AmrDabPipes a download into a shellllms.txtclawdcursor
AmrDab/clawdcursor/docs/llms.txt
clawdcursor > Lets AI use your computer in real time. A local MCP server that gives a > tool-calling agent the real desktop: you take the fastest way into each
llms.txt examples
How real projects summarise themselves for language models.
Your trail
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.
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AmrDabPipes a download into a shellllms.txt
Googlellms.txtthree-live / examples
google/three-live/examples/llms-full.txt
magnetic-snap` + `shared-room`) ```html const deck = new THREE.Mesh( new THREE.CylinderGeometry(1.4, 1.4, 0.04, 48), new THREE.MeshStandardMaterial({ color: 0x0f172a, roughness: 0.85 }) ); deck.name = 'walkable-deck'; deck.position.set(0, -0.45, -1.2); deck.userData.xr = { navigable: 'walkable', explode: false, sync: false }; scene.add(deck); const chassis = new THREE.Mesh( new THREE.CylinderGeometry(0.28, 0.32, 0.16, 36), new THREE.MeshStandardMaterial({ color: 0x334155, metalness: 0.75, roughness: 0.25 }) ); chassis.name = 'turbine-chassis'; chassis.position.set(0, 0.75, -1.2); chassis.userData.xr = { dimensions: { unit: 'cm', axes: ['x', 'y', 'z'] }, snapSocket: { tag: 'rotor-bay', radius: 0.18, highlightColor: '#22c55e' }, explode: false }; scene.add(chassis); const shroud = new THREE.Mesh( new THREE.CylinderGeometry(0.24, 0.26, 0.22, 36), new THREE.MeshStandardMaterial({ color: 0x38bdf8, metalness: 0.65, roughness: 0.2 }) ); shroud.name = 'upper-shroud'; shroud.position.set(0, 0.96, -1.2); shroud.userData.xr = { grab: '1-hand', snapTo: { socketTag: 'rotor-bay', radius: 0.18, angleDeg: 15 }, explode: { direction: [0, 1, 0], distance: 0.32, layer: 2, leaderLine: true }, gaze: { dwellMs: 1000, action: 'select' }, anchor: { id: 'upper-shroud', persist: true }, sync: true }; scene.add(shroud); const key = new THREE.DirectionalLight(0xffffff, 2.2); key.position.set(2, 4, 3); scene.add(key); scene.add(new THREE.AmbientLight(0xffffff, 0.6)); ``` -------------------------------------------------------------------------------- ## 5. Closed-Loop AI Agent Verification (`three-live-cli` & MCP Server) AI coding agents can validate, visually inspect, and simulate
neoneyeReads credentialsllms.txtPlanExe
neoneye/PlanExe/public/llms.txt
billing, and API key management. - No public REST API on this host. AI-facing MCP server: - https://mcp.planexe.org/mcp - https://mcp.planexe.org/mcp/ (for clients that require trailing slash) Agent discovery
AceDataCloudllms.txtNexior
AceDataCloud/Nexior/public/llms.txt
Documentation](https://docs.acedata.cloud): Comprehensive guides and API reference - [GitHub](https://github.com/AceDataCloud): Open source MCP servers and clients
ffrolivallms.txtgflow-cli
ffroliva/gflow-cli/llms.txt
gflow-cli > Python CLI and MCP server for Google Flow — drives Veo (image-to-video, text-to-video, reference-to-video) and Imagen (text-to-image) generations from the terminal
mcpwarellms.txtcross-code-organizer
mcpware/cross-code-organizer/llms.txt
Cross-Code Organizer (CCO) > Organize Claude Code memories, skills, MCP servers, commands, agents, rules, hooks, plans, sessions, and config across scopes. ## Docs - [AI Index](https://github.com/mcpware/cross-code-organizer/blob/main/AI_INDEX.md): Canonical AI-oriented
AGIHuntPipes a download into a shellllms.txtblurt / site
AGIHunt/blurt/site/llms.txt
Handed off. Copy Full Text copies the note itself. - Agents report through Blurt's MCP server: In progress, then To verify (mark_fixed). An agent never marks an item done
Goldentriillms.txtAgentRecall-X
Goldentrii/AgentRecall-X/llms.txt
user from Claude Code to Codex to Cursor and any MCP client. Use the free hosted server (https://agentrecallx.com/mcp, open sign-up with GitHub) or run it locally
gizmodatallms.txtgizmosql
gizmodata/gizmosql/docs/llms.txt
docs.gizmosql.com/opentelemetry.md): traces and metrics export ## Integrations - [Extensions & Tools](https://docs.gizmosql.com/integrations.md): ecosystem integrations - [MCP Server](https://github.com/gizmodata/gizmosql-mcp): Model Context Protocol server and Claude Desktop extension so AI agents
griddynamicsPipes a download into a shellllms.txtrosetta
griddynamics/rosetta/llms-full.txt
upgrade = switch to latest. All releases uploaded to RAGFlow as separate datasets; MCP serves latest-stable only. Server-controlled VERSION (clients don't set it) → managed rollouts, no drift
openaccountantsllms.txtopenaccountants
openaccountants/openaccountants/llms-full.txt
/api/bundle/CA-ON` **Option 2 — Raw file URL:** Read individual skill files via raw.githubusercontent.com. **Option 3 — MCP server:** `https://www.openaccountants.com/api/mcp` — provides `list_skills`, `get_skill`, `get_skill_sections`, and structured
openaccountantsllms.txtopenaccountants
openaccountants/openaccountants/llms.txt
/api/bundle/CA-ON` **Option 2 — Raw file URL:** Read individual skill files via raw.githubusercontent.com. **Option 3 — MCP server:** `https://www.openaccountants.com/api/mcp` — provides `list_skills`, `get_skill`, `get_skill_sections`, and structured
brycewang-stanfordllms.txtStatsPAI
brycewang-stanford/StatsPAI/llms.txt
install statspai` (`pip install "statspai[fixest,plotting]"` for high-dimensional fixed effects and figures). MCP server: `statspai-mcp`. CLI: `statspai`. ## Start here - [AGENTS.md](AGENTS.md): how an agent discovers, calls
oxbshwllms.txtwatch-skill
oxbshw/watch-skill/llms.txt
Install the workspace: ```bash npx --yes @deepwatch/cli setup npx --yes @deepwatch/cli web --workspace ./my-project ``` MCP server configuration: ```json { "mcpServers": { "watch-skill": { "command": "uvx", "args": ["--from", "watch-skill[standard]", "watch-skill
mindroom-aiReads credentialsllms.txtmindroom / references
mindroom-ai/mindroom/skills/mindroom-docs/references/llms-full.txt
connect to 100+ tools, including Gmail, Google Drive, Slack, Jira, Confluence, GitHub, and any MCP server ([Tools](https://docs.mindroom.chat/tools/), [MCP Servers](https://docs.mindroom.chat/mcp/)), and search knowledge bases built
mindroom-aillms.txtmindroom / references
mindroom-ai/mindroom/skills/mindroom-docs/references/llms.txt
docs.mindroom.chat/deployment/bridges/) - [Telegram Bridge](https://docs.mindroom.chat/deployment/bridges/telegram/) - [Enable & Configure Tools](https://docs.mindroom.chat/tools/) - [MCP Servers](https://docs.mindroom.chat/mcp/) - [Dynamic Tools](https://docs.mindroom.chat/tools/dynamic-tools/) - [Minimal Agent Mode](https://docs.mindroom.chat/tools/agent-cli/) - [Background
HigangsshPipes a download into a shellllms.txthomebutler
Higangssh/homebutler/llms.txt
network scan, Wake-on-LAN - Multi-server SSH management and remote deploy - Built-in MCP server for AI tools - JSON output for scripts and agents ## Primary workflow For monitoring
daymoniollms.txtdaymon
daymonio/daymon/docs/llms-full.txt
fails (ideal for monitoring) - **never**: Never nudge How it works: 1. Task completes in MCP server or sidecar 2. Checks task's nudge_mode via `shouldNudgeTask()` 3. Checks quiet hours
daymoniollms.txtdaymon
daymonio/daymon/docs/llms.txt
shell) - **Sidecar** standalone Node.js HTTP server (cron scheduler, file watchers, task execution, embeddings) - **MCP Server** (stdio transport) for Claude Desktop and Claude Code integration - **Shared modules** used by both sidecar
mediar-aillms.txtmcp-server-macos-use
mediar-ai/mcp-server-macos-use/llms.txt
mcp-server-macos-use > MCP server for macOS desktop automation using native accessibility APIs ## Overview mcp-server-macos-use is a Model Context Protocol (MCP) server written in Swift that
About llms.txt
What is llms.txt?
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.
What's llms-full.txt?
The same idea with the content included, so an agent can read the documentation in one request.
Who reads it?
Agents and tools that fetch documentation on someone's behalf. It's a proposal, not a standard, and support varies.
How do I write one?
Start with a one-line summary, then sections of links with a sentence each. The examples here show what real projects do.