tweet.md
URL-swap converter that turns X posts, threads, Articles, and profiles into LLM-ready Markdown by replacing x.com with tweet.md.
If X is a research surface for you, tweet.md removes a genuinely annoying step with official-API fidelity instead of scrape guesswork. The URL swap is the whole product, and that's a compliment: nothing to configure for a one-off post, and thread ordering, X Articles, and profile data come back reconstructed rather than half-scraped the way a general reader like Jina Reader tends to return them. Credits are the catch — each returned post is 1 credit and userinfo=author adds 2, so bulk thread and profile archiving scales linearly with no flat plan. Light use is cheap; heavy research gets expensive fast.
Verified 7d ago · liveness 78/100 · cite: rightaichoice.com/tools/tweet-md
- Prompt engineers pulling X threads into ChatGPT, Claude, or Gemini without copy-paste cleanup
- Developers building LLM agents that need to read tweets via the npx skill and API
- Researchers and analysts archiving entire X conversations as structured Markdown
- iOS and macOS users who want to save shared tweets to Obsidian via the Apple Shortcut
- Teams needing scheduling, analytics, or social media management — this only converts
- Bulk pipelines on a fixed budget — credits scale linearly with no flat plan
- Workflows outside X — there is no support for other social platforms
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip tweet.md if you need to convert anything other than X — it does not handle other social platforms — or if you need a flat-rate plan to cap a high-volume pipeline, since credits scale linearly with every post and author you pull.
Each returned post costs 1 credit and userinfo=author adds 2 credits for the root author — author metadata is the part that inflates a research bill.
Credit packs suit individuals and small teams doing occasional X-to-Markdown pulls: $5 gets 200 credits (~66 posts with metadata), $17 gets 1,000 (~333 posts), $42 gets 3,000 (~1,000 posts), all one-time with tax added at checkout. There is no subscription and no flat plan, so it undercuts generic page-to-text readers on fidelity for X specifically but loses to them on breadth — and heavy, continuous archiving scales linearly with credits in a way a flat-rate tool would not.
In short
tweet.md — URL-swap converter that turns X posts, threads, Articles, and profiles into LLM-ready Markdown by replacing x.com with tweet.md. Best for Prompt engineers pulling X threads into ChatGPT, Claude, or Gemini without copy-paste cleanup, Developers building LLM agents that need to read tweets via the npx skill and API, Researchers and analysts archiving entire X conversations as structured Markdown. Free to start; paid plans from $5.
What's new in tweet.md
Checked 7 days agoAcross the latest 5 updates: 3 feature updates, 1 launch and 1 changelog entry.
Publish and edit dates on every post
Every post in the Markdown output now carries a Posted date — on single posts, each thread post, and quoted or embedded posts — while edited posts add an Updated line with the latest edit time.
Profiles as one timeline
Profiles return as a single timeline with max (10–500), replies=on, and retweets=on, plus verification type, protected status, and affiliated organisation; HTML escapes in posts, names, and bios were fixed.
A leaner thread default
Leaving out thread now returns branch-5 instead of branch-15 — the opened post plus up to four more from its branch, ancestors first — which costs fewer credits by default, with caps up to 500.
A smoother start with tweet.md
Profile creation with Google, GitHub, or email and password plus a guided setup chat, multiple named API keys per account, optional API-key claiming into verified profiles, and free controlled demos on the synthetic tweet.md/*t-twmduser corpus.
Trusted IPs, simpler URLs, better articles
Trusted IP authentication lets an allowlisted IP call the API without sending a key, per-key default query parameters were added, and long-form X Articles now return as properly structured Markdown.
What people actually say about tweet.md — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
19 mentions across 2 sources (Hacker News, Bluesky) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +URL swap is dead simple: replace 'x.com' with 'tweet.md'.
- +Threads up to 100 posts are converted to structured Markdown.
- +Output is token-efficient, optimized for LLMs like ChatGPT and Gemini.
- +Works with 20+ AI tools including Cursor, Claude, and Obsidian.
- +Apple Shortcut integration enables share sheet, clipboard, and Siri.
- −Free tier caps at 5 single posts per month—too low for testing.
- −No support for private X accounts or protected posts.
- −No analytics or logging of previous conversions.
- −Only supports X/Twitter; no other social media platforms.
- −No batch conversion for multiple unrelated posts.
- • None mentioned in community data
Viability Score
How well maintained and how widely used is tweet.md? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Convert any X post to Markdown by replacing x.com with tweet.md
- Export complete threads in order with thread=branch-N, ancestors-N, or all-N (caps up to 500)
- Default thread is branch-5: opened post plus up to four from its branch, ancestors first
- Convert long-form X Articles with headings, links, and media intact
- Fetch a profile as one timeline: bio, stats, pinned post, recent posts (max 10–500, default 10)
- Fold profile replies or reposts in with replies=on or retweets=on; each post is labelled post, reply, or repost
- Profile Account block shows verification type (blue, government, business), protected status, affiliated org, location, join date
- Author metadata via userinfo=author or userinfo=all (name, handle, followers)
- Every post carries a Posted date; edited posts add an Updated line with the latest edit time
- Obsidian-ready output with YAML frontmatter and posted/updated fields via ?format=obsidian
- Token-efficient Markdown optimized for LLM context
- npx agent skill (npx skills add tweet-md/skill) for auto URL swapping in agents
- Apple Shortcut for iOS/macOS — Siri, share sheet, and clipboard input
- API authentication via session cookie, Authorization: Bearer, x-ios-apikey, or ?apikey=
- Trusted IP authentication — allowlist an IP in the dashboard and call without sending a key
About tweet.md
tweet.md turns X (Twitter) content into clean, token-efficient Markdown you paste straight into ChatGPT, Claude, Gemini, Cursor, or any LLM agent. The mechanic is a URL swap: take any post, thread, X Article, or profile link, replace x.com with tweet.md, and get structured Markdown back — no HTML scraping, no embeds, no raw JSON. It runs on the official X API, so complete threads and author stats survive the trip intact. Output covers single posts with stats and media, ordered threads (thread=ancestors, branch, or all, with caps to 500), long-form X Articles with headings and links preserved, and profile pages with bio, verification type, affiliated organisation, join date, pinned post, and one continuous timeline you filter with replies=on or retweets=on. You steer results with URL parameters: userinfo=author or all for author metadata, ?format=obsidian for YAML frontmatter. Every post now carries a Posted date, and edited posts add an Updated line. This is built for prompt engineers, agent developers, and researchers who move X material into LLMs constantly. There is an npx agent skill (npx skills add tweet-md/skill) that teaches an agent to auto-replace x.com URLs, plus an Apple Shortcut that fires from Siri, the share sheet, or your clipboard on iPhone, iPad, and Mac. A no-charge demo runs on a controlled corpus — the synthetic tweet.md/*t-twmduser profile — so you can see real output first.
Behind the Verdict
Strengths first: tweet.md does one job and does it with data a generic page-to-text tool cannot assemble. Because it sits on the official X API, a thread comes back in order rather than as a pile of partial replies, an X Article comes back with headings and links, and a profile comes back as a single timeline with bio, verification type (blue, government, business), protected status, affiliated organisation, join date, and pinned post. The July 31, 2026 update also fixed HTML escaping — a post written as a Markdown quote no longer arrives as > quoted line — and added publish and edit dates so an LLM can tell when each post was written. The agent story is real: an npx skill instructs your agent to swap x.com for tweet.md, and the Apple Shortcut covers iPhone, iPad, and Mac via Siri, share sheet, or clipboard. The Obsidian guide adds YAML frontmatter, so archive workflows land in a vault without manual cleanup. Weaknesses are structural. It only speaks X — nothing else — so it is a point tool, not a conversion layer. Pricing is credit packs ($5/200, $17/1,000, $42/3,000, one-time each, tax excluded and added at checkout), which means a 500-post research sweep costs real money and there is no flat plan to cap it. Each post costs 1 credit and author metadata costs 2 more per unique author, so the metadata you want for provenance is the part that inflates the bill. Output quality is only as good as the X API — a deleted or protected post is not something it can conjure. Where it fits: prompt engineers who pull threads into ChatGPT, Claude, or Gemini; agent developers wiring tweet reading into a pipeline via the skill or API; researchers archiving conversations as structured Markdown; iOS and macOS users who want shared tweets in Obsidian. Where it doesn't: teams wanting scheduling, analytics, or social management (this only converts), anyone needing LinkedIn, YouTube, or web pages (no non-X support), and collaborators wanting shared credit pools or seats.
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Real-world workflow fit
Concrete scenarios for the personas tweet.md actually fits — and what changes day-one when you adopt it.
You find a long X thread on an AI engineering topic and want it in ChatGPT without missing replies or broken links. You replace x.com with tweet.md, set thread=all-5 or branch-20, copy the Markdown, and paste it into ChatGPT.
Outcome: You get the thread in order with author metadata and source links, and the LLM reads the full text instead of a truncated page scrape.
You install the skill with npx skills add tweet-md/skill, give your agent an API key, and let it swap any x.com URL for tweet.md when it needs to read a tweet.
Outcome: The agent fetches LLM-optimized Markdown on its own, saving tokens versus pasting raw pages, and you can set per-key default query parameters so you never repeat options in the URL.
You convert a profile and its recent posts with ?format=obsidian, then import the Markdown into your vault.
Outcome: The notes land with YAML frontmatter, source metadata, posted dates, and an Account block showing verification type and join date — enough to tell an official account from a lookalike without a second lookup.
Use Cases
- Extract an X thread as ordered Markdown for analysis in an LLM.
- Save an X profile's bio, verification status, and recent posts as clean Markdown.
- Use the Apple Shortcut to convert tweets on the go via the share sheet or Siri.
- Feed Markdown into an AI agent with the npx skill for automated summarization or research.
- Archive X content to Obsidian with YAML frontmatter, posted dates, and source metadata.
- Replace manual copying from X with a quick x.com → tweet.md URL swap.
- Fetch profile and author data for lead generation or competitor research.
- Pull an X Article with headings and links preserved for reading or citation.
Limitations
- Conversion works by replacing x.com with tweet.md in an X/Twitter URL (post, thread, Article, or profile) — there is no other input path described.
- The free, no-charge demo runs only on a controlled demo corpus (the synthetic tweet.md/*t-twmduser profile), so converting your own content requires a profile and credits: packs are $5/200, $17/1,000, and $42/3,000 credits, one-time, with sales tax, VAT, or GST added at checkout.
- Scope is limited to X/Twitter content, and output is delivered as Markdown for pasting into chat assistants, agents, editors such as Obsidian/Notion, or similar tools.
as of 2026-10-01
Verification history
We have re-verified tweet.md 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published tweet.md tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Live demo
$0
Ideal for
Anyone who wants to see real Markdown output before spending anything — no account, no key, and never charged.
What this tier adds
Starting tier: runs on a controlled demo corpus (a post, a thread, an X Article, and a profile) and does not convert your own content.
200 credits
$5 one-time
Ideal for
Solo user making occasional one-off conversions who wants an API key without committing to volume.
What this tier adds
Entry paid tier at $0.025 per credit — 200 credits, roughly 66 posts with metadata, with API key plus browser session after checkout.
1,000 credits
$17 one-time
Ideal for
Regular prompt engineer or researcher converting threads and profiles weekly rather than daily.
What this tier adds
Lowers the rate to $0.017 per credit — 1,000 credits, roughly 333 posts with metadata, the lowest per-credit rate of the three packs.
3,000 credits
$42 one-time
Ideal for
Agent developer or researcher running sustained X-to-Markdown pipelines who wants the best per-credit rate.
What this tier adds
Best balance for agents at $0.014 per credit — 3,000 credits, roughly 1,000 posts with metadata, and the lowest effective cost per pull.
Where the pricing makes sense
The company stage and team size where tweet.md's pricing actually pencils out — and where peers do it cheaper.
Credit packs suit individuals and small teams doing occasional X-to-Markdown pulls: $5 gets 200 credits (~66 posts with metadata), $17 gets 1,000 (~333 posts), $42 gets 3,000 (~1,000 posts), all one-time with tax added at checkout. There is no subscription and no flat plan, so it undercuts generic page-to-text readers on fidelity for X specifically but loses to them on breadth — and heavy, continuous archiving scales linearly with credits in a way a flat-rate tool would not.
Setup time & first value
How long it actually takes to get something useful out of tweet.md — broken out by persona, not the marketing-page minute.
Prompt engineer: under a minute for a one-off post — swap x.com for tweet.md and copy the Markdown, no account needed for the demo corpus. Agent developer: about 10 minutes — create a profile with Google, GitHub, or email and password, let the guided chat run a real request, then install the skill with npx skills add tweet-md/skill and set an API key. Researcher or Obsidian user: roughly 15
Switching to or from tweet.md
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual copy-paste from X: swap x.com for tweet.md in the URL and copy the LLM-ready Markdown instead of cleaning up pasted text.
- →From generic page-to-text readers such as Jina Reader: point the same X URLs at tweet.md to get ordered threads, X Articles, and profile blocks the generic scrape does not reconstruct.
- →From screenshotting or bookmarking threads: convert to Markdown with ?format=obsidian so the content lives in your vault with YAML frontmatter and posted dates.
- ↗To a general-purpose reader: if you need web pages, LinkedIn, or YouTube alongside X, a broader page-to-text tool covers more surfaces, though it will not rebuild thread order or profile blocks.
- ↗To a social media management suite: if you need scheduling, analytics, or shared team seats, tweet.md only converts, so a management platform is the direction to move.
- ↗To a flat-rate archiving setup: if credit-per-post scaling becomes the constraint on a fixed budget, a subscription-based storage or sync tool caps the cost.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “tweet.md”, and we withheld 6: 6 could not be judged, because “tweet.md” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about tweet.md.
Official links
Tools that pair well with tweet.md
Common stack mates teams adopt alongside tweet.md, with the specific reason each pairing earns its keep.
AnyCrawler
Web scraping API that searches, fetches, renders, and returns AI-ready Markdown for agents.
Olostep
Developer-first web data API that searches, scrapes, crawls, maps, and monitors the web through one endpoint for AI agents and RAG pipelines.
Crawl4AI
Open-source, LLM-ready crawler that turns any URL into clean Markdown, typed JSON, or search results — self-hosted or via a paid cloud API.
Featured Head-to-Head Comparisons
Tweet Md vs Praktika
These tools are for completely different jobs. Choose tweet.md if you need to feed X posts/threads into LLMs or AI workflows — it's a specialized ingest tool. Choose Praktika if you want to practice speaking a language with AI tutors that correct you in real time. They don't compete; pick based on your task: data extraction vs. language learning.
Tweet Md vs Screenplayiq
tweet.md and ScreenplayIQ serve entirely different needs. Choose tweet.md if you're a developer or researcher needing clean Markdown from X for AI workflows—it's free for single posts and integrates with 20+ tools. Choose ScreenplayIQ if you're a screenwriter or producer needing AI-driven screenplay analysis with box office predictions—its free tier offers one analysis per month.
Turnitin vs Tweet Md
These tools serve entirely different needs. tweet.md is for AI developers and researchers who need to pull X content into LLMs as clean Markdown – think data prep for training or analysis. Turnitin is an institutional platform for plagiarism and AI writing detection in education. Choose tweet.md if you work with X data programmatically and want token-efficient output. Choose Turnitin if you're an educator or institution ensuring academic integrity.
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AnyCrawler
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Frequently Asked Questions
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