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Dev Guide Generator

CommunityPopular
zebbern
dev-guide-generator

Generates complete technical tutorials from prerequisites and environment setup to core steps, troubleshooting, and a final cheatsheet. Trigger on requests to write a tutorial, create a setup guide, organize steps for beginners, or keywords like step-by-step, quickstart, or how-to guide.

Overview

Publisherzebbern
Repositoryclaude-code-guide
Skill namedev-guide-generator
Stars
4.6K
Forks
464
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Dev Guide Generator 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.
  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/zebbern/claude-code-guide.git /tmp/claude-code-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-guide/skills/dev-guide-generator .claude/skills/dev-guide-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dev Guide Generator 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 Dev Guide Generator 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 Dev Guide Generator 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.

Dev Guide Generator

One topic → complete technical tutorial: Through a structured SOP workflow, transform a technical topic into a comprehensive tutorial covering prerequisites, environment setup, core steps, common error troubleshooting, and advanced topics — all accompanied by a cheatsheet.

Quick Start

The user simply provides a technical topic or operational goal, and the Agent automatically generates a complete tutorial following this workflow:

User: Help me write a Docker beginner's tutorial
Agent: [Outputs complete technical tutorial + Cheatsheet following the SOP workflow]

SOP Workflow

Phase 1: Topic Scoping & Audience Analysis

Goal: Define the tutorial's technical subject, target audience, and scope boundaries.

Steps:

  1. Parse the topic: Identify the core technology, operational goals, and expected deliverables from the user's input
  2. Ask clarifying questions (up to 4 key questions):
    • What is the target audience's technical level? (Complete beginner / Some experience / Experienced developer)
    • What operating system will the reader be using? (macOS / Windows / Linux / Any)
    • What should the reader be able to do after completing the tutorial? (Specific deliverable)
    • Are there any version or tech stack constraints?
  3. If the user asks to skip clarification, proceed with these default assumptions:
    • Audience: Has basic programming experience but is unfamiliar with the topic technology
    • Environment: Cover both macOS and Linux (note Windows differences where necessary)
    • Goal: Be able to independently complete a minimal working example

Output: Tutorial metadata summary (topic, audience, objective, scope — no more than 150 words)


Phase 2: Prerequisites

Goal: List all knowledge and tools the reader needs before starting this tutorial, ensuring there are no knowledge gaps.

Steps:

  1. Dependency analysis:

    • List all technical concepts involved in this tutorial
    • For each concept, determine whether it should be "explained within the tutorial" or "assumed as prior knowledge"
    • Decision rule: If explaining it would digress more than 200 words from the main topic, classify it as a prerequisite
  2. Prerequisites checklist:

    • Categorize into "Must know" and "Nice to know" tiers

    • Attach a one-line explanation for each: "why you need it"

    • Format:

      **Must know**:
      - [Concept]: [Why you need it] (Recommended resource)
      
      **Nice to know**:
      - [Concept]: [What aspects of the tutorial it relates to]
  3. Self-check rules:

    • If prerequisites exceed 5 items, consider narrowing the tutorial scope or splitting into a series
    • Every prerequisite must have a publicly available learning resource

Output: Tiered prerequisites checklist


Phase 3: Environment Setup

Goal: Provide a reproducible environment configuration path so the reader is fully set up before starting the core steps.

Steps:

  1. Environment inventory: List all tools to install/configure with recommended versions

    • Format: Tool name Version requirement (e.g., >= x.y) | Purpose
    • Clearly distinguish "required" from "optional" installations
  2. Installation steps: Provide commands for each operating system

    • Precede each command with a one-line explanation of what it does

    • Use only officially recommended methods or mainstream package managers

    • Format:

      **macOS**:
      # Install xxx (via Homebrew)
      brew install xxx
      
      **Linux (Ubuntu/Debian)**:
      # Install xxx (via apt)
      sudo apt update && sudo apt install -y xxx
  3. Environment verification: Provide a verification command and expected output after each tool installation

    • Format:

      # Verify installation
      xxx --version
      # Expected output: xxx x.y.z
  4. Self-check rules:

    • All installation commands must come from official documentation or mainstream package managers — no third-party scripts
    • Never include real API keys, passwords, tokens, or other sensitive values
    • When configuration files are involved, use placeholders (e.g., YOUR_API_KEY) and explain how to obtain the real value

Output: OS-specific installation and configuration guide + verification commands


Phase 4: Core Steps

Goal: Walk the reader through the core operations in a progressive structure, where each step can be independently verified.

Steps:

  1. Step planning:

    • Break the entire operation into 5–10 steps (each focused on one sub-goal)
    • Order steps strictly by dependency
    • Each step includes: step number, title, and objective statement
  2. Step writing format:

    #### Step N: [Step Title]
    
    **Objective**: [What state is achieved after this step]
    
    **Actions**:
    [Code block or operational instructions]
    
    **Explanation**:
    - [Line-by-line or section-by-section explanation of key parts]
    
    **Verification**:
    [What command to run / what result to check to confirm success]
    Expected output: [Specific expected result]
  3. Writing guidelines:

    • Code blocks must specify the language (e.g., bash, python)
    • Placeholders use ALL_CAPS_WITH_UNDERSCORES format (e.g., YOUR_PROJECT_NAME) and are explained on first occurrence
    • Each code block should not exceed 30 lines; split and explain in sections if longer
    • Use relative file paths; state the project root directory at the beginning
    • Every step must end with a verification section
  4. Progressive complexity:

    • Steps 1–3: Minimal runnable example (Hello World level)
    • Middle steps: Gradually introduce real-world features
    • Final 1–2 steps: Combine everything into a complete example

Output: Numbered step list, each containing actions + explanation + verification


Phase 5: Troubleshooting

Goal: Anticipate problems the reader may encounter and provide a direct path from error message to solution.

Steps:

  1. Error collection: Based on the technical topic, list the 5–8 most common error scenarios

    • Sources: Environment misconfiguration, version incompatibilities, permission issues, typos, network problems, etc.
  2. Error entry format:

    **Error N: [Error message summary]**
    
    Full error message:
    [Actual error output]
    
    Cause: [One-sentence explanation of why this error occurs]
    
    Solution:
    [Specific fix commands or steps]
    
    Verify the fix:
    [What to run to confirm the issue is resolved]
  3. Writing guidelines:

    • Error messages must be real (do not fabricate error messages)
    • Solutions must be actionable — avoid vague advice like "check your configuration"
    • If an error has multiple possible causes, list them from most to least likely
    • For permission-related issues, explain why the permission is needed rather than jumping to sudo or chmod 777
  4. Self-check rules:

    • Solutions must not include operations that could create security risks (e.g., chmod 777, disabling firewalls)
    • Never advise the reader to disable security features to "fix" a problem

Output: Structured troubleshooting table


Phase 6: Advanced Topics

Goal: Point readers who have completed the basics toward next steps, providing a learning path from beginner to advanced.

Steps:

  1. Advanced topic recommendations (3–5 directions):

    • For each direction, write a short paragraph: what it is, why it's worth learning, and what scenarios it applies to

    • Tag the difficulty level: Intermediate / Advanced

    • Format:

      **Direction N: [Topic Name]** | Difficulty: [Intermediate/Advanced]
      
      [Short paragraph]
      
      Recommended resources:
      - [Resource name] ([Type: documentation/book/course])
  2. Hands-on project suggestions:

    • Provide 2–3 small projects the reader can independently complete using what they learned
    • Each project includes: project name, one-sentence description, and relevant concepts
  3. Best practice tips (3–5 items):

    • Key differences between production and tutorial environments
    • Security considerations
    • Performance optimization directions

Output: Advanced learning roadmap + hands-on project suggestions + best practices


Phase 7: Cheatsheet

Goal: Distill the tutorial's essentials into a one-page quick reference for everyday use.

Steps:

  1. Cheatsheet structure:

    # [Technology Name] Cheatsheet
    
    ## Environment Info
    | Item | Command/Path |
    |------|--------------|
    | Install | `command` |
    | Version check | `command` |
    | Config file location | `path` |
    
    ## Common Commands
    | Action | Command | Description |
    |--------|---------|-------------|
    | xxx    | `xxx`   | xxx         |
    
    ## Common Code Snippets
    [Up to 5 frequently used code snippets, each no more than 10 lines]
    
    ## Quick Troubleshooting
    | Symptom | Possible Cause | Quick Fix |
    |---------|---------------|-----------|
    | xxx     | xxx           | `xxx`     |
  2. Writing guidelines:

    • Total cheatsheet length should fit on 2 printed A4 pages
    • Commands must be complete and ready to copy-paste
    • No explanatory prose — only "what to do → how to do it" mappings
    • Order items by frequency of use, from most to least common

Output: One-page cheatsheet


Phase 8: Document Assembly & Output

Goal: Assemble the outputs from the previous seven phases into a complete tutorial document.

Tutorial document template:

markdown
# [Technical Topic] Complete Tutorial

> Last updated: [Current date] | Applicable version: [Version number]
> Difficulty: [Beginner/Intermediate/Advanced] | Estimated time: [N hours/minutes]

## Tutorial Overview

[Phase 1 metadata summary — what the reader will be able to do after completion]

## 1. Prerequisites

[Phase 2 prerequisites checklist]

## 2. Environment Setup

[Phase 3 installation and configuration guide]

## 3. Core Steps

[Phase 4 numbered step list]

## 4. Troubleshooting

[Phase 5 troubleshooting table]

## 5. Advanced Topics

[Phase 6 advanced roadmap and hands-on projects]

## 6. Cheatsheet

[Phase 7 cheatsheet]

## Appendix

- Glossary (if domain-specific terminology is used, list in a table: Term | Definition)
- Reference links (official documentation, community resources, etc.)

Document output requirements:

  • All code blocks must specify the language
  • All commands must be directly copy-pasteable (no line numbers, prompts, or other noise)
  • All placeholders use the YOUR_XXX format and are explained on first occurrence
  • Configuration files must not contain real keys or tokens
  • Dates must use the actual current date

Flow Control Rules

Interaction Mode Selection

Choose the mode based on the level of detail in the user's input:

User InputModeBehavior
Just a technology name (e.g., "Docker tutorial")Guided modeExecute Phase 1 questions, wait for answers before continuing
Specific goal (e.g., "Deploy a Node.js app with Docker")Semi-auto modeAsk 1–2 key questions while simultaneously planning the steps
Detailed description (includes audience, environment, goal)Full-auto modeStart output directly from Phase 2
User says "just write it / don't ask"Quick modeOutput the complete tutorial based on default assumptions

Quality Checklist

Before outputting the final tutorial, check each item:

  • Prerequisites checklist is complete with no knowledge gaps
  • Every environment setup command has a verification step
  • Every core step includes "actions + explanation + verification"
  • Step dependencies are correct (no step uses a tool that hasn't been installed yet)
  • At least 5 common errors with specific, actionable solutions
  • At least 3 advanced directions with resource recommendations
  • Cheatsheet is self-contained with common commands and troubleshooting info
  • All code blocks specify the language
  • No hardcoded keys, tokens, or personal paths
  • No suggestions that could create security vulnerabilities (e.g., chmod 777)
  • No dependency on paid APIs or subscription-only tools (unless the tool itself is the tutorial subject)

Iterative Refinement

If the user provides feedback on the tutorial:

  1. Identify which Phase the feedback relates to
  2. Re-execute from that Phase
  3. Cascade updates to all downstream content (e.g., an environment change must propagate to subsequent steps and the Cheatsheet)
  4. Maintain step numbering continuity

Use Cases

This tutorial generator is suitable for the following types of technical tutorials:

  • Tool usage: Tutorials for tools like Git, Docker, Kubernetes, Vim, etc.
  • Environment setup: Development environments, CI/CD pipelines, server configuration, etc.
  • Programming introductions: Language primers, framework quickstarts, library usage, etc.
  • Operations manuals: Deployment, monitoring, logging, backup and recovery, etc.
  • Data processing: Database operations, ETL workflows, data analysis tool usage, etc.

Frequently asked questions

What does the Dev Guide Generator AI skill do?

Generates complete technical tutorials from prerequisites and environment setup to core steps, troubleshooting, and a final cheatsheet. Trigger on requests to write a tutorial, create a setup guide, organize steps for beginners, or keywords like step-by-step, quickstart, or how-to guide.

Why use Dev Guide Generator on TypingMind?

Because you install it once and use it with any model. Dev Guide Generator 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 Dev Guide Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zebbern/claude-code-guide/tree/main/skills/dev-guide-generator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dev Guide Generator?

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 Dev Guide Generator?

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

Is the Dev Guide Generator AI skill free?

Yes. It is published on GitHub by zebbern under the MIT license. 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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