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Agent skill

Running Clustering Algorithms

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups.

MITAuto-check passedData & Analytics

Install Running Clustering Algorithms

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill running-clustering-algorithms -a claude-code

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

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace running-clustering-algorithms --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/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/running-clustering-algorithms .claude/skills/running-clustering-algorithms && 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/

Facts

Skill name
running-clustering-algorithms
GitHub stars
2.8k
Token cost
~1k tokens
SKILL.md length
458 words
Files
7 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups.

  • Works in 4 steps: Analyzing the Context: Claude analyzes… → Generating Code: Claude generates Python… → Executing Clustering: The generated code… → …
  • Requesting run clustering
  • SKILL.md covers Overview, How It Works, When to Use This Skill and Examples, plus 7 more sections
  • Runs Python scripts from its folder

What it does

Running Clustering Algorithms is an agent skill from jeremylongshore/tons-of-skills-marketplace. Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points". Trigger with relevant phrases based on skill purpose.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/clustering_visualization.py` and `assets/config_template.json`). Compatibility notes: Designed for Claude Code

It sits in Data & Analytics. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Requesting run clustering
  • Cluster analysis
  • Group data points
  • With relevant phrases based on skill purpose

Example prompts

  • “run clustering”
  • “cluster analysis”
  • “group data points”
  • “/running-clustering-algorithms”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(cmd:*)

Workflow steps

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

  1. Analyzing the Context: Claude analyzes the user's request to determine the dataset, desired clustering algorithm (if specified), and any…
  2. Generating Code: Claude generates Python code using appropriate ML libraries (e.g., scikit-learn) to perform the clustering task…
  3. Executing Clustering: The generated code is executed, and the clustering algorithm is applied to the dataset.
  4. Providing Results: Claude presents the results, including cluster assignments, performance metrics (e.g., silhouette score, Davies-Bouldin…

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    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.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Running Clustering Algorithms loads about 1k tokens when it runs, and up to ~1k if it reads all its reference files. Until then it costs about 68 tokens; SKILL.md has 458 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1k

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 jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 458 words, ~1,017 tokens.

Download SKILL.mdSave it as .claude/skills/running-clustering-algorithms/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
running-clustering-algorithms
description
Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points". Trigger with relevant phrases based on skill purpose.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
compatibility
Designed for Claude Code
version
1.23.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, clustering-algorithms

Clustering Algorithm Runner

Run clustering algorithms (K-means, DBSCAN, hierarchical) on datasets to discover natural groupings and structure in data.

Overview

This skill empowers Claude to perform clustering analysis on provided datasets. It allows for automated execution of various clustering algorithms, providing insights into data groupings and structures.

How It Works

  1. Analyzing the Context: Claude analyzes the user's request to determine the dataset, desired clustering algorithm (if specified), and any specific requirements.
  2. Generating Code: Claude generates Python code using appropriate ML libraries (e.g., scikit-learn) to perform the clustering task, including data loading, preprocessing, algorithm execution, and result visualization.
  3. Executing Clustering: The generated code is executed, and the clustering algorithm is applied to the dataset.
  4. Providing Results: Claude presents the results, including cluster assignments, performance metrics (e.g., silhouette score, Davies-Bouldin index), and visualizations (e.g., scatter plots with cluster labels).

When to Use This Skill

This skill activates when you need to:

  • Identify distinct groups within a dataset.
  • Perform a cluster analysis to understand data structure.
  • Run K-means, DBSCAN, or hierarchical clustering on a given dataset.

Examples

Example 1: Customer Segmentation

User request: "Run clustering on this customer data to identify customer segments. The data is in customer_data.csv."

The skill will:

  1. Load the customer_data.csv dataset.
  2. Perform K-means clustering to identify distinct customer segments based on their attributes.
  3. Provide a visualization of the customer segments and their characteristics.
Example 2: Anomaly Detection

User request: "Perform DBSCAN clustering on this network traffic data to identify anomalies. The data is available at network_traffic.txt."

The skill will:

  1. Load the network_traffic.txt dataset.
  2. Perform DBSCAN clustering to identify outliers representing anomalous network traffic.
  3. Report the identified anomalies and their characteristics.
Show full SKILL.md (177 more words)Show less

Best Practices

  • Data Preprocessing: Always preprocess the data (e.g., scaling, normalization) before applying clustering algorithms to improve performance and accuracy.
  • Algorithm Selection: Choose the appropriate clustering algorithm based on the data characteristics and the desired outcome. K-means is suitable for spherical clusters, while DBSCAN is better for non-spherical clusters and anomaly detection.
  • Parameter Tuning: Tune the parameters of the clustering algorithm (e.g., number of clusters in K-means, epsilon and min_samples in DBSCAN) to optimize the results.

Integration

This skill can be integrated with data loading skills to retrieve datasets from various sources. It can also be combined with visualization skills to generate insightful visualizations of the clustering results.

Prerequisites

  • Appropriate file access permissions
  • Required dependencies installed

Instructions

  1. Invoke this skill when the trigger conditions are met
  2. Provide necessary context and parameters
  3. Review the generated output
  4. Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

  • Invalid input: Prompts for correction
  • Missing dependencies: Lists required components
  • Permission errors: Suggests remediation steps

Resources

  • Project documentation
  • Related skills and commands

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references, assets) in skills/.curated/running-clustering-algorithms of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • assets/clustering_visualization.py
  • assets/config_template.json
  • assets/example_data.csv
  • references/README.md
  • scripts/README.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Running Clustering Algorithms 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.

Running Clustering Algorithms compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Running Clustering Algorithms this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1kAutomated safety check: PassMIT
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Scikit LearnzLanqing/codex-claude-academic-skills4.7k16 repos~3.9kAutomated safety check: PassBSD-3-Clause
Chart Visualizationbytedance/deer-flow84k1 repos~840Automated safety check: PassMIT
TimesFM Forecastinggoogle-research/timesfm34k—~4.7kAutomated safety check: PassApache-2.0

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Categories

Questions about Running Clustering Algorithms

What does Running Clustering Algorithms do?

Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Running Clustering Algorithms is an agent skill from jeremylongshore/tons-of-skills-marketplace. Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups.

When should I use Running Clustering Algorithms?

Running Clustering Algorithms fits situations like: requesting run clustering; cluster analysis; group data points; with relevant phrases based on skill purpose.

How do I install Running Clustering Algorithms in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill running-clustering-algorithms -a claude-code`. Or copy the skill folder (skills/.curated/running-clustering-algorithms in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/running-clustering-algorithms in your project. Claude Code loads it when a task matches its description.

How do I install Running Clustering Algorithms in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill running-clustering-algorithms -a codex`. Or copy the skill folder (skills/.curated/running-clustering-algorithms in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/running-clustering-algorithms in your project. Codex loads it when a task matches its description.

Can I use Running Clustering Algorithms 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 jeremylongshore/tons-of-skills-marketplace --skill running-clustering-algorithms -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/running-clustering-algorithms, .gemini/skills/running-clustering-algorithms, .github/skills/running-clustering-algorithms and .opencode/skills/running-clustering-algorithms in your project.

What does Running Clustering Algorithms need to run?

Going by SKILL.md and its folder, Running Clustering Algorithms needs Python for the scripts in its folder. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Running Clustering Algorithms 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 Running Clustering Algorithms 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 Running Clustering Algorithms use?

Running Clustering Algorithms 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 Running Clustering Algorithms use?

About 1k tokens (SKILL.md is roughly 4.1k 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 18 tokens, read only when the agent opens those files.

What are the alternatives to Running Clustering Algorithms?

Skills that share tags, products or a category with Running Clustering Algorithms: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Chart Visualization (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Running Clustering Algorithms?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

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