MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Create-Llms

Create-Llms是一款code方向的AI技能,核心价值是Create an llms,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/

Last verified on: 2026-05-30
mkdir -p ./skills/create-llms && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/create-llms/SKILL.md -o ./skills/create-llms/SKILL.md

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

Skill Content

# Create LLMs.txt File from Repository Structure


Create a new `llms.txt` file from scratch in the root of the repository following the official llms.txt specification at https://llmstxt.org/. This file provides high-level guidance to large language models (LLMs) on where to find relevant content for understanding the repository's purpose and specifications.


Primary Directive


Create a comprehensive `llms.txt` file that serves as an entry point for LLMs to understand and navigate the repository effectively. The file must comply with the llms.txt specification and be optimized for LLM consumption while remaining human-readable.


Analysis and Planning Phase


Before creating the `llms.txt` file, you must complete a thorough analysis:


Step 1: Review llms.txt Specification


- Review the official specification at https://llmstxt.org/ to ensure full compliance

- Understand the required format structure and guidelines

- Note the specific markdown structure requirements


Step 2: Repository Structure Analysis


- Examine the complete repository structure using appropriate tools

- Identify the primary purpose and scope of the repository

- Catalog all important directories and their purposes

- List key files that would be valuable for LLM understanding


Step 3: Content Discovery


- Identify README files and their locations

- Find documentation files (`.md` files in `/docs/`, `/spec/`, etc.)

- Locate specification files and their purposes

- Discover configuration files and their relevance

- Find example files and code samples

- Identify any existing documentation structure


Step 4: Create Implementation Plan


Based on your analysis, create a structured plan that includes:


- Repository purpose and scope summary

- Priority-ordered list of essential files for LLM understanding

- Secondary files that provide additional context

- Organizational structure for the llms.txt file


Implementation Requirements


Format Compliance


The `llms.txt` file must follow this exact structure per the specification:


1. **H1 Header**: Single line with repository/project name (required)

2. **Blockquote Summary**: Brief description in blockquote format (optional but recommended)

3. **Additional Details**: Zero or more markdown sections without headings for context

4. **File List Sections**: Zero or more H2 sections containing markdown lists of links


Content Requirements


#### Required Elements


- **Project Name**: Clear, descriptive title as H1

- **Summary**: Concise blockquote explaining the repository's purpose

- **Key Files**: Essential files organized by category (H2 sections)


#### File Link Format


Each file link must follow: `[descriptive-name](relative-url): optional description`


#### Section Organization


Organize files into logical H2 sections such as:


- **Documentation**: Core documentation files

- **Specifications**: Technical specifications and requirements

- **Examples**: Sample code and usage examples

- **Configuration**: Setup and configuration files

- **Optional**: Secondary files (special meaning - can be skipped for shorter context)


Content Guidelines


#### Language and Style


- Use concise, clear, unambiguous language

- Avoid jargon without explanation

- Write for both human and LLM readers

- Be specific and informative in descriptions


#### File Selection Criteria


Include files that:

- Explain the repository's purpose and scope

- Provide essential technical documentation

- Show usage examples and patterns

- Define interfaces and specifications

- Contain configuration and setup instructions


Exclude files that:

- Are purely implementation details

- Contain redundant information

- Are build artifacts or generated content

- Are not relevant to understanding the project


Execution Steps


Step 1: Repository Analysis


1. Examine the repository structure completely

2. Read the main README.md to understand the project

3. Identify all documentation directories and files

4. Catalog specification files a

🎯 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 Create-Llms 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 Create-Llms 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 Create-Llms?

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

How do I install Create-Llms?

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