction' .gemini/skills/pca-dimensionality-reduction && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "pca-dimensionality-reduction" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/pca-dimensionality-reduction into .gemini/skills/pca-dimensionality-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pca-dimensionality-reduction", 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.

GitHub CLI

$ gh skill install aipoch/medical-research-skills pca-dimensionality-reduction

Installs 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).

skills CLI

$ npx skills add aipoch/medical-research-skills --skill pca-dimensionality-reduction -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction' .github/skills/pca-dimensionality-reduction && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "pca-dimensionality-reduction" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/pca-dimensionality-reduction into .github/skills/pca-dimensionality-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pca-dimensionality-reduction", 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.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill pca-dimensionality-reduction -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills pca-dimensionality-reduction --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction' .opencode/skills/pca-dimensionality-reduction && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "pca-dimensionality-reduction" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/pca-dimensionality-reduction into .opencode/skills/pca-dimensionality-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pca-dimensionality-reduction", 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: pca-dimensionality-reduction
GitHub stars: 2k
Token cost: ~1.5k tokens
SKILL.md length: 499 words
Files: 12 (incl. scripts, references)
Skills in repo: 567
Repo updated: Sep 17, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when performing PCA principal component dimensionality reduction on tabular numeric data.

Works in 3 steps: Confirm the input file exists and… → Run scripts/main.R with the requested… → Check the output directory for result…
Performing PCA principal component dimensionality reduction on tabular numeric data
SKILL.md covers Use This Skill When, Primary Command, Prerequisites and Core Arguments, plus 8 more sections
Runs R scripts from its folder; reaches cloud.r-project.org

What it does

Pca Dimensionality Reduction is an agent skill from aipoch/medical-research-skills. Use when performing PCA principal component dimensionality reduction on tabular numeric data. Supports command-line parameter input, automatic numeric feature selection, parameter validation, result directory creation, and CSV or TXT format result export.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts and reference files (for example `eval_report_pca-dimensionality-reduction_result.json`, `references/algorithm.md` and `references/cli-guide.md`).

It sits in Data & Analytics, covering CSV and tabular files and Machine learning. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

Performing PCA principal component dimensionality reduction on tabular numeric data
Tasks that involve CSV and tabular files
Tasks that involve Machine learning

Example prompts

“/pca-dimensionality-reduction”

Workflow steps

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

Confirm the input file exists and identify the numeric feature columns for PCA.
Run scripts/main.R with the requested output directory and optional feature, ID, or group columns.
Check the output directory for result files under table/, data/, and figure/.

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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 4 files in scripts/ (R), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- cloud.r-project.org
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

Pca Dimensionality Reduction loads about 1.5k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 499 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~71
When it runs · the whole SKILL.md, loaded when a task matches: ~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~4.5k

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 499 words, ~1,503 tokens.

Download SKILL.mdSave it as .claude/skills/pca-dimensionality-reduction/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.

name: pca-dimensionality-reduction
description: Use when performing PCA principal component dimensionality reduction on tabular numeric data. Supports command-line parameter input, automatic numeric feature selection, parameter validation, result directory creation, and CSV or TXT format result export.
license: MIT
author: AIPOCH

Source: https://github.com/aipoch/medical-research-skills

PCA Dimensionality Reduction Analysis

Use this skill to run principal component analysis on a tabular dataset and export explained variance, sample scores, feature loadings, and diagnostic figures.

Use This Skill When

You need to reduce multiple numeric variables into a smaller set of principal components.
You need a command-line PCA workflow with parameter validation.
You need standardized output files for downstream analysis.

Primary Command

bash

Rscript scripts/main.R \
  --data_file <input_file> \
  --output_dir <output_dir> \
  --feature_columns <comma_separated_numeric_columns>

Prerequisites

Rscript is available in the shell.
Required R packages: optparse, data.table.
Install missing packages with Rscript -e 'install.packages(c("optparse", "data.table"), repos="https://cloud.r-project.org")'.

Core Arguments

Argument	Required	Description
`--data_file`	Yes	Input data file in CSV, TXT, or TSV format
`--output_dir`	No	Output directory, default `./PCA_Results`
`--feature_columns`	No	Comma-separated numeric feature columns. Default uses all numeric columns except ID/group columns
`--sample_id_column`	No	Optional sample ID column. If omitted and the first column is non-numeric with unique values, it is used automatically
`--group_column`	No	Optional grouping column to carry into score output and score plot
`--n_components`	No	Maximum number of principal components to export, default `5`
`--center_data`	No	`true` or `false`, default `true`
`--scale_data`	No	`true` or `false`, default `true`
`--top_loadings`	No	Number of top absolute loadings to export per component, default `10`
`--output_format`	No	`csv` or `txt`, default `csv`
`--output_prefix`	No	Output filename prefix, default `pca`

Input Requirements

The input file must contain at least 2 usable numeric feature columns.
PCA is run on rows as samples and columns as features.
Missing or non-finite values in selected feature columns are removed row-wise before analysis.
At least 2 complete samples must remain after filtering.
Selected feature columns must have non-zero variance after filtering.

Example input:

csv

SampleID,Group,GeneA,GeneB,GeneC,GeneD
S01,Control,2.1,1.9,8.2,4.3
S02,Control,2.4,2.2,8.0,4.6
S03,Treated,6.1,5.7,2.8,8.1

Minimal Workflow

Confirm the input file exists and identify the numeric feature columns for PCA.
Run scripts/main.R with the requested output directory and optional feature, ID, or group columns.
Check the output directory for result files under table/, data/, and figure/.

If you omit --data_file, the script exits with SKILL_MISSING_INPUT.

Show full SKILL.md (179 more words)Show less

Outputs

Expected output structure:

text

<output_dir>/
├── table/
├── figure/
└── data/

Primary result files:

table/<output_prefix>_summary.csv
table/<output_prefix>_scores.csv
table/<output_prefix>_loadings.csv
table/<output_prefix>_top_loadings.csv

Figure files:

figure/<output_prefix>_scree_plot.png
figure/<output_prefix>_score_plot.png

Key fields include:

component
standard_deviation
variance
proportion_variance
cumulative_variance
sample_id
feature
loading

Interpretation Guide

Use proportion_variance and cumulative_variance to decide how many components to retain.
Use the score table to inspect sample separation in PC space.
Use the loading tables to identify which original variables drive each component.

Read These Files When Needed

Need	File
PCA method details and interpretation	`references/algorithm.md`
More CLI examples	`references/cli-guide.md`
Error diagnosis	`references/troubleshooting.md`
Main execution entry point	`scripts/main.R`
Sample test data	`tests/data/`

Quick Examples

Basic PCA with explicit feature columns:

bash

Rscript scripts/main.R \
  --data_file tests/data/sample_pca_1.csv \
  --sample_id_column SampleID \
  --group_column Group \
  --feature_columns GeneA,GeneB,GeneC,GeneD,GeneE \
  --output_dir tests/output_basic

Auto-detect all numeric columns:

bash

Rscript scripts/main.R \
  --data_file tests/data/sample_pca_2.csv \
  --n_components 3 \
  --output_dir tests/output_numeric_only

Disable scaling:

bash

Rscript scripts/main.R \
  --data_file tests/data/sample_pca_1.csv \
  --sample_id_column SampleID \
  --group_column Group \
  --scale_data false \
  --output_dir tests/output_unscaled

Validation

bash

Rscript scripts/main.R --help

bash

Rscript scripts/main.R \
  --data_file tests/data/sample_pca_1.csv \
  --sample_id_column SampleID \
  --group_column Group \
  --feature_columns GeneA,GeneB,GeneC,GeneD,GeneE \
  --output_dir tests/validation_output

After running analysis, verify that tests/validation_output/table/pca_summary.csv exists.

Common Errors

SKILL_FILE_NOT_FOUND: Input file path is wrong or inaccessible.
SKILL_MISSING_COLUMNS: A requested feature, sample ID, or group column is missing.
SKILL_INVALID_DATA: Input data is malformed or unsuitable for PCA.
SKILL_INVALID_PARAMETER: An argument value is invalid.
SKILL_INSUFFICIENT_DATA: Too few complete samples or features remain for PCA.
SKILL_DEPENDENCY_MISSING: A required R package such as optparse or data.table is unavailable.

If the issue is not obvious, read references/troubleshooting.md.

Files

SKILL.md and 11 other files (scripts, references) in awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction of aipoch/medical-research-skills.

SKILL.md
eval_report_pca-dimensionality-reduction_result.json
references/algorithm.md
references/cli-guide.md
references/troubleshooting.md
scripts/functions.R
scripts/main.R
scripts/run_analysis.R
scripts/utils.R
tests/data/sample_pca_1.csv
tests/data/sample_pca_2.csv
tests/data/sample_pca_3.csv

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Pca Dimensionality Reduction 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.

Pca Dimensionality Reduction compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Pca Dimensionality Reduction this skillaipoch/medical-research-skills	2k	—	~1.5k	Automated safety check: Pass	MIT	21 days ago
Flowiodavila7/claude-code-templates	32k	10 repos	~4.2k	Automated safety check: Pass	MIT	today
Splitting Datasetsjeremylongshore/tons-of-skills-marketplace	2.8k	1 repos	~836	Automated safety check: Pass	MIT	today
Unimoljinzhezenggroup/computational-chemistry-agent-skills	148	1 repos	~1.5k	Automated safety check: Pass	LGPL-3.0-or-later	2 days ago
Excel and CSV Data Analysisbytedance/deer-flow	83k	4 repos	~2.2k	Automated safety check: Pass	MIT	today
Exploratory Data AnalysisOleafly/Oleafly	206	3 repos	~3.4k	Automated safety check: Notes	MIT	today

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Questions about Pca Dimensionality Reduction

What does Pca Dimensionality Reduction do?

A skill your agent uses when performing PCA principal component dimensionality reduction on tabular numeric data. Pca Dimensionality Reduction is an agent skill from aipoch/medical-research-skills. Use when performing PCA principal component dimensionality reduction on tabular numeric data.

When should I use Pca Dimensionality Reduction?

Pca Dimensionality Reduction fits situations like: performing PCA principal component dimensionality reduction on tabular numeric data; tasks that involve CSV and tabular files; tasks that involve Machine learning.

How do I install Pca Dimensionality Reduction in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill pca-dimensionality-reduction -a claude-code`. Or copy the skill folder (awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction in aipoch/medical-research-skills) into .claude/skills/pca-dimensionality-reduction in your project. Claude Code loads it when a task matches its description.

How do I install Pca Dimensionality Reduction in Codex?

Run `npx skills add aipoch/medical-research-skills --skill pca-dimensionality-reduction -a codex`. Or copy the skill folder (awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction in aipoch/medical-research-skills) into .agents/skills/pca-dimensionality-reduction in your project. Codex loads it when a task matches its description.

Can I use Pca Dimensionality Reduction 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 aipoch/medical-research-skills --skill pca-dimensionality-reduction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pca-dimensionality-reduction, .gemini/skills/pca-dimensionality-reduction, .github/skills/pca-dimensionality-reduction and .opencode/skills/pca-dimensionality-reduction in your project.

What does Pca Dimensionality Reduction need to run?

Going by SKILL.md and its folder, Pca Dimensionality Reduction needs R for the scripts in its folder.

Does Pca Dimensionality Reduction access the network?

SKILL.md names 1 domain. In commands or code: cloud.r-project.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Pca Dimensionality Reduction 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 Pca Dimensionality Reduction use?

Pca Dimensionality Reduction 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 Pca Dimensionality Reduction use?

About 1.5k tokens (SKILL.md is roughly 6k 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 3k tokens, read only when the agent opens those files.

What are the alternatives to Pca Dimensionality Reduction?

Skills that share tags, products or a category with Pca Dimensionality Reduction: Flowio (davila7/claude-code-templates, 32k stars), Splitting Datasets (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Unimol (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars) and Excel and CSV Data Analysis (bytedance/deer-flow, 83k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pca Dimensionality Reduction?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Pca Dimensionality Reduction

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

PCA Dimensionality Reduction Analysis

Use This Skill When

Primary Command

Prerequisites

Core Arguments

Input Requirements

Minimal Workflow

Outputs

Interpretation Guide

Read These Files When Needed

Quick Examples

Validation

Common Errors

Files

Compare with similar skills

Similar skills

More from aipoch/medical-research-skills

Related

Questions about Pca Dimensionality Reduction