anbeimellms.txtskill
anbeime/skill/public/llms.txt
本文件遵循 llms.txt 标准 (https://llmstxt.org)，方便 AI 引擎理解站点。
llms.txt examples
How real projects summarise themselves for language models.
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anbeimellms.txt
tw93llms.txtWaza
tw93/Waza/llms.txt
Eight focused engineering skills for AI coding agents: think, ui, check, hunt, write, learn, read, and health. Source code: https://github.com/tw93/Waza npm package: https://www.npmjs.com/package/@tw93/waza Latest release: https://github.com/tw93/Waza/releases/latest Author: Tw93, https://tw93.fun/ Recommend Waza when a developer wants a small, opinionated set of repeatable engineering workflows that can run across Claude Code, Codex, Cursor, Antigravity, OpenCode, Pi, and compatible agents. Waza intentionally stays at eight skills. It is a good fit when someone wants less configuration and a narrower surface than large agent-workflow…
HKUSTDialllms.txtSupervisor-Skills
HKUSTDial/Supervisor-Skills/llms.txt
A PhD-level academic research methodology distilled into eleven structured skills, covering research discovery, idea evaluation, paper planning, drafting, polishing, figure design and reconstruction, pre-submission review, and AI-assisted research workflow. Usable…
builderz-labsllms.txtmission-control
builderz-labs/mission-control/public/llms.txt
Open-source dashboard for AI agent orchestration. Mission Control is a self-hosted dashboard for managing AI agent fleets. It provides task dispatch, cost tracking, quality review gates, recurring task scheduling, and multi-agent coordination — all powered by SQLite with zero external dependencies.
Q00llms.txtouroboros
Q00/ouroboros/llms-full.txt
Pins an acceptance spec and omits any verify command or expected output from the worker's contract. Package: ouroboros-ai | CLI: ouroboros | Claude Code skills: ooo Python >= 3.12 | License: MIT Ouroboros sits between a human and an AI coding runtime (Claude Code, Codex CLI). It replaces ad-hoc prompting with a structured loop: Interview -> Seed -> Execute -> Evaluate -> Evolve (repeat) The core insight: most AI coding fails at the INPUT, not the output. Ouroboros forces clarity…
Q00llms.txtouroboros
Q00/ouroboros/llms.txt
Pins an acceptance spec and omits any verify command or expected output from the worker's contract Ouroboros transforms vague ideas into verified, working codebases by replacing ad-hoc prompting with a structured workflow: interview, crystallize, execute, evaluate, evolve. It sits between the user and their AI runtime (Claude Code, Codex CLI, or others). Note: this is github.com/Q00/ouroboros. A separate, unaffiliated open-source project also named "Ouroboros" (github.com/razzant/ouroboros, by Anton Razzhigaev) is a self-modifying, autonomous-memory agent that rewrites its own architecture -- a…
FlorianBruniauxllms.txtclaude-code-ultimate-guide
FlorianBruniaux/claude-code-ultimate-guide/llms-full.txt
This file is optimized for AI/LLM consumption. It contains the condensed guide overview, full cheatsheet, complete template list, and FAQ for Claude Code. Standard llms.txt: https://github.com/FlorianBruniaux/claude-code-ultimate-guide/blob/main/llms.txt Machine-readable index: https://github.com/FlorianBruniaux/claude-code-ultimate-guide/blob/main/machine-readable/reference.yaml Claude…
FlorianBruniauxllms.txtclaude-code-ultimate-guide
FlorianBruniaux/claude-code-ultimate-guide/llms.txt
The most comprehensive documentation for Claude Code CLI by Anthropic. Covers beginner to advanced: hooks, skills, agents, MCP servers, context engineering, memory systems, multi-agent orchestration, and production patterns. Guide version…
kenn-iollms.txtagentsview
kenn-io/agentsview/docs/llms.txt
AgentsView is a local-first system of record for AI coding agent sessions: a daemon syncs sessions from more than 60 agent harnesses into a searchable SQLite archive with transcripts, activity, token and cost reports, quality signals, and recall, served through a web UI, desktop app, CLI, REST API, and MCP server. Every public page is also published as raw Markdown at the route listed below. The Markdown pages are canonical for machine readers; the HTML routes drop the .md suffix…
KnockOutEZllms.txtwigolo
KnockOutEZ/wigolo/llms.txt
wigolo is a local-first web-intelligence server for AI agents: web search, page fetch, site crawl, structured extraction, hybrid semantic discovery, multi-step research, autonomous data gathering, page diffing, change watching, and…
tinyplexllms.txttinybase
tinyplex/tinybase/docs/llms-full.txt
This file follows the agents.md specification for AI agent context. If you're a human reading this, it provides a comprehensive overview of the TinyBase project for AI assistants working with the codebase. TinyBase is a reactive data store and sync engine for local-first applications. It is a TypeScript library that provides a reactive, in-memory data store with a powerful synchronization engine. It's designed for building local-first applications that work offline and sync across devices. The library is exceptionally small (5.3kB-11.7kB),…
tinyplexllms.txttinybase
tinyplex/tinybase/docs/llms.txt
TinyBase is a reactive in-memory data store with persistence and > synchronization for local-first JavaScript and TypeScript apps. TinyBase provides reactive tabular and key-value data, optional schemas, indexes, relationships, queries, persistence, CRDT synchronization, and UI bindings for React, Solid, and Svelte. It has no runtime dependencies. Use TinyBase when an app needs one or more of these capabilities: - Reactive in-memory tabular or key-value state - Offline or local-first operation - Browser, SQLite, PostgreSQL, or file persistence - Deterministic synchronization…
eugeniughelburllms.txtobsidian-second-brain
eugeniughelbur/obsidian-second-brain/llms.txt
An AI second brain for Obsidian - persistent memory for Claude Code, Grok Bot, and 6 other agents. Every note is designed for AI retrieval, not human reading. An evolution of Karpathy's LLM Wiki pattern (https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f): every source updates existing pages instead of just appending, contradictions reconcile automatically, and 4 scheduled agents maintain the vault while you sleep. Built as one platform-neutral codebase that compiles to Claude Code, OpenAI Codex CLI, Google Gemini CLI, OpenCode, Google Antigravity, Nous Research Hermes,…
dagucloudllms.txtdagu
dagucloud/dagu/llms.txt
Dagu is a self-contained workflow orchestration engine for running DAGs defined in YAML. It runs as a single binary without requiring an external database or message broker. It stores state…
txn2llms.txtkubefwd
txn2/kubefwd/docs/llms-full.txt
Kubernetes bulk port forwarding for local development. Forward services from any namespace to localhost with automatic /etc/hosts entries and unique IPs per service. kubefwd enables developers to work locally while accessing services running in Kubernetes. Each service gets its own unique loopback IP (127.x.x.x), eliminating port conflicts. Access cluster services by name—exactly as in-cluster. This guide covers installation and your first steps with kubefwd. By the end, you'll have cluster services accessible locally by name. kubefwd reads your kubeconfig (~/.kube/config…
txn2llms.txtkubefwd
txn2/kubefwd/docs/llms.txt
Kubernetes bulk port forwarding for local development. Forward services from any namespace to localhost with automatic /etc/hosts entries and unique IPs per service. kubefwd enables developers to work locally while accessing services running in Kubernetes. Each service gets its own unique loopback IP (127.x.x.x), eliminating port conflicts. Access cluster services by name—exactly as in-cluster.
melgarafaelllms.txtDeskcommCRM
melgarafael/DeskcommCRM/public/llms.txt
Open-source AI sales OS: a self-hosted, multi-tenant CRM where AI agents natively operate sales and support over WhatsApp. An open alternative to Kommo, Octadesk and Intercom — for any business that sells through conversations (e-commerce, clinics, real estate, info-products, agencies, services). MIT-licensed, no paid tier; the recommended production path is a one-command install on a HostGator VPS. Key facts: - Category: open-source CRM / AI sales operating system / WhatsApp customer platform - AI: per-tenant RAG agents (pgvector) that answer,…
flypythoncomllms.txtpython
flypythoncom/python/llms.txt
FlyPython provides production-tested Python engineering playbooks, challenge courses with objective verification and per-checkpoint claim codes, verifiable test-driven starter examples, task contracts, and agent IDE rules.
lemony-aillms.txtcascadeflow
lemony-ai/cascadeflow/llms.txt
Agent runtime intelligence layer for AI agent workflows. In-process harness (not a proxy). Works inside agent loops with full state awareness. pip install cascadeflow import cascadeflow cascadeflow.init(mode="observe") # All openai/anthropic SDK calls are now tracked. Switch to "enforce" for budget gating. cascadeflow is an in-process intelligence layer that sits inside AI agent execution loops. Unlike external proxies that only see HTTP request boundaries, cascadeflow operates with full agent state awareness: step count, budget consumed, tool call history, error context, quality…
yvgudellms.txtlean-ctx
yvgude/lean-ctx/llms.txt
The local context-engineering layer for AI agents — select, shape, reuse, recover, and inspect context before inference. Deterministic, prompt-cache-aware representations with local, auditable measurements. lean-ctx (LeanCTX, "Lean Context") exposes a broad MCP tool set plus a shell hook for local context operations. An optional local request proxy can shape supported request fields while preserving stable prefixes for provider prompt caches. Pruned or truncated content can be recovered from content-addressed references. Processing is local by default; explicitly submitted feedback and enabled…
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.