Agent skill

Algorithm Visualizer Guide

by wentorai in wentorai/research-plugins

Guide to Algorithm Visualizer for interactive algorithm exploration

MITAuto-check: notesData & Analytics

Install Algorithm Visualizer Guide

skills CLI
$ npx skills add wentorai/research-plugins --skill algorithm-visualizer-guide -a claude-code

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

GitHub CLI
$ gh skill install wentorai/research-plugins algorithm-visualizer-guide --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/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analysis/dataviz/algorithm-visualizer-guide .claude/skills/algorithm-visualizer-guide && 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
algorithm-visualizer-guide
GitHub stars
298
Used in
1 other repo
Token cost
~2.4k tokens
SKILL.md length
501 words
Files
1
Skills in repo
428
Repo updated
First seen
Licence
MIT

At a glance

Guide to Algorithm Visualizer for interactive algorithm exploration

  • Tasks that involve Data visualization
  • SKILL.md covers Overview, Platform Architecture and Setup, Visualization API for Custom… and Research-Relevant Algorithm…, plus 3 more sections
  • Calls npm, git and docker; reaches github.com

What it does

Algorithm Visualizer Guide is an agent skill from wentorai/research-plugins. Guide to Algorithm Visualizer for interactive algorithm exploration

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Data visualization. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Data visualization

Example prompts

  • “/algorithm-visualizer-guide”

Requirements

  • Node.js
  • Docker

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. 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

    Shell commands in SKILL.md call:

    • npm
    • git
    • docker
    • npx

    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:

    • github.com

    Also links to:

    • algorithm-visualizer.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

Algorithm Visualizer Guide loads about 2.4k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 501 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~24
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:63
    ACT_APP_API_URL=http://localhost:8080" > .env.local

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 wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 501 words, ~2,401 tokens.

Download SKILL.mdSave it as .claude/skills/algorithm-visualizer-guide/SKILL.md (or your agent's skills folder).
name
algorithm-visualizer-guide
description
Guide to Algorithm Visualizer for interactive algorithm exploration

Algorithm Visualizer Guide

Overview

Algorithm Visualizer is an interactive online platform with over 48K stars on GitHub that allows researchers, educators, and students to visualize algorithms through animated graphical representations. The platform provides a web-based environment where algorithm code runs step-by-step alongside a visual canvas that shows data structures being manipulated in real time.

For academic researchers, Algorithm Visualizer serves two primary purposes. First, it is an excellent tool for teaching computational methods in courses and workshops. Complex algorithms in sorting, graph theory, dynamic programming, and numerical methods become immediately intuitive when students can see the step-by-step execution animated on screen. Second, researchers developing new algorithms can use the platform to debug, validate, and communicate their approaches visually, making it easier to explain novel computational contributions in papers and presentations.

The platform supports JavaScript-based algorithm implementations and provides a visualization API with tracer objects for arrays, graphs, logs, and custom 2D canvases. Researchers can create custom visualizations of their own algorithms and share them through the platform's public repository or embed them in course materials.

Platform Architecture and Setup

Algorithm Visualizer consists of three main components that work together to provide the interactive visualization experience.

Components
  • algorithm-visualizer - The web application frontend (React-based)
  • server - The backend API that compiles and executes code
  • algorithms - The public repository of contributed algorithm visualizations
Running Locally for Research Use
bash
# Clone the repository
git clone https://github.com/algorithm-visualizer/algorithm-visualizer.git
cd algorithm-visualizer

# Install dependencies
npm install

# Start development server
npm start

# Access at http://localhost:3000
Self-Hosted Deployment for Lab or Course
bash
# Clone all required components
git clone https://github.com/algorithm-visualizer/algorithm-visualizer.git
git clone https://github.com/algorithm-visualizer/server.git

# Build and run with Docker
cd server
docker build -t algo-viz-server .
docker run -d -p 8080:8080 algo-viz-server

cd ../algorithm-visualizer
# Set the server URL in environment configuration
echo "REACT_APP_API_URL=http://localhost:8080" > .env.local
npm install && npm run build
npx serve -s build -l 3000

Visualization API for Custom Algorithms

The platform provides tracer objects that researchers use to instrument their algorithm code with visual output.

Array Tracer for Sorting and Searching
javascript
const { Tracer, Array1DTracer, LogTracer, Layout, VerticalLayout } = require('algorithm-visualizer');

// Set up visualization layout
const arrayTracer = new Array1DTracer('Array');
const logger = new LogTracer('Execution Log');
Layout.setRoot(new VerticalLayout([arrayTracer, logger]));

// Example: Visualizing insertion sort on research ranking data
const impactFactors = [3.2, 1.8, 7.5, 2.1, 5.9, 4.3, 6.7, 0.9];
arrayTracer.set(impactFactors);
Tracer.delay();

for (let i = 1; i < impactFactors.length; i++) {
    const key = impactFactors[i];
    let j = i - 1;

    logger.println(`Inserting element ${key} at position ${i}`);
    arrayTracer.select(i);
    Tracer.delay();

    while (j >= 0 && impactFactors[j] > key) {
        arrayTracer.patch(j + 1, impactFactors[j]);
        Tracer.delay();

        impactFactors[j + 1] = impactFactors[j];
        arrayTracer.depatch(j + 1);
        j--;
    }

    impactFactors[j + 1] = key;
    arrayTracer.patch(j + 1, key);
    Tracer.delay();
    arrayTracer.depatch(j + 1);
    arrayTracer.deselect(i);
}

logger.println('Sorting complete: journals ranked by impact factor');
Graph Tracer for Network Algorithms
javascript
const { Tracer, GraphTracer, LogTracer, Layout, VerticalLayout } = require('algorithm-visualizer');

const graphTracer = new GraphTracer('Citation Network');
const logger = new LogTracer('BFS Traversal');
Layout.setRoot(new VerticalLayout([graphTracer, logger]));

// Adjacency matrix representing citation relationships
const citations = [
    [0, 1, 1, 0, 0, 0],
    [0, 0, 1, 1, 0, 0],
    [0, 0, 0, 0, 1, 0],
    [0, 0, 0, 0, 1, 1],
    [0, 0, 0, 0, 0, 1],
    [0, 0, 0, 0, 0, 0]
];

const paperNames = ['Paper A', 'Paper B', 'Paper C', 'Paper D', 'Paper E', 'Paper F'];

graphTracer.set(citations);
Tracer.delay();

// BFS to find citation chains
function bfs(startNode) {
    const visited = new Set();
    const queue = [startNode];
    visited.add(startNode);

    logger.println(`Starting BFS from ${paperNames[startNode]}`);
    graphTracer.visit(startNode);
    Tracer.delay();

    while (queue.length > 0) {
        const current = queue.shift();

        for (let neighbor = 0; neighbor < citations.length; neighbor++) {
            if (citations[current][neighbor] === 1 && !visited.has(neighbor)) {
                visited.add(neighbor);
                queue.push(neighbor);

                logger.println(`${paperNames[current]} cites ${paperNames[neighbor]}`);
                graphTracer.visit(neighbor, current);
                Tracer.delay();
            }
        }
    }

    logger.println(`BFS complete. Visited ${visited.size} papers.`);
}

bfs(0);

Research-Relevant Algorithm Categories

The platform includes visualizations across categories directly relevant to computational research.

Graph Algorithms (Network Analysis)
  • Breadth-First Search / Depth-First Search - Traversing citation networks, dependency graphs
  • Dijkstra / Bellman-Ford - Shortest paths in weighted collaboration networks
  • Minimum Spanning Tree (Kruskal, Prim) - Finding minimum-cost network connections
  • Topological Sort - Ordering tasks in experimental pipelines
  • Strongly Connected Components - Identifying tightly coupled research clusters
Show full SKILL.md (180 more words)Show less
Sorting and Searching (Data Processing)
  • Merge Sort / Quick Sort - Efficient sorting of large experimental datasets
  • Binary Search - Fast lookup in ordered measurement arrays
  • Heap Sort - Priority queue operations for scheduling simulations
Dynamic Programming (Optimization)
  • Longest Common Subsequence - Sequence alignment in bioinformatics
  • Knapsack Problem - Resource allocation under constraints
  • Edit Distance - String similarity measures for text analysis
Numerical Methods
  • Newton's Method - Root finding for equation solving
  • Monte Carlo Simulation - Stochastic estimation of integrals and probabilities
  • Gradient Descent - Parameter optimization in model fitting

Creating Custom Visualizations for Teaching

Researchers teaching computational courses can create custom algorithm visualizations and organize them into course-specific collections.

javascript
// Template for a custom research algorithm visualization
const {
    Tracer, Array1DTracer, Array2DTracer,
    LogTracer, Layout, VerticalLayout
} = require('algorithm-visualizer');

// Initialize tracers for your algorithm
const matrixTracer = new Array2DTracer('Distance Matrix');
const logger = new LogTracer('Algorithm Steps');
Layout.setRoot(new VerticalLayout([matrixTracer, logger]));

// Set initial data
const data = [
    [0, 3, 8, Infinity, -4],
    [Infinity, 0, Infinity, 1, 7],
    [Infinity, 4, 0, Infinity, Infinity],
    [2, Infinity, -5, 0, Infinity],
    [Infinity, Infinity, Infinity, 6, 0]
];

matrixTracer.set(data);
logger.println('Floyd-Warshall: Computing all-pairs shortest paths');
Tracer.delay();

// Floyd-Warshall algorithm with visualization
for (let k = 0; k < data.length; k++) {
    logger.println(`Intermediate vertex: ${k}`);
    for (let i = 0; i < data.length; i++) {
        for (let j = 0; j < data.length; j++) {
            if (data[i][k] + data[k][j] < data[i][j]) {
                data[i][j] = data[i][k] + data[k][j];
                matrixTracer.patch(i, j, data[i][j]);
                Tracer.delay();
                matrixTracer.depatch(i, j);
            }
        }
    }
}

logger.println('All-pairs shortest paths computed.');

Integration Tips for Researchers

  • Course websites: Embed Algorithm Visualizer links directly in course syllabi and lab handouts
  • Paper supplements: Create interactive algorithm demonstrations as supplementary material
  • Lab meetings: Use visualizations to explain algorithmic approaches to interdisciplinary collaborators
  • Self-hosted instances: Deploy within university networks for courses with restricted internet access
  • Contributing back: Submit new algorithm visualizations to the public repository to share with the community

References

© wentorai, 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 skills/analysis/dataviz/algorithm-visualizer-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Algorithm Visualizer Guide 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.

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Questions about Algorithm Visualizer Guide

What does Algorithm Visualizer Guide do?

Guide to Algorithm Visualizer for interactive algorithm exploration. Algorithm Visualizer Guide is an agent skill from wentorai/research-plugins.

When should I use Algorithm Visualizer Guide?

Algorithm Visualizer Guide fits situations like: tasks that involve Data visualization.

How do I install Algorithm Visualizer Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill algorithm-visualizer-guide -a claude-code`. Or copy the skill folder (skills/analysis/dataviz/algorithm-visualizer-guide in wentorai/research-plugins) into .claude/skills/algorithm-visualizer-guide in your project. Claude Code loads it when a task matches its description.

How do I install Algorithm Visualizer Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill algorithm-visualizer-guide -a codex`. Or copy the skill folder (skills/analysis/dataviz/algorithm-visualizer-guide in wentorai/research-plugins) into .agents/skills/algorithm-visualizer-guide in your project. Codex loads it when a task matches its description.

Can I use Algorithm Visualizer Guide 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 wentorai/research-plugins --skill algorithm-visualizer-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algorithm-visualizer-guide, .gemini/skills/algorithm-visualizer-guide, .github/skills/algorithm-visualizer-guide and .opencode/skills/algorithm-visualizer-guide in your project.

What does Algorithm Visualizer Guide need to run?

Going by SKILL.md and its folder, Algorithm Visualizer Guide needs the command-line tools its instructions call (npm, git, docker and npx). Our summary lists: Node.js; Docker.

Does Algorithm Visualizer Guide access the network?

SKILL.md names 2 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: algorithm-visualizer.org. This is read from the text; nothing was executed.

Is Algorithm Visualizer Guide safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Algorithm Visualizer Guide use?

Algorithm Visualizer Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Algorithm Visualizer Guide use?

About 2.4k tokens (SKILL.md is roughly 9.6k 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 Algorithm Visualizer Guide?

Skills that share tags, products or a category with Algorithm Visualizer Guide: Matplotlib (zLanqing/codex-claude-academic-skills, 4.6k stars), Chart Visualization (bytedance/deer-flow, 83k stars), Scientific Visualization (mims-harvard/OptimusKG, 146 stars) and Seaborn (zLanqing/codex-claude-academic-skills, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algorithm Visualizer Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 428 skills in this directory. The repository was last updated on June 19, 2026.

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