MR
Mayur Rathi
@mayurrathi
⭐ 5 GitHub stars

Wiki Onboarding

Generates two complementary onboarding guides \u2014 a Principal-Level architectural deep-dive and a Zero-to-Hero contributor walkthrough. Use when the user wants onboarding documentation fo...

mkdir -p ./skills/wiki-onboarding && curl -sfL https://raw.githubusercontent.com/mayurrathi/awesome-agent-skills/main/skills/wiki-onboarding/SKILL.md -o ./skills/wiki-onboarding/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# Wiki Onboarding Guide Generator


Generate two complementary onboarding documents that together give any engineer — from newcomer to principal — a complete understanding of a codebase.


When to Activate


- User asks for onboarding docs or getting-started guides

- User runs `/deep-wiki:onboard` command

- User wants to help new team members understand a codebase


Language Detection


Scan the repository for build files to determine the primary language for code examples:

- `package.json` / `tsconfig.json` → TypeScript/JavaScript

- `*.csproj` / `*.sln` → C# / .NET

- `Cargo.toml` → Rust

- `pyproject.toml` / `setup.py` / `requirements.txt` → Python

- `go.mod` → Go

- `pom.xml` / `build.gradle` → Java


Guide 1: Principal-Level Onboarding


**Audience**: Senior/staff+ engineers who need the "why" behind decisions.


Required Sections


1. **System Philosophy & Design Principles** — What invariants does the system maintain? What were the key design choices and why?

2. **Architecture Overview** — Component map with Mermaid diagram. What owns what, communication patterns.

3. **Key Abstractions & Interfaces** — The load-bearing abstractions everything depends on

4. **Decision Log** — Major architectural decisions with context, alternatives considered, trade-offs

5. **Dependency Rationale** — Why each major dependency was chosen, what it replaced

6. **Data Flow & State** — How data moves through the system (traced from actual code, not guessed)

7. **Failure Modes & Error Handling** — What breaks, how errors propagate, recovery patterns

8. **Performance Characteristics** — Bottlenecks, scaling limits, hot paths

9. **Security Model** — Auth, authorization, trust boundaries, data sensitivity

10. **Testing Strategy** — What's tested, what isn't, testing philosophy

11. **Operational Concerns** — Deployment, monitoring, feature flags, configuration

12. **Known Technical Debt** — Honest assessment of shortcuts and their risks


Rules

- Every claim backed by `(file_path:line_number)` citation

- Minimum 3 Mermaid diagrams (architecture, data flow, dependency graph)

- All Mermaid diagrams use dark-mode colors (see wiki-vitepress skill)

- Focus on WHY decisions were made, not just WHAT exists


Guide 2: Zero-to-Hero Contributor Guide


**Audience**: New contributors who need step-by-step practical guidance.


Required Sections


1. **What This Project Does** — 2-3 sentence elevator pitch

2. **Prerequisites** — Tools, versions, accounts needed

3. **Environment Setup** — Step-by-step with exact commands, expected output at each step

4. **Project Structure** — Annotated directory tree (what lives where and why)

5. **Your First Task** — End-to-end walkthrough of adding a simple feature

6. **Development Workflow** — Branch strategy, commit conventions, PR process

7. **Running Tests** — How to run tests, what to test, how to add a test

8. **Debugging Guide** — Common issues and how to diagnose them

9. **Key Concepts** — Domain-specific terminology explained with code examples

10. **Code Patterns** — "If you want to add X, follow this pattern" templates

11. **Common Pitfalls** — Mistakes every new contributor makes and how to avoid them

12. **Where to Get Help** — Communication channels, documentation, key contacts

13. **Glossary** — Terms used in the codebase that aren't obvious

14. **Quick Reference Card** — Cheat sheet of most-used commands and patterns


Rules

- All code examples in the detected primary language

- Every command must be copy-pasteable

- Include expected output for verification steps

- Use Mermaid for workflow diagrams (dark-mode colors)

- Ground all claims in actual code — cite `(file_path:line_number)`


When to Use

This skill is applicable to execute the workflow or actions described in the overview.

🎯 Best For

  • Technical writers
  • API documentation teams
  • Developers scaffolding new projects
  • Prototype builders
  • UI designers

💡 Use Cases

  • Generating JSDoc/TSDoc comments
  • Writing README files for new projects
  • Bootstrapping React components
  • Creating API route handlers

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Wiki Onboarding to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

Does it follow my documentation style?

Most documentation skills respect existing style. Provide a style guide or example in your prompt.

Can I customize the generated output?

Yes — modify the skill's prompt instructions to match your project conventions and coding style.

Does this work with Figma?

Some design skills integrate with Figma plugins. Check the Works With section for supported tools.

How do I install Wiki Onboarding?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/wiki-onboarding/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Auto-generating without reviewing

AI documentation can contain inaccuracies. Always verify technical accuracy.

Using generated code without understanding

Understand what generated code does before shipping it to production.

Skipping usability testing

AI-generated designs should be validated with real users before development.

Not reading the full skill

Skills contain important context and edge cases beyond the quick start.

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