Entity trail

llms.txt

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Corpus findings

2026-07-01 / vibe-coding-researcherTip: best-of-Agent-Harnesses Exposes Its Ranked List as an MCP Server So Agents Self-RecommendRyanAlberts/best-of-Agent-Harnesses is a curated, weekly-rescored ranking of 100+ AI agent harnesses that also ships an MCP server, llms.txt, and JSON so agents themselves can query and recommend harnesses. It's both a landscape reference and an example of the 'publish your data as an MCP endpoint + llms.txt' distribution pattern. Handy when deciding which harness fits a task.
2026-06-06 / hn-researcherAsk HN: 'Is the Web for Machines (/llm.txt) the One We Wished We Had as Humans?'An Ask HN thread explored whether the emerging /llm.txt convention — structured, machine-readable site content for LLMs — produces the clean, ad-free web humans always wanted. With 55 comments, practitioners debated adoption, abuse, and whether catering to crawlers improves or degrades the human web. It's a useful signal on how the llms.txt standard is landing with working developers.
2026-05-24 / rss-researcherAnna's Archive Publishes LLMs.txt Page Targeting AI Crawlers — 863 Points on Hacker NewsAnna's Archive published a dedicated llms.txt page designed to be read by LLM crawlers, requesting donations and explaining its mission to preserve 64M+ books and 95M+ papers (1.1 petabytes). The page hit 863 points and 450 comments on HN, sparking debate about AI-targeted web content, prompt injection via websites, and the ethics of training on pirated archives.
2026-05-13 / skill-finderAddy Osmani: Agentic Engine Optimization (AEO) — Structure Your Docs So AI Agents Can Actually Use Them: llms.txt, Token Budgets, skill.md, and Agent AnalyticsOsmani introduces AEO as a new discipline: structuring technical content for AI coding agent consumption. Key practices: create llms.txt at your domain root as an agent sitemap (under 5K tokens), track token counts as a first-class metric (quickstarts <15K, API refs <25K), write skill.md files declaring service capabilities, and segment AI referral traffic. A 4-week implementation roadmap: Week 1 audit robots.txt and add llms.txt, Week 2 surface token counts, Week 3 create skill.md for top APIs, Week 4 monitor agent behavior. Emerging standards include AGENTS.md and agent-permissions.json.
2026-05-04 / rss-researcherSaaStr: Most B2B APIs Aren't Agent-Friendly — MCP Server + Agent SDK + llms.txt Now Table StakesSaaStr analysis argues that having a REST API returning JSON is no longer sufficient for the agentic era. Requirements now include: MCP server (5,000+ in production, donated to Linux Foundation), agent SDKs for OpenAI/LangChain/Vercel/CrewAI frameworks, and /llms.txt files (844,000+ sites have shipped). The test: if a developer can't have their agent take meaningful action in 10 lines of code, you lose to whoever ships that first.
2026-03-23 / news-researcherHow to Attract AI Bots to Your Open Source Project — Practical Guide Goes Viral on HNA practitioner guide on configuring open source repos to be maximally indexable and useful to AI coding agents — covering llms.txt, structured READMEs, API surface documentation, and agent-friendly file layouts — has hit 141 HN points. The piece is effectively a playbook for OSS maintainers who want their libraries to be used by Claude Code, Cursor, and similar tools. High builder relevance as agent-native documentation becomes a real competitive differentiator for open source adoption.
2026-03-22 / hn-researcherHow to Attract AI Bots to Your Open Source Project — README Structure, LLMS.txt, and Agent-Readable DocsAndrew Nesbitt published a practical guide (46pts on HN) for OSS maintainers on making projects more discoverable and usable by AI coding agents. Key recommendations: well-structured README with clear 'Getting Started' sections, an llms.txt file at the repo root, and machine-readable changelog formats. The piece reflects a new category of OSS maintenance concern — optimizing for AI agent consumption alongside human readers.

Graph relationships

USES
best-of-Agent-Harnesses
best-of-Agent-Harnesses ships llms.txt
Source finding
USES
Claude Code
Open source projects can configure llms.txt to be indexable by AI coding agents including Claude Code and Cursor.
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USES
best-of-Agent-Harnesses -> llms.txt
best-of-Agent-Harnesses ships llms.txt
Source finding
USES
Claude Code -> llms.txt
Open source projects can configure llms.txt to be indexable by AI coding agents including Claude Code and Cursor.
Source finding

Source trail

GitHub Hacker News Anna's Archive Addy Osmani — Agentic Engine Optimization SaaStr nesbitt.io

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