Official agent skill

Wiki Onboarding

by microsoft in microsoft/skills

Generates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager.

OfficialMITAuto-check passedDevelopment

Install Wiki Onboarding

skills CLI
$ npx skills add microsoft/skills --skill wiki-onboarding -a claude-code

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

GitHub CLI
$ gh skill install microsoft/skills wiki-onboarding --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/microsoft/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/plugins/deep-wiki/skills/wiki-onboarding .claude/skills/wiki-onboarding && 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
wiki-onboarding
GitHub stars
3.1k
Token cost
~3.3k tokens
SKILL.md length
1,564 words
Files
1
Skills in repo
150
Repo updated
First seen
Licence
MIT

At a glance

Generates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager.

  • Works in 4 steps: Check for git remote: Run git remote… → Ask the user: "Is this a local-only… → Determine default branch: Run git… → …
  • The user wants onboarding documentation for a codebase
  • SKILL.md covers Source Repository Resolution…, When to Activate, Output Structure and Language Detection, plus 6 more sections
  • Calls git

What it does

Wiki Onboarding is an agent skill from microsoft/skills, published by the product's own GitHub organization. Generates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager. Use when the user wants onboarding documentation for a codebase.

Its SKILL.md is about 3.3k 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 Development. It works with TypeScript, Azure DevOps and Git. The repository describes itself as: Skills, MCP servers, Custom Agents, Agents.md for SDKs to ground Coding Agents. The licence is MIT.

When your agent uses it

  • The user wants onboarding documentation for a codebase

Example prompts

  • “Use the wiki-onboarding skill to generate four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer…”
  • “/wiki-onboarding”

Requirements

  • Python 3

Workflow steps

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

  1. Check for git remote: Run git remote get-url origin to detect if a remote exists
  2. Ask the user: "Is this a local-only repository, or do you have a source repository URL (e.g., GitHub, Azure DevOps)?"
  3. Determine default branch: Run git rev-parse --abbrev-ref HEAD
  4. Do NOT proceed until source repo context is resolved

What it can do on your machine

Read from SKILL.md and the folder at commit 354361d. 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:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Wiki Onboarding loads about 3.3k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 1,564 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from microsoft/skills at commit 354361d, republished under its MIT licence (© microsoft). 1,564 words, ~3,343 tokens.

Download SKILL.mdSave it as .claude/skills/wiki-onboarding/SKILL.md (or your agent's skills folder).
name
wiki-onboarding
description
Generates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager. Use when the user wants onboarding documentation for a codebase.
license
MIT
metadata.author
Microsoft
metadata.version
1.0.0

Wiki Onboarding Guide Generator

Generate four audience-tailored onboarding documents in an onboarding/ folder, each giving a different stakeholder exactly the understanding they need.

Source Repository Resolution (MUST DO FIRST)

Before generating any guides, you MUST determine the source repository context:

  1. Check for git remote: Run git remote get-url origin to detect if a remote exists
  2. Ask the user: "Is this a local-only repository, or do you have a source repository URL (e.g., GitHub, Azure DevOps)?"
    • Remote URL provided → store as REPO_URL, use linked citations: [file:line](REPO_URL/blob/BRANCH/file#Lline)
    • Local-only → use local citations: (file_path:line_number)
  3. Determine default branch: Run git rev-parse --abbrev-ref HEAD
  4. Do NOT proceed until source repo context is resolved

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

Output Structure

Generate an onboarding/ folder with these files:

onboarding/
├── index.md                    # Onboarding hub — links to all 4 guides with audience descriptions
├── contributor-guide.md        # For new contributors (assumes Python or JS background)
├── staff-engineer-guide.md     # For staff/principal engineers
├── executive-guide.md          # For VP/director-level engineering leaders
└── product-manager-guide.md    # For product managers and non-engineering stakeholders
index.md — Onboarding Hub

A landing page with:

  • One-paragraph project summary
  • Guide selector table:
GuideAudienceWhat You'll LearnTime
Contributor GuideNew contributors with Python/JS experienceSetup, first PR, codebase patterns~30 min
Staff Engineer GuideStaff/principal engineersArchitecture, design decisions, system boundaries~45 min
Executive GuideVP/directors of engineeringCapabilities, risks, team topology, investment thesis~20 min
Product Manager GuideProduct managersFeatures, user journeys, constraints, data model~20 min

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: Contributor Guide

File: onboarding/contributor-guide.md Audience: Engineers joining the project. Assumes proficiency in Python or JavaScript and general software engineering experience. Length: 1000–2500 lines. Progressive — each section builds on the last.

Required Sections

Part I: Foundations (skip if repo uses Python or JS)

  1. {Primary Language} for Python/JS Engineers — Syntax comparison tables, async model, collections, type system, package management. Concrete code side-by-side, NOT abstract descriptions.
  2. {Primary Framework} Essentials — Compare to equivalent Python/JS frameworks (e.g., FastAPI, Express). Request pipeline, routing, DI, config.

Part II: This Codebase 3. What This Project Does — 2-3 sentence elevator pitch 4. Project Structure — Annotated directory tree (what lives where and why). Include graph TB architecture overview. 5. Core Concepts — Domain-specific terminology explained with code examples. Use erDiagram for data model. 6. Request Lifecycle — sequenceDiagram (with autonumber) tracing a typical request end-to-end. 7. Key Patterns — "If you want to add X, follow this pattern" templates with real code

Part III: Getting Productive 8. Prerequisites & Setup — Table: Tool, Version, Install Command. Step-by-step with expected output at each step. 9. Your First Task — End-to-end walkthrough of adding a simple feature 10. Development Workflow — Branch strategy, commit conventions, PR process. Use flowchart diagram. 11. Running Tests — All tests, single file, single test, coverage commands 12. Debugging Guide — Common issues table: Symptom, Cause, Fix 13. Common Pitfalls — Mistakes every new contributor makes and how to avoid them

Appendices

  • Glossary (40+ terms)
  • Key File Reference — Table: Path, Purpose, Why It Matters, Source
  • 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 with expected output
  • Minimum 5 Mermaid diagrams (architecture, ER, sequence, flowchart, state)
  • Use Mermaid for workflow diagrams (dark-mode colors) — add <!-- Sources: ... --> comment block after each
  • Ground all claims in actual code — cite using linked format

Guide 2: Staff Engineer Guide

File: onboarding/staff-engineer-guide.md Audience: Staff/principal engineers who need the "why" behind every decision. Deep systems experience, may not know this repo's language. Length: 800–1200 lines. Dense, opinionated, architectural.

Required Sections
  1. Executive Summary — What the system is in one dense paragraph. What it owns vs delegates.
  2. The Core Architectural Insight — The SINGLE most important concept. Include pseudocode in a DIFFERENT language from the repo.
  3. System Architecture — Full Mermaid graph TB diagram. Call out the "heart" of the system.
  4. Domain Model — Mermaid erDiagram of core entities. Data invariants table: Entity, Invariant, Enforced By, Source.
  5. Key Abstractions & Interfaces — classDiagram showing load-bearing abstractions.
  6. Request Lifecycle — sequenceDiagram (with autonumber) showing typical request from entry to response.
  7. State Transitions — stateDiagram-v2 for entities with meaningful lifecycle states.
  8. Decision Log — Table: Decision, Alternatives Considered, Rationale, Source.
  9. Dependency Rationale — Table: Dependency, Purpose, What It Replaced, Source.
  10. Data Flow & State — How data moves through the system. Storage comparison table.
  11. Failure Modes & Error Handling — flowchart for error propagation paths.
  12. Performance Characteristics — Bottlenecks, scaling limits, hot paths.
  13. Security Model — Auth, authorization, trust boundaries, data sensitivity.
  14. Testing Strategy — What's tested, what isn't, testing philosophy.
  15. Known Technical Debt — Table: Issue, Risk Level, Affected Files, Source.
  16. Where to Go Deep — Recommended reading order of source files, links to wiki sections.
Rules
  • Use pseudocode in a different language to explain concepts
  • Use comparison tables to map unfamiliar concepts (e.g., Task<T> = Awaitable[T])
  • Dense prose with tables, NOT shallow bullet lists
  • Every claim backed by linked citation
  • Minimum 5 Mermaid diagrams (architecture, ER, class, sequence, state, flowchart)
  • Each diagram followed by <!-- Sources: ... --> comment block
  • Use tables aggressively — decisions, dependencies, debt should ALL be tables with Source columns
  • Focus on WHY decisions were made, not just WHAT exists

Guide 3: Executive Guide

File: onboarding/executive-guide.md Audience: VP/director of engineering. Needs capability overview, risk assessment, and investment context — NOT code-level details. Length: 400–800 lines. Strategic, concise, decision-oriented.

Show full SKILL.md (689 more words)Show less
Required Sections
  1. System Overview — What it does, who uses it, business value in 2-3 sentences
  2. Capability Map — Table: Capability, Status (Built/Partial/Planned), Maturity, Dependencies. What the system can and cannot do today.
  3. Architecture at a Glance — High-level Mermaid graph LR diagram. Services, data stores, external integrations — NO internal code details. Focus on deployment units and team boundaries.
  4. Team Topology — Which team/person owns which components. Table: Component, Owner, Criticality, Bus Factor.
  5. Technology Investment Thesis — Why these technologies were chosen. Table: Technology, Purpose, Alternatives Considered, Risk Level.
  6. Risk Assessment — Table: Risk, Likelihood, Impact, Mitigation, Owner. Cover reliability, security, scalability, compliance.
  7. Cost & Scaling Model — How costs scale with usage. What the bottlenecks are. When the next scaling investment is needed.
  8. Dependency Map — graph TB showing critical external dependencies. Table: Dependency, Type (Service/Library/Platform), Risk if Unavailable.
  9. Key Metrics & Observability — What's measured, what dashboards exist, alerting coverage. Table: Metric, Current Value, Target, Source.
  10. Roadmap Alignment — Engineering workstreams mapped to business priorities. What's in progress, what's planned, what's blocked.
  11. Technical Debt Summary — Top 5 debt items with business impact. Table: Issue, Business Impact, Effort to Fix, Priority.
  12. Recommendations — 3-5 actionable recommendations for the next quarter, prioritized by impact.
Rules
  • NO code snippets — this guide is for engineering leaders, not coders
  • Diagrams at service/team level, not class/function level
  • Every claim backed by evidence — cite wiki sections, architecture docs, or source files
  • Minimum 3 Mermaid diagrams (architecture overview, dependency map, capability/roadmap)
  • Tables for every structured finding — this audience reads tables, not prose
  • Business language — translate technical concepts into impact (reliability, velocity, cost, risk)

Guide 4: Product Manager Guide

File: onboarding/product-manager-guide.md Audience: Product managers and non-engineering stakeholders. Needs to understand what the system does, what's possible, and where the boundaries are — NOT how it's built. Length: 400–800 lines. User-centric, feature-focused, constraint-aware.

Required Sections
  1. What This System Does — 2-3 sentence elevator pitch in user-facing language (no jargon)
  2. User Journey Map — Mermaid graph LR or journey diagram showing primary user flows through the system
  3. Feature Capability Map — Table: Feature, Status (Live/Beta/Planned/Not Possible), User-Facing Behavior, Limitations. Comprehensive map of what's built and what's not.
  4. Data Model (Product View) — Simplified Mermaid erDiagram showing entities users interact with. Explain in business terms (e.g., "A Project has many Documents" not "FK relationship").
  5. Configuration & Feature Flags — Table: Flag/Config, What It Controls, Default, Who Can Change It. What can be toggled without engineering work.
  6. API Capabilities — What integrations are possible. Table: Capability, Endpoint/Method, Authentication, Rate Limits. Written for integration partners, not developers.
  7. Performance & SLAs — Response times, throughput limits, availability targets. Table: Operation, Expected Latency, Throughput Limit, Current SLA.
  8. Known Limitations & Constraints — Honest list of what the system can't do or does poorly. Table: Limitation, User Impact, Workaround, Planned Fix.
  9. Data & Privacy — What data is collected, where it's stored, retention policies, compliance status. Table: Data Type, Storage Location, Retention, Compliance.
  10. Glossary — Domain terms explained in plain language (not engineering jargon)
  11. FAQ — 10+ common questions a PM would ask, answered concisely
Rules
  • ZERO engineering jargon — no "middleware", "dependency injection", "ORM". Use plain language.
  • User-centric framing — describe everything in terms of what users experience, not how code works
  • Minimum 3 Mermaid diagrams (user journey, data model, feature map/capability overview)
  • Tables for every structured finding — PMs scan tables, not prose
  • If a technical concept must be mentioned, explain it in one sentence (e.g., "Feature flags — toggles that let us turn features on/off without deploying code")
  • Every claim grounded in evidence — cite wiki sections or source files for verification

Mermaid Diagram Rules (ALL guides)

ALL diagrams must use dark-mode colors:

  • Node fills: #2d333b, borders: #6d5dfc, text: #e6edf3
  • Subgraph backgrounds: #161b22, borders: #30363d
  • Lines: #8b949e
  • If using inline style directives, use dark fills with ,color:#e6edf3
  • Do NOT use <br/> in Mermaid labels (use <br> or line breaks)

Validation

After generating each guide, verify:

  • All file paths mentioned actually exist in the repo
  • All class/method names are accurate (not hallucinated)
  • Mermaid diagrams render (no syntax errors)
  • No bare HTML-like tags (generics like List<T>) outside code fences — wrap in backticks
  • Each guide is appropriate for its audience — no code in Executive/PM guides

© microsoft, 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 .github/plugins/deep-wiki/skills/wiki-onboarding of microsoft/skills.

Open the folder on GitHubat commit 354361d

Compare with similar skills

Wiki Onboarding 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.

Wiki Onboarding compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Wiki Onboarding this skillmicrosoft/skills3.1k—~3.3kAutomated safety check: PassMIT
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
Convert Internal Package to TypeScriptTryGhost/Ghost55k—~1.2kAutomated safety check: PassMIT
Skyvern Version BumpSkyvern-AI/skyvern23k—~1kAutomated safety check: NotesAGPL-3.0
Azdo Internalmicrosoft/aspire6.3k—~4.5kAutomated safety check: PassMIT
Hunk Extension Authoring Mapmodem-dev/hunk9.5k—~5.8kAutomated safety check: PassMIT

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Categories

Questions about Wiki Onboarding

What does Wiki Onboarding do?

Generates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager. Wiki Onboarding is an agent skill from microsoft/skills, published by the product's own GitHub organization. Generates four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager.

When should I use Wiki Onboarding?

Wiki Onboarding fits situations like: the user wants onboarding documentation for a codebase.

How do I install Wiki Onboarding in Claude Code?

Run `npx skills add microsoft/skills --skill wiki-onboarding -a claude-code`. Or copy the skill folder (.github/plugins/deep-wiki/skills/wiki-onboarding in microsoft/skills) into .claude/skills/wiki-onboarding in your project. Claude Code loads it when a task matches its description.

How do I install Wiki Onboarding in Codex?

Run `npx skills add microsoft/skills --skill wiki-onboarding -a codex`. Or copy the skill folder (.github/plugins/deep-wiki/skills/wiki-onboarding in microsoft/skills) into .agents/skills/wiki-onboarding in your project. Codex loads it when a task matches its description.

Can I use Wiki Onboarding 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 microsoft/skills --skill wiki-onboarding -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wiki-onboarding, .gemini/skills/wiki-onboarding, .github/skills/wiki-onboarding and .opencode/skills/wiki-onboarding in your project.

What does Wiki Onboarding need to run?

Going by SKILL.md and its folder, Wiki Onboarding needs the command-line tools its instructions call (git). Our summary lists: Python 3.

Does Wiki Onboarding access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Wiki Onboarding 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. Review the folder before installing.

What licence does Wiki Onboarding use?

Wiki Onboarding 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 Wiki Onboarding use?

About 3.3k tokens (SKILL.md is roughly 13k 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 Wiki Onboarding?

Skills that share tags, products or a category with Wiki Onboarding: Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Convert Internal Package to TypeScript (TryGhost/Ghost, 55k stars), Skyvern Version Bump (Skyvern-AI/skyvern, 23k stars) and Azdo Internal (microsoft/aspire, 6.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Wiki Onboarding?

microsoft (a GitHub organization, an official publisher) maintains it in microsoft/skills, which has 3,091 GitHub stars. The repository holds 150 skills in this directory. The repository was last updated on October 6, 2026.

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