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
NirDiamantllms.txtNirDiamant/Prompt_Engineering/llms.txt
Retrieval-Augmented Generation. - [agents-towards-production](https://github.com/NirDiamant/agents-towards-production): Taking agents from prototype to deployed product. - [Course, Prompt to Production](https://diamant-ai.com/courses): Building software with AI, taught systematically. - [Newsletter
anbeimellms.txtanbeime/skill/public/llms-full.txt
practices, upgrade-stripe - **Supabase** (1个) - postgres-best-practices - **Expo** (3个) - expo-app-design, expo-deployment, upgrading-expo - **Sentry** (7个) - code-review, commit, create-pr, find-bugs 等 - **Better Auth** (3个
KnockOutEZllms.txtKnockOutEZ/wigolo/llms.txt
docs/sdks.md): wigolo-sdk (TS), wigolo (Python), LangChain/CrewAI/LlamaIndex/Vercel AI SDK integrations - [Self-hosting](docs/self-hosting.md): VPS/Docker deployment, tokens, wiring n8n and remote agents, resource sizing, datacenter-IP limits, reverse proxies - [Skills](docs/skills.md
melgarafaelllms.txtmelgarafael/DeskcommCRM/public/llms.txt
setup of every integration - [Self-host on a VPS](https://github.com/melgarafael/DeskcommCRM/blob/main/docs/deploy-selfhost/README.md): docker compose deploy on any VPS ## Repository - [GitHub](https://github.com/melgarafael/DeskcommCRM): source code, issues, discussions
LangChainllms.txtlangchain-ai/langgraphjs/docs/docs/llms.txt
server locally and interact with it using REST API and LangGraph Studio Web UI. - [Deploy with LangGraph Cloud Quickstart](https://docs.langchain.com/oss/javascript/langgraph/cloud/quick_start/): Deploy a LangGraph app using LangGraph Cloud. ## Concepts
Microsoftllms.txtmicrosoft/skills/docs/llms-full.txt
application development. It combines production-grade infrastructure with friendly interfaces for building agents, deploying models, and developing generative AI applications. ## Key Information **Portal**: https://ai.azure.com **SDK Installation**: ```bash pip install
Microsoftllms.txtmicrosoft/skills/docs/llms.txt
application development. It provides a comprehensive set of AI capabilities for building agents, deploying models, and developing generative AI applications with built-in enterprise-readiness including tracing, monitoring, evaluations
AWSllms.txtaws/sagemaker-python-sdk/llms.txt
Instructions for LLM agents (SageMaker Python SDK) - **SDK-first:** for any SageMaker task (train, deploy, process, pipelines), use the **SageMaker Python SDK v3** as the primary interface. Do not drop
tractorjuicellms.txttractorjuice/arc-kit/docs/llms.txt
# ArcKit > ArcKit is The Enterprise Architecture Governance Harness — an open-source toolkit covering strategy
mrgooniellms.txtmrgoonie/claudekit-skills/.claude/skills/claude-code/llms.txt
references/configuration.md ### Enterprise Features - Identity & Access Management (SSO, RBAC) - Security & Compliance (sandboxing, audit logging, certifications) - Deployment options (Bedrock, Vertex AI, self-hosted) - Monitoring & Analytics - Network configuration - See: references/enterprise-features.md ### IDE Integration - Visual
Googlellms.txtgoogle-gemini/genai-processors/llms.txt
framework logic. * `development/caching.md` / `development/tracing.md`: Optimizations and tracing. * `rapid-prototyping/ai-studio.md` / `rapid-prototyping/cli.md`: App deployment references. * **Examples** (`examples/` directory): * `chat.py`: Standard chatbot architecture. * `live_simple_cli.py` / `realtime_simple_cli.py`: Streaming architectures. * `research/`: Modular multi
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.
