How real projects summarise themselves for language models.
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
respeak-iollms.txtrespeak-io/episko/site/llms.txt
third party. Built with Tauri v2 — a Rust backend with a vanilla-TypeScript frontend. ## Requirements - The `claude` CLI (Claude Code) installed and on your PATH, with your own Anthropic access
sandraschiReads credentialsllms.txtsandraschi/advanced-memory-mcp/llms-full.txt
FTS5) │ │ LanceDB (vector embeddings) │ │ Markdown files (human-readable) │ └──────────────────────────────────────┘ ↓ Webapp (port 10704/10705) ``` ## Ports | Service | Port | |---------|------| | Frontend (Vite React) | 10704 | | Backend (FastAPI + MCP HTTP) | 10705 | | MCP SSE transport | 10732 | ## MCP Tools
AishwaryShrivastavllms.txtAishwaryShrivastav/vibe-testing/llms-full.txt
SvelteKit, Nuxt, Vue, Express, Fastify) - Detects monorepos (Turborepo, pnpm, yarn, Lerna) and finds frontend app - Parses all routes (file-based and code-defined, including src/ variants) - Extracts forms, buttons, dialogs
CLOUDWERX-DEVPipes a download into a shellllms.txtCLOUDWERX-DEV/gpt-image-1-mcp/.docs/llms-full.txt
# Example Clients Source: https://modelcontextprotocol.io/clients A list of applications that support MCP integrations
ssdeanxReads credentialsllms.txtssdeanx/secure-rag-multi-agent/llms.txt
This repository demonstrates production-ready governed RAG using Mastra's agentic architecture, combining Next.js frontend, Qdrant vector database, and OpenAI LLMs with comprehensive security controls for enterprise knowledge bases. ## Documentation
akustikrauschllms.txtakustikrausch/gonzales/llms.txt
automations - Dark/light theme support - Mobile-responsive design ## Technical Stack - Backend: Python, FastAPI, SQLAlchemy, SQLite - Frontend: React, TypeScript, Vite, Recharts, TanStack Query - Speed Testing: Ookla Speedtest CLI (third-party, separate license
heymiReads credentialsllms.txtheymi/aldus-palace/llms-full.txt
system inferred it. **It follows you.** One file, one API. Claude, Cursor, your own frontend, a script you write. ## Who it's for and when
sagarithmllms.txtsagarithm/sagarithm-kit/llms.txt
certification. - **DevOps, SRE & Cloud Governance**: Containerization, OpenTelemetry observability, SLO/SLI tracking, and release governance. - **Frontend & UI/UX Engineering**: Accessible design systems, state machines, and optimal Core Web Vitals. - **Marketing, AEO & Growth
webrenewReads credentialsllms.txtwebrenew/unicon/LLMs.txt
mode support **Live Site:** https://unicon.sh **Repository:** https://github.com/WebRenew/unicon **License:** MIT ## Tech Stack ### Frontend - **Framework:** Next.js 16 (App Router, React 19) - **Styling:** Tailwind CSS 4 - **UI Components:** Radix
bntvllntDeletes or force-pushesllms.txtbntvllnt/agent-skills/llms-full.txt
Detect domain: software 3. Select 10 perspectives: - Architect - DevOps - Security Engineer - Database Engineer - Frontend Developer - QA Engineer - Performance Engineer - SRE - Tech Lead - CTO 4. Launch all 10 agents in parallel
jimy-rReads credentialsllms.txtjimy-r/agent-workspace-architecture/docs/llms-full.txt
# Agent Workspace Architecture — full text > A redacted reference for building agent workspaces: the roles
Nanako0129llms.txtNanako0129/TokenBar-Tauri/llms.txt
updater ## Architecture - [Tauri shell source](https://github.com/Nanako0129/TokenBar/tree/main/src-tauri): Rust backend, native tray, animation frames - [Frontend source](https://github.com/Nanako0129/TokenBar/tree/main/src): React + Vite, react-three-fiber 3D graph - [tokscale upstream](https://github.com
ariaxhanllms.txtariaxhan/kernel-claude/llms.txt
kernel:human-pass`: the acceptance pass a human runs against a real build. - `/kernel:frontend` · `$kernel:frontend`: art direction derived from product and audience, not a house style. - `/kernel:marketing
nirholasllms.txtnirholas/task-queue-system/llms-full.txt
task files in `prompts/ /NN- .md` following the format in `PROMPT-SPEC.md`. **Example structure**: ``` prompts/ frontend/ 01-dashboard-layout.md 02-responsive-design.md 03-dark-mode.md backend/ 01-api-routes.md 02-database-schema.md 03-websocket-server.md tests/ 01-unit-tests.md 02-integration-tests.md ``` ### 3. Generate TASK-MANIFEST.md Run this
nirholasllms.txtnirholas/vscode-google-cloud/llms-full.txt
gcloud compute zones list` ## Examples ### Run 5 instances for different projects ```bash ./gcp-vscode.sh create frontend-app ./gcp-vscode.sh create backend-api ./gcp-vscode.sh create ml-training e2-standard-16 # beefier VM ./gcp-vscode.sh
BubblegunnPipes a download into a shellllms.txtBubblegunn/ai-slop-linter/llms-full.txt
Founding engineer, [Cendra](https://cendra.ai)** (Aug 2025 to Jul 2026): primary author of the frontend. ``` There is no dependable signal in the bold span alone that separates this structural
nodaroaillms.txtnodaroai/app.nodaro.ai/docs/llms.txt
step DAGs that run autonomously on a server. Nodaro is a Fastify backend + Vite/React frontend, source-available under the [Nodaro Sustainable Use License](https://github.com/nodaroai/app.nodaro.ai/blob/main/LICENSE). The backend is REST
daftAI2026llms.txtdaftAI2026/awesome-jev/public/llms-full.txt
# Awesome JEV > Awesome JEV (awesome-jev): a free GitHub directory for TypeSafe Jev / System
bablsoftllms.txtbablsoft/accessflow/website/llms.txt
payloads, WebSocket events - [Backend](https://raw.githubusercontent.com/bablsoft/accessflow/main/docs/05-backend.md): Modular backend, proxy engine, workflow state machine - [Frontend](https://raw.githubusercontent.com/bablsoft/accessflow/main/docs/06-frontend.md): React/Vite layout, SQL editor, TanStack & Zustand - [Security](https://raw.githubusercontent.com/bablsoft/accessflow/main/docs/07-security.md): JWT, SAML
bradleybond512llms.txtbradleybond512/crystal-ball/public/llms-full.txt
temporal windows against learned baselines with per-term spike multipliers and cooldowns ## Tech Stack - **Frontend**: TypeScript, Vite, MapLibre GL JS, deck.gl (WebGL), D3.js, Supercluster (marker clustering), Transformers.js (browser-side
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.
