skills/create-llms/SKILL.md
Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/
npx skillsauth add williamlimasilva/.copilot create-llmsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
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.
Before creating the llms.txt file, you must complete a thorough analysis:
.md files in /docs/, /spec/, etc.)Based on your analysis, create a structured plan that includes:
The llms.txt file must follow this exact structure per the specification:
Each file link must follow: [descriptive-name](relative-url): optional description
Organize files into logical H2 sections such as:
Include files that:
Exclude files that:
llms.txt file in the repository root/llms.txt)# [Repository Name]
> [Concise description of the repository's purpose and scope]
[Optional additional context paragraphs without headings]
## Documentation
- [Main README](README.md): Primary project documentation and getting started guide
- [Contributing Guide](CONTRIBUTING.md): Guidelines for contributing to the project
- [Code of Conduct](CODE_OF_CONDUCT.md): Community guidelines and expectations
## Specifications
- [Technical Specification](spec/technical-spec.md): Detailed technical requirements and constraints
- [API Specification](spec/api-spec.md): Interface definitions and data contracts
## Examples
- [Basic Example](examples/basic-usage.md): Simple usage demonstration
- [Advanced Example](examples/advanced-usage.md): Complex implementation patterns
## Configuration
- [Setup Guide](docs/setup.md): Installation and configuration instructions
- [Deployment Guide](docs/deployment.md): Production deployment guidelines
## Optional
- [Architecture Documentation](docs/architecture.md): Detailed system architecture
- [Design Decisions](docs/decisions.md): Historical design decision records
The created llms.txt file should:
development
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can handle. DO NOT use for local-only vector stores (Chroma, FAISS, pgvector) or pure keyword search with no semantic component.
development
Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.
devops
Query AWS resources using natural language. Covers EC2, S3, RDS, Lambda, ECS, EKS, Secrets Manager, IAM, VPC, networking, messaging, and more. Strictly read-only — no writes, deletes, or mutations.
devops
Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.