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Make Readme

Community
gupsammy
make-readme

This skill should be used when the user asks to "create a README", "generate a README", "make a readme", "write a README for my project", "need a README", "add a README", "document my project", "set up project docs", "readme with badges".

Overview

Publishergupsammy
RepositoryClaudest
Skill namemake-readme
Stars
275
Forks
16
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Make Readme 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/gupsammy/Claudest/tree/main/plugins/claude-coding/skills/make-readme
  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/gupsammy/Claudest.git /tmp/Claudest
mkdir -p .claude/skills
cp -r /tmp/Claudest/plugins/claude-coding/skills/make-readme .claude/skills/make-readme
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Make Readme 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 Make Readme 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 Make Readme 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.

README Maker

Generate a professional README.md for any project — from 50-line minimal to 600-line comprehensive — with shields.io badges, styled headers, and structured sections. Interview the user to determine preferences, then produce the full README in one pass.

Step 1 — Detect Project Context

Before asking, scan the working directory for context clues:

  • Glob for package.json, pyproject.toml, Cargo.toml, go.mod — Read the first match to extract project name and version as pre-filled defaults
  • Check if README.md already exists — warn the user before overwriting
  • Note the primary language from the detected manifest file
  • For Plugin Collection detection: Glob for */.claude-plugin/plugin.json, */package.json in subdirectories. If 3+ sub-manifests are found, pre-select Plugin Collection in Step 2 and note the count.
  • If no manifest is found, skip pre-filling — rely entirely on user input in Step 2

Step 2 — Round 1 Interview (Always)

Ask 4 questions via AskUserQuestion. Pre-select options that match detected context where possible.

#headeroptions
1"Project type"Library/Package · CLI Tool · Web App/API · Desktop App · Plugin Collection
2"Language"Python · JavaScript/Node · Go · Rust
3"Depth"Minimal · Standard · Comprehensive
4"License"MIT · Apache-2.0 · GPL-3.0 · No license

Project type definitions:

  • Plugin Collection / Monorepo — a repo containing multiple installable components (plugins, packages, extensions) each with their own versions and descriptions. Automatically enables the component directory table (Step 4) and per-section version badges.

Depth definitions (include as option descriptions):

  • Minimal — small utilities, internal tools, or anything with one clear use case
  • Standard — open-source projects expecting contributors and external users
  • Comprehensive — mature projects with public APIs, active maintainers, or large communities

If depth = Minimal, skip Step 3 and proceed directly to Step 4.

Step 3 — Round 2 Interview (Standard / Comprehensive Only)

Ask 4 questions via AskUserQuestion:

#headermultiSelectoptions
1"Sections"trueFeatures list · Demo/Screenshots · Configuration docs · API Reference
2"Extras"trueContributing guide · Roadmap · FAQ · Acknowledgments
3"Badges"trueLicense · CI/Build · Coverage · Version/Release · Downloads · Stars+Forks · Per-section version
4"Style"falseSimple · Centered · Styled

Style definitions (include as option descriptions):

  • Simple — plain # Title with > tagline on next line
  • Centered — <div align="center"> block, title + tagline + badge row centered
  • Styled — centered + emoji prefix on every H2 and H3 heading. H2 defaults: ✨ Features, 🚀 Quick Start, ⚙️ Configuration, 📖 API, 🤝 Contributing, 🗺 Roadmap, ❓ FAQ, 📄 License. H3 for Plugin Collection components: choose from 🧠 🔍 💻 ✍️ 🤔 🎬 🔧 📦 🛠️ — match to component domain, not alphabetically.

Per-section version badge is pre-selected when project type = Plugin Collection.

Step 4 — Generate README

Minimal Structure

Five sections in order: title + tagline, description paragraph (what/who/why), Installation with syntax-highlighted copy-paste command, Usage with a minimal working example plus expected output as a comment, License with SPDX name and link to LICENSE file.

Standard / Comprehensive Structure

Assemble sections in this order, including only those selected in Round 2:

  1. Header block — apply Style from Round 2 (see styles below)
  2. Component directory table — Plugin Collection type only; a compact markdown table listing each component with an anchor link, version, and key skills or features. Glob for sub-directory manifests (*/.claude-plugin/plugin.json, */package.json) to auto-discover components and pre-fill the table. Place immediately after the header block.
  3. Table of Contents — Comprehensive only; auto-generate anchored links for every H2 section present. Because headings that contain images (version badges) or emoji have unreliable GitHub anchor generation, place <a id="slug"></a> on its own line before each target heading and link via #slug in the TOC. Example:
    <a id="installation"></a>
    ## 🚀 Installation  ![v1.0](...)
    TOC entry: - [Installation](#installation)
  4. Description — 2–3 sentences: problem solved, target user, key differentiator
  5. Features (if selected) — bulleted list of capabilities, present tense, parallel form
  6. Demo/Screenshots (if selected) — ![Demo](demo.gif) placeholder; instruct user to replace with an actual GIF or screenshot path
  7. Installation — prerequisites block, then numbered install steps
  8. Usage — single example for Standard; multiple headed examples for Comprehensive
  9. Configuration (if selected) — markdown table: Name · Type · Default · Description
  10. API Reference (if selected) — each function/endpoint: signature, one-line description, minimal example
  11. Contributing (if selected) — fork → clone → branch → commit → PR, 5 steps
  12. Roadmap (if selected) — - [ ] checklist of planned features
  13. FAQ (if selected) — H3 questions with short paragraph answers (2–3 entries)
  14. Acknowledgments (if selected) — linked credits
  15. License — SPDX name, link to LICENSE file

For Comprehensive: append [⬆ back to top](#readme) after each H2 section.

Header Block Styles

Simple: # Project Name followed by > One-line tagline.

Centered: Wrap title, tagline, and badge row in <div align="center">, using <h1> for title, <p> for tagline, and markdown badge syntax for badges.

Styled: Same as Centered; prepend the relevant emoji to every H2 and H3 per the emoji lists in Step 3. Add <a id="slug"></a> before each H2 and H3 that will be linked from the TOC or component directory table.

Badge URLs

Load references/badges.md when assembling the badge row — it contains the complete URL patterns for all supported services with substitution instructions. If Language = Other, omit the tech stack badge row unless the user provides their language name.

Per-section version badge (Plugin Collection and Library types):

![v{VERSION}](https://img.shields.io/badge/v{VERSION}-blue?style=flat-square)

Place inline with the component/section heading, separated by &nbsp;:

markdown
### 🧠 plugin-name &nbsp; ![v0.7.5](https://img.shields.io/badge/v0.7.5-blue?style=flat-square)

Always use <a id="slug"></a> before these headings since the badge makes the auto-generated GitHub anchor unpredictable.

Step 5 — Write and Summarize

Write to README.md in the current working directory. If README.md already exists, confirm overwrite before writing.

After writing, output a concise summary:

  • File path written
  • Sections included
  • Placeholder values the user must replace: USER/REPO, PKG, workflow filename, screenshot/GIF paths, and any description body text

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 Make Readme AI skill do?

This skill should be used when the user asks to "create a README", "generate a README", "make a readme", "write a README for my project", "need a README", "add a README", "document my project", "set up project docs", "readme with badges".

Why use Make Readme on TypingMind?

Because you install it once and use it with any model. Make Readme 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 Make Readme in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gupsammy/Claudest/tree/main/plugins/claude-coding/skills/make-readme. 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 Make Readme?

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 Make Readme?

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

Is the Make Readme AI skill free?

It is published on GitHub by gupsammy. 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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