MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Doc-And-Modernize

Doc-And-Modernize is an code AI skill with a core value of >-. It helps developers solve real-world problems in the code domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

>-

Last verified on: 2026-07-16

Quick Facts

Category code
Works With Claude, GitHub Copilot
Source github/awesome-copilot
Stars ⭐ 34.1k
Last Verified 2026-07-16
Risk Level Low
mkdir -p ./skills/doc-and-modernize && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/doc-and-modernize/SKILL.md -o ./skills/doc-and-modernize/SKILL.md

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

Skill Content

# Documentation & Modernization


Two complementary workflows for a repository the user already has checked out

locally, bundled as one skill:


- **Documentation mode** — produce one definitive, cited architecture document

from the code on disk. Ideal for onboarding, system-design maps, or as the

evidence base for a modernization effort.

- **Modernization mode** — turn that architecture into a phased, safety-laddered

plan to upgrade, migrate, or rewrite a legacy system.


Mode selection


- If the user wants to **understand, document, map, research, or onboard onto** a

codebase, run **Documentation mode**.

- If the user wants to **modernize, migrate, upgrade, or rewrite** a system, run

**Modernization mode**. Modernization mode is self-sufficient: if no

architecture document exists yet, it runs the **Documentation mode** workflow

first (in the same pass), then continues straight through to the plan.


When in doubt, produce the architecture document first — it is the audited

evidence base both modes rely on.


Documentation mode


Generate one definitive, cited architecture document for a repository the user

already has checked out locally. The goal is a writeup someone could hand to a

new engineer as their onboarding reference — broad enough to cover the whole

system, deep enough on the hard parts to be useful, and trustworthy because

every claim traces back to a file on disk.


Why local-first


Reading from the local checkout (not the GitHub API or the web) is the deliberate

**default**. It is faster, free, avoids rate limits, and — most importantly — it

describes *the exact code in front of you* rather than whatever `main` happens

to look like remotely. The one tradeoff is that remote-only facts (star counts,

full CI run history, sibling repos) aren't visible. That's fine: state those as

out-of-scope or mark them `[UNVERIFIED]` rather than guessing.


Local-first is not local-*never*-remote: a web/API lookup is a deliberate

**last-resort fallback**, reserved for a fact that genuinely cannot be determined

from disk and that materially matters to the document. When you do reach for it,

flag the result clearly (e.g. `[UNVERIFIED]` / sourced-remotely) so the reader

knows it didn't come from the checkout, and never let it become the easy path

that displaces reading the code on disk.


Workflow


1. **Establish identity first.** Run `git remote -v`, `git branch --show-current`,

and `git log -1` so the document is anchored to a specific remote, branch, and

commit. A reader must be able to tell which snapshot this describes. Remote

URLs can contain embedded credentials (e.g. `https://<token>@github.com/...`)

— **redact any credentials/tokens** from the URL before recording it in the

document.

2. **Detect, don't assume.** Read the real manifests (`go.mod`, `package.json`,

`Cargo.toml`, `pyproject.toml`, `pom.xml`, etc.), the `Makefile`/task runner,

CI config, and any repo-specific agent or contributor docs (`AGENTS.md`,

`CONTRIBUTING`, `README`, `docs/`). These are the source of truth for the tech

stack and commands — prefer them over your prior knowledge of the framework.

3. **Map breadth, then drill into depth.** First build the whole-repo map (the

three lenses below), then pick the 2-3 hardest subsystems and go deep on them.

4. **Verify as you go.** Open the files you cite. If you reference a line number,

you should have actually read that line. Unsupported claims are worse than

omissions here — the whole value of this document is that it can be trusted.


Output structure


Produce a **single Markdown file** with the sections below, in this order. Adapt

the headings to the actual project (a CLI tool has no "frontend" lens — fold that

slot into whatever matters for that repo), but keep the three-lens shape and the

verification discipline.


#### Part 1 — Whole-repo technical deep-dive

- What the repository is (one paragraph, cited to README).

- Tech-stack detection table: lay

🎯 Best For

  • Claude users
  • GitHub Copilot users
  • Software engineers
  • Development teams
  • Tech leads

💡 Use Cases

  • Code quality improvement
  • Best practice enforcement

📖 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 or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Doc-And-Modernize to Your Work

    Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.

  4. 4

    Review and Refine

    Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.

❓ Frequently Asked Questions

Is Doc-And-Modernize compatible with Cursor and VS Code?

Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.

Do I need specific dependencies for Doc-And-Modernize?

Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.

How do I install Doc-And-Modernize?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/doc-and-modernize/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

Skipping validation

Always test AI-generated code changes, even for simple refactors.

Missing dependency updates

Check if the skill requires updated dependencies or new packages.

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