How real projects summarise themselves for language models.
garrytanllms.txtgarrytan/gstack/gstack/llms.txt
read it, click through a flow, take screenshots, check console errors. - [/canary](canary/SKILL.md): Post-deploy canary monitoring. - [/careful](careful/SKILL.md): Safety guardrails for destructive commands. - [/claude-code](claude-code/SKILL.md): Claude Code
LangChainllms.txtlangchain-ai/langgraph/docs/llms.txt
/oss/python/langgraph/changelog-py.md) - [Checkpointers](https://docs.langchain.com/oss/python/langgraph/checkpointers.md) - [Choosing between Graph and Functional APIs](https://docs.langchain.com/oss/python/langgraph/choosing-apis.md) - [Deployment](https://docs.langchain.com/oss/python/langgraph/deploy.md) - [GRAPH_RECURSION_LIMIT](https://docs.langchain.com/oss/python/langgraph/errors/GRAPH_RECURSION_LIMIT.md) - [INVALID_CHAT_HISTORY](https://docs.langchain.com/oss/python/langgraph/errors/INVALID_CHAT_HISTORY.md)
OpenAIllms.txtopenai/openai-agents-python/docs/llms-full.txt
tracked, how to retrieve usage metadata, and how to forecast cost for different deployment patterns. - [Configuration](https://openai.github.io/openai-agents-python/config/): Centralized reference for tuning model settings, retries, rate limits, timeouts, logging
alirezarezvanillms.txtalirezarezvani/claude-skills/docs/llms-full.txt
/alirezarezvani/claude-skills - **Documentation site:** https://alirezarezvani.github.io/claude-skills/ - **Author:** Alireza Rezvani - **License:** MIT (free to extract, deploy, adapt, and redistribute) - **Distribution:** Claude Code plugin marketplace (`.claude-plugin/marketplace.json`) and the ClawHub registry
alirezarezvanillms.txtalirezarezvani/claude-skills/docs/llms.txt
llms-full.txt](https://alirezarezvani.github.io/claude-skills/llms-full.txt). Key facts for agents: - **License:** MIT. Free to extract, deploy, and adapt. - **Repository:** https://github.com/alirezarezvani/claude-skills - **Distribution:** Claude Code plugin marketplace + ClawHub registry. - **Design philosophy:** Algorithm
NirDiamantllms.txtNirDiamant/GenAI_Agents/llms.txt
same author - [agents-towards-production](https://github.com/NirDiamant/agents-towards-production): Taking agents from prototype to deployed product. - [RAG_Techniques](https://github.com/NirDiamant/RAG_Techniques): 41 notebooks on Retrieval-Augmented Generation. - [Agent_Memory_Techniques
NirDiamantllms.txtNirDiamant/agents-towards-production/llms.txt
production stack: orchestration, memory, retrieval, security and guardrails, observability and tracing, evaluation, deployment, GPU serving, fine-tuning and user interfaces. Each track stands alone and can be followed without
Evil0ctalllms.txtEvil0ctal/Douyin_TikTok_Download_API/llms.txt
administrator account, and one link parsed — from the console and from `curl`. - [Installation and deployment](https://github.com/Evil0ctal/Douyin_TikTok_Download_API/blob/main/documents/en/02-installation.md): The compose file service by service, every `DTK_*` variable, profiles, reverse proxies
AWSllms.txtaws/amazon-sagemaker-examples/llms.txt
Amazon SageMaker Examples > Example notebooks for building, training, and deploying models with the Amazon SageMaker > Python SDK **v3**. This repository targets **v3 only**; `pip install sagemaker` installs
A markdown file at a website's root that gives language models a short guide to the site and links to the pages worth reading.
The same idea with the content included, so an agent can read the documentation in one request.
Agents and tools that fetch documentation on someone's behalf. It's a proposal, not a standard, and support varies.
Start with a one-line summary, then sections of links with a sentence each. The examples here show what real projects do.
