tweet.md

Product Introduction

1. Definition: tweet.md is a specialized API and web service that converts X (formerly Twitter) posts and threads into clean, structured Markdown text. It is a technical tool in the data extraction, content processing, and AI/LLM (Large Language Model) pipeline categories.
2. Core Value Proposition: It exists to provide developers, researchers, and AI agents with reliable, token-efficient, and LLM-ready text from X, bypassing the unreliability of direct links and the complexity of raw API scraping. Its primary keywords are X to Markdown, thread to Markdown, LLM-ready text, and AI agent context.

Main Features

1. URL-Based Conversion: The core functionality works by a simple URL swap. Users replace the x.com domain in any X post URL with tweet.md. The service then fetches the post via the official X API and returns a normalized Markdown document. This eliminates the need for user authentication for basic access.
2. Thread Unrolling: By default or with the thread=full parameter, the service can walk the reply chain of a post, assembling an entire conversation into a single, ordered Markdown document (up to 100 posts by default). Each post is clearly numbered, includes metadata (author, stats, source URL), and is formatted with blockquote syntax (>).
3. API & Integration for AI Agents: Beyond the web interface, tweet.md offers a developer API. A unique feature is the provision of a ready-made "SKILL.md" instruction set that can be given to an AI agent, enabling the agent to autonomously convert any X URL into clean Markdown using the user's API key, making it a native tool for AI workflows.

Problems Solved

1. Pain Point: X posts and threads are poor, brittle context for LLMs and AI agents. Pasting a raw X URL into a chatbot often results in the LLM being unable to access the content (due to lack of live browsing capability), leading to hallucinations or incomplete information.
2. Target Audience: The primary user personas are AI/ML Engineers building agentic systems, Prompt Engineers and Researchers who need accurate source text for analysis, and Developers building applications that require processed social media content without handling X API complexity directly.
3. Use Cases: Essential scenarios include: feeding accurate thread context into AI chatbots (ChatGPT, Claude, Gemini) for summarization or Q&A; providing AI agents with a reliable skill to read social media posts; archiving X conversations in note-taking apps like Obsidian with clean formatting; and academic research requiring verifiable, text-based records of social media discourse.

Unique Advantages

1. Differentiation: Unlike simple web scrapers or embed generators, tweet.md uses the official X API, ensuring reliability and data completeness. Its output is not HTML, JSON, or an embed code, but purpose-built, minimal Markdown optimized to reduce token count for LLMs, unlike copying and pasting from the X website which includes extraneous UI text.
2. Key Innovation: The service's core innovation is its output format optimization specifically for the AI/LLM ecosystem. It strips away all visual clutter, structures metadata consistently, and uses Markdown—a format natively well-understood by LLMs—to deliver the pure semantic content. The "browser session cookie" key delivery after purchase also simplifies user access for non-developers.

Frequently Asked Questions (FAQ)

1. How does tweet.md convert X threads to Markdown? tweet.md works by programmatically calling the official X API when you swap the x.com domain for tweet.md in a URL. It then processes the JSON response, normalizes the text and metadata, and formats it into a clean, sequential Markdown document suitable for large language models and research.
2. Is there a free tier for the tweet.md X to Markdown converter? Yes, tweet.md offers a limited free tier allowing 5 single-post conversions per IP address per calendar month, but it restricts the use of thread unrolling and author metadata features, which require the purchase of credits.
3. What are credits and how do they work in tweet.md? Credits are tweet.md's consumption unit. Converting a single post costs 1 credit. Enabling author metadata (userinfo=author) adds a cost of 2 credits per unique author in the response. Credit packs are purchased upfront and do not expire, with volume discounts available.
4. Can I use tweet.md with AI agents like ChatGPT or Claude? Absolutely, tweet.md is specifically designed for AI workflows. You can manually convert a thread to Markdown and paste the text, or you can provide your AI agent with the official SKILL.md instructions and your API key, enabling the agent to autonomously fetch and process X content.
5. What output formats does tweet.md support besides Markdown? The primary and default format is markdown. tweet.md also supports an obsidian format, which adds YAML frontmatter and adjusts heading structures for seamless import into Obsidian note-taking vaults. The format is controlled by appending ?format= to the request URL.