How real projects summarise themselves for language models.
Leonxlnxllms.txtLeonxlnx/taste-skill/skills/llms.txt
preserved for projects depending on its exact behavior. The current default is `design-taste-frontend` (v2 experimental). gpt-taste: Elite Awwwards-level frontend design and GSAP motion skill for premium
koala73llms.txtkoala73/worldmonitor/public/llms.txt
www.worldmonitor.app/api/download?platform=macos-x64): macOS x64 build - [Linux AppImage](https://www.worldmonitor.app/api/download?platform=linux-appimage): Linux .AppImage ## Tech Stack - **Frontend**: TypeScript, Vite, MapLibre GL JS, deck.gl, D3.js, Transformers.js (browser-side ML) - **Internationalization**: i18next with lazy
DeusDatallms.txtDeusData/codebase-memory-mcp/docs/llms.txt
native executable with an authenticated integration asset; the UI variant adds one content-addressed frontend pack. All processing is local — the embeddings run on-device; there is no embedded
alirezarezvanillms.txtalirezarezvani/claude-skills/docs/llms-full.txt
Fullstack, AI/ML, DevOps, security, data, and QA skills. Includes senior-role skills (architect, backend, frontend, fullstack, DevOps, data engineer/scientist, ML, computer vision, prompt, QA, security, SecOps), cloud architects (AWS/Azure/GCP
anbeimellms.txtanbeime/skill/public/llms-full.txt
group** ⭐⭐ - 同行顾问团 #### 💼 产品与项目管理（2个） - **product-manager-toolkit** ⭐⭐⭐ - 产品经理工具包 - **sales-ai-assistant** ⭐⭐ - 销售AI助手 #### 🎨 设计与可视化（4个） - **frontend-design** ⭐⭐⭐ - 前端界面设计 - **ai-drawio** ⭐⭐⭐⭐ - 流程图绘制 - **pop-up-book-illustration** ⭐⭐ - 3D纸艺插画 - **web-to-app** ⭐⭐ - 网页转桌面应用 #### 📑 文档处理
tw93llms.txttw93/Waza/llms.txt
requirement and produce a decision-complete plan - `ui`: build or polish a distinctive frontend interface - `check`: review diffs, releases, publishing, and project readiness - `hunt`: diagnose a bug to root cause
txn2llms.txttxn2/kubefwd/docs/llms-full.txt
sudo -E kubefwd svc -n default --tui # Multiple namespaces sudo -E kubefwd svc -n frontend,backend,data --tui # Filter by label sudo -E kubefwd svc -n default
LangChainllms.txtlangchain-ai/langgraphjs/docs/docs/llms.txt
debug mode](https://docs.langchain.com/oss/javascript/langgraph/cloud/how-tos/stream_debug/) - [How to stream multiple modes](https://docs.langchain.com/oss/javascript/langgraph/cloud/how-tos/stream_multiple/) ### Frontend & Generative UI With LangGraph Platform you can integrate LangGraph agents into your React applications and colocate
Googlellms.txtgoogle-gemini/genai-processors/llms.txt
backend, default to localhost. * create local webpage with JS or TS for the frontend, base64 decode the media data when receiving. * All messages sent and received are JSON-serialized ProcessorParts
NVIDIAllms.txtNVIDIA/aistore/docs/llms.txt
buckets - [S3 Compatibility](https://aistore.nvidia.com/docs/s3compat): Amazon S3 API support — backend and frontend modes - [ETL](https://aistore.nvidia.com/docs/etl): Extract, Transform, Load — run transforms on storage nodes alongside data - [Batch Operations
NVIDIAllms.txtNVIDIA/cudnn-frontend/llms.txt
cuDNN Frontend > NVIDIA cuDNN Frontend (FE): a header-only C++ library and Python package (`nvidia-cudnn-frontend`, import `cudnn`) exposing the cuDNN Graph API, plus open-source CuTeDSL kernels (SDPA/Flash
reticlehqllms.txtreticlehq/reticle/llms.txt
instead of a confident green. ## How it works (architecture) **One engine, many frontends.** The verification engine (session + tools + flows + the verdict artifact) is transport-agnostic; every consumer hits the same
manojmallickllms.txtmanojmallick/sigmap/llms-full.txt
outputs = ["copilot"] adapters = null srcDirs = ["src","app","lib","packages","services","api","server","client","web","frontend","backend","desktop","mobile","shared","common","core","workers","functions","lambda","cmd","pages","components","hooks","routes","controllers
LinklyAIllms.txtLinklyAI/best-skills/llms.txt
head -11 ``` ### Identity - `skill` is the short name and is not unique: three unrelated `frontend-design` entries can exist. Always show `vendor` or `skill_key` next to it. - `skill
nirholasllms.txtnirholas/XActions/llms-full.txt
handlers scripts/ 95 browser console scripts, plus build and maintenance scripts dashboard/ Static HTML frontend extension/ Chrome and Edge extension (Manifest V3) skills/ 50 Agent Skills (skills/*/SKILL.md) tests/ Vitest
Ikalus1988llms.txtIkalus1988/MisakaNet/docs/llms-full.txt
badges](https://misakanet.org/evidence-badges/) - [Guide: fanuc-knowledge-card-2026-07](https://misakanet.org/fanuc-knowledge-card-2026-07/) - [Guide: frontend-status](https://misakanet.org/frontend-status/) - [Guide: glama-mcp-publish-guide](https://misakanet.org/glama-mcp-publish-guide/) - [Guide: glama
HuangYuChuhllms.txtHuangYuChuh/ComfyUI_Skills_OpenClaw/docs/llms-full.txt
ComfyUI APIs, waits and downloads. - `scripts/server_manager.py`: Manage multi-server settings from CLI. - `ui/`: FastAPI + frontend dashboard for server/workflow/schema management. - `data/ / /workflow.json`: normalized ComfyUI API workflow. - `data/ / /schema.json`: parameter exposure
jamditisllms.txtjamditis/claude-skills-journalism/docs/llms.txt
Electron app patterns - python-pipeline, data pipeline workflows - web-scraping, content extraction - zero-build-frontend, no-build web apps - mobile-debugging, Eruda, vConsole, remote debug - accessibility-compliance, WCAG, alt text
aayushchllms.txtaayushch/laya/landing/llms.txt
before the user opens the app. ## Architecture - Desktop app: Tauri (Rust) + SvelteKit (Svelte 5) frontend - Engine: Python FastAPI backend with async SQLite + ChromaDB - Integration gateway: n8n running as a local
VasiHemanthllms.txtVasiHemanth/tokentelemetry/llms.txt
Vasi - **Category:** AI observability, LLM monitoring, autonomous agent observability, developer tools - **Tech stack:** Node.js (frontend: Next.js 16), Python (backend: FastAPI) - **Platforms:** macOS, Linux, Windows - **Privacy:** Local-first — your logs, prompts
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.
