Operating System Visualization logo

Operating System Visualization

Community
mingchen666
operating-system-visualization

Use this skill whenever the user wants visual learning for operating systems, process states, CPU scheduling, Gantt charts, synchronization, semaphore/PV problems, deadlock, Banker's algorithm, memory management, paging, address translation, page replacement, file systems, disk scheduling, or 408 OS review.

Overview

Publishermingchen666
RepositoryReviva
Skill nameoperating-system-visualization
Stars
293
Forks
31
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mingchen666 on GitHub. Read the source before you install it.

Installation

Install the Operating System Visualization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/operating-system-visualization
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mingchen666/Reviva.git /tmp/Reviva
mkdir -p .claude/skills
cp -r /tmp/Reviva/electron/builtin-assets/skills/operating-system-visualization .claude/skills/operating-system-visualization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Operating System Visualization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Operating System Visualization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Operating System Visualization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Operating System Visualization Skill

Create visual explanations for operating system mechanisms. The goal is to make queues, states, resources, and address mappings observable.

Use For

  • process/thread state transition and scheduling queues;
  • FCFS, SJF, priority, RR, HRRN scheduling and Gantt charts;
  • semaphore/PV, producer-consumer, readers-writers, dining philosophers;
  • deadlock conditions, resource allocation graphs, Banker's algorithm;
  • paging, segmentation, virtual memory, TLB, page table translation;
  • page replacement: OPT, FIFO, LRU, Clock;
  • file allocation, directory structure, free-space management;
  • disk scheduling: FCFS, SSTF, SCAN, C-SCAN.

Output Modes

TopicPreferred visual
Schedulingtimeline/Gantt chart + ready queue per time
PV problemconstraint graph + semaphore table + pseudocode
Deadlockresource allocation graph + request/available table
Address translationvirtual address split + page table/TLB + physical address
Page replacementframe table per reference + fault markers
Disk schedulingcylinder line + head movement path

Bundled Template

Choose the closest bundled template:

  • assets/templates/os-stepper.html - process states, CPU scheduling, Gantt charts, disk scheduling, queue-based simulations.
  • assets/templates/page-replacement.html - FIFO/LRU/OPT/Clock page replacement and frame tables.
  • assets/templates/banker-matrix.html - Banker's algorithm, safe sequence, deadlock avoidance matrices.

Replace DATA.steps or the relevant DATA fields with the user's scheduling, page replacement, deadlock, or disk scheduling data. Keep the queue/matrix/table panels and exam-answer note unless the task is intentionally static.

Follow the shared UI standard in /skills/cs-course-learning/references/visual-template-ui.md when available: learning-workbench layout, stable controls, state table, step inspector, and exam-transfer panel.

Research-Informed Design Patterns

Use patterns from OS teaching simulators:

  • accept custom datasets: processes, arrival times, burst times, time quantum, priorities, page reference strings, resource matrices;
  • show both visualization and calculation table;
  • include Gantt charts for scheduling;
  • include Allocation/Max/Need/Available matrices for Banker's algorithm;
  • include frame tables and page-fault markers for page replacement;
  • include test mode or quick-check questions after the simulation.

Topic Coverage Checklist

For 408/期末复习, cover these families when relevant:

  • process/thread model: states, PCB, context switch, IPC;
  • scheduling: FCFS, SJF, SRTF, priority, RR, HRRN;
  • synchronization: semaphore, mutex, monitor concept, producer-consumer, readers-writers;
  • deadlock: conditions, prevention, avoidance, detection, Banker's algorithm;
  • memory: contiguous allocation, paging, segmentation, TLB, virtual memory;
  • page replacement: OPT, FIFO, LRU, Clock/second chance;
  • file/I/O: file allocation, directory, free space, disk scheduling, interrupt/DMA.

Required Structure

  • Show current state, next event, and why the transition occurs.
  • Keep tables synchronized with animations.
  • Include formulas for turnaround time, waiting time, page-fault count, or seek distance when relevant.
  • Include an exam-answer panel that shows what to write in a paper solution.
  • Include user-editable input panels when generating a learning simulator rather than solving one fixed problem.

Accuracy Rules

  • Ask for missing scheduling parameters: arrival time, service time, time quantum, priority rule.
  • For PV problems, identify shared resources, mutually exclusive sections, and precedence constraints before writing code.
  • For page replacement, state the initial frame state and tie-breaking rule.
  • For disk scheduling, state current head position, direction, and request queue.
  • For Banker's algorithm, show Work/Finish and safe sequence step by step.

Coordination

  • Use technical-diagram-skill for state machines, resource allocation graphs, page-table diagrams, and scheduling timelines.
  • Use learning-visualization-skill for interactive HTML pages with step controls.
  • Use cs-course-learning for broader OS learning and 408 strategy.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Operating System Visualization AI skill do?

Use this skill whenever the user wants visual learning for operating systems, process states, CPU scheduling, Gantt charts, synchronization, semaphore/PV problems, deadlock, Banker's algorithm, memory management, paging, address translation, page replacement, file systems, disk scheduling, or 408 OS review.

Why use Operating System Visualization on TypingMind?

Because you install it once and use it with any model. Operating System Visualization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Operating System Visualization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/operating-system-visualization. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Operating System Visualization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Operating System Visualization?

As many as you like. As long as a model supports skills, you can use Operating System Visualization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Operating System Visualization AI skill free?

It is published on GitHub by mingchen666. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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