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NYTM Tools

NYTM started as a few small utilities I kept needing while working. It has now grown into 202 free tools for PDFs, images, text, code, converters, security, business tasks and everyday work. Most tools run directly in the browser. No signup or installation needed. You can search by task, browse categories, pin useful tools, and use it on desktop or mobile. The project is also open source.

llms txt Validator

How this actually works. An llms.txt file is the difference between a site AI assistants can navigate and one they have to guess about. It’s a plain Markdown file at your site root — a curated index of your most important pages, written for machines that need to find the right content without wading through HTML, ads, and JavaScript. The ToolsVale llms.txt Validator is the free llms.txt validator built for how the file actually gets consumed in 2026: it checks your file against the llmstxt.org v2 specification (August 2026), HEAD-checks every link, validates your Content-Type headers, cross-references your robots.txt for AI crawler conflicts, and simulates access from five different crawlers — Chrome, Googlebot, GPTBot, ClaudeBot, and PerplexityBot — to catch the WAF blocks and cloaking that browser testing misses entirely. This page explains what an llms.txt validator does, why most existing validators catch less than half of the real problems, and how this tool closes the gaps that actually matter for AI agent discoverability. If you’re in a hurry, scroll up and paste your URL — the audit runs in under 15 seconds and streams results as they arrive. What is llms.txt and why does it need validating? llms.txt is a proposed standard created by Jeremy Howard (co-founder of Answer.AI and fast.ai) in September 2024. The idea is simple: web pages are built for people, with navigation menus, ads, cookie banners, and JavaScript that make it hard for AI agents to find the content that matters. An llms.txt file gives AI assistants a clean, curated map — a Markdown document listing your most important pages with one-line descriptions, organized into sections. The specification defines a strict format: exactly one H1 with your site name (the only required element), an optional blockquote summary that AI models often quote verbatim, optional body text, and H2 sections containing curated link lists. The file must be served as text/plain or text/markdown at your site root (/llms.txt) or any subpath. The spec sounds simple, but the real world is messy. Webflow, Framer, and Shopify serve the file as text/html by default. WAFs silently block AI crawlers. robots.txt rules ban GPTBot from the very file it’s supposed to read. Links go stale, descriptions get skipped, and the file drifts out of sync with the site. That’s why validation matters — and why a validator needs to check far more than just Markdown syntax. Why most llms.txt validators fall short in 2026 Three things changed between 2024 and 2026 that made shallow validation useless: Chrome Lighthouse 13.3 added llms.txt to Agentic Browsing audits. Since May 7, 2026, every PageSpeed Insights run and Chrome DevTools audit checks whether your llms.txt exists and follows the spec. The audit sits in the new “Agentic Browsing” category alongside WebMCP, accessibility tree, and CLS checks. A broken llms.txt now shows up as a visible failure in every developer’s workflow — and the search queries for “llms.txt validator” have surged accordingly. But Lighthouse’s check is deliberately limited: it flags server errors (5xx = FAIL) and marks a missing file as N/A (not a failure). It doesn’t check your links, your robots.txt, your Content-Type, or whether AI crawlers can actually reach the file. That’s the gap a serious validator fills. Adoption accelerated, but most implementations are broken. An Ahrefs study of 137,000 sites in June 2026 found roughly 38,000 with a valid llms.txt — but 97% of those files received zero requests. Among the top 1,000 Tranco sites, 186 served a file and only 64 were valid. The other 122 had problems. Most sites that try still get it wrong. The issues aren’t exotic edge cases — they’re wrong Content-Type headers, dead links, robots.txt contradictions, and missing blockquotes. The spec evolved to v2. The August 2026 revision added subpath support (/docs/llms.txt covering everything under /docs/), BOM tolerance, and link relation headers (rel="alternate" for Markdown versions, rel="describedby" pointing to the covering llms.txt). Validators built against v1 miss subpath scoping violations and don’t know about the new conventions. Every existing free validator we tested — llms-txt.io, llmstxtvalidator.dev, Rankability, MRS Digital, llmstxtchecker.net, and others — focuses on Markdown structure: does your H1 exist, is there a blockquote, are links in the right format. That’s the floor. None of them HEAD-check every link. None validate Content-Type headers. None cross-reference robots.txt. None simulate access from AI crawlers. The ToolsVale llms.txt Validator was built specifically to cover everything above the floor. What this llms.txt validator checks that others don’t 1. Full link health audit Every link in your file gets a HEAD request. If the server returns 405 or 403 to HEAD (common on Cloudflare and Fastly CDNs), we automatically retry with a controlled GET request — fetching headers only, closing the body stream immediately. For each link we report the HTTP status code, any redirect chain, response time, and the exact line number in your file. Dead links get flagged as LLMS-013, redirects as LLMS-014 with the final URL offered as a copy-paste deterministic fix, and DNS failures as LLMS-015. We check up to 100 links per audit with concurrent requests and per-host throttling to avoid rate-limiting the target site. Across 523+ real-world llms.txt files analyzed by Mdream, the median file contains 83 links. A single dead link breaks every agent that follows it — and in a file that’s designed to be a curated index, a broken link is worse than no link at all. 2. Five-crawler access simulation We fetch your llms.txt as five different user agents: Chrome Desktop (baseline), Googlebot (search), GPTBot (ChatGPT/OpenAI), ClaudeBot (Anthropic), and PerplexityBot (Perplexity). The responses are normalized — whitespace-insensitive content hashes, not raw byte counts — and compared. If any crawler gets a different HTTP status, different content, or is blocked entirely, the crawler access matrix shows exactly which one and what happened. This catches the silent failures that browser testing can never reveal. Cloudflare Bot Management and AWS WAF routinely serve 403 or challenge pages to AI crawlers while returning a clean 200 to Chrome. Your llms.txt could be perfectly formatted and completely invisible to GPTBot. We label this capability “user-agent access simulation” because we’re testing server behavior from our infrastructure, not claiming to be the actual crawlers fetching from their IP ranges. 3. robots.txt cross-reference Your robots.txt and your llms.txt live on the same domain but serve different purposes. robots.txt tells crawlers what they may access; llms.txt tells them what’s important. If your robots.txt contains User-agent: GPTBot / Disallow: /, GPTBot is banned from your entire site — including /llms.txt. We parse your robots.txt using Python’s standard RobotFileParser (correct Allow/Disallow semantics, not naive string matching), test each AI crawler’s access rules against your llms.txt path, and flag contradictions with the specific robots.txt line and a copy-paste Allow: /llms.txt fix for each affected crawler. 4. Content-Type and delivery validation The spec requires text/plain or text/markdown. Webflow, Framer, and Shopify serve llms.txt as text/html by default — the single most common deployment mistake. We check the Content-Type header, charset, HTTP status code, redirect chains, and scan the response body for WAF challenge signatures from Cloudflare, Akamai, and Sucuri. Every Content-Type fix comes with platform-specific configuration tabs: Nginx, Apache, Cloudflare Rules, Vercel vercel.json, and Netlify _headers. 5. Lighthouse Agentic Browsing prediction Our validator maps to Lighthouse 13.3’s exact llms.txt audit behavior. Lighthouse marks a missing file (404) as “Not Applicable” — not a failure. It marks server errors (5xx) and spec violations as “FAIL”. A valid, accessible file is “PASS”. We predict your result so you can fix issues before running the real Lighthouse audit in Chrome DevTools or PageSpeed Insights. We’re explicit about scope: this prediction covers only the llms.txt portion of Lighthouse’s Agentic Browsing category — it doesn’t predict the WebMCP, accessibility tree, or CLS audits.

CSV Viewer

How to Use the CSV Viewer Drop or paste. Drag a .csv, .tsv, or .txt file onto the drop zone, or switch to the Paste tab and paste raw CSV data from any source — a database export, API response, spreadsheet copy, or log file. Review the detection. The tool auto-detects your delimiter (comma, tab, semicolon, or pipe) and whether the first row is a header. Both decisions are shown in the detection banner with a confidence level. If the header detection is wrong, toggle the “First row is header” checkbox to override it instantly. Explore your data. The Data Insights panel shows row count, column count, total cells, fill rate, and file size. The Column Analysis panel breaks down each column by type, unique values, and statistics. Click any column header to sort. Use the search box to filter rows across all columns. Download. Use “Download” for the original raw file, or “Download View” for the current filtered and sorted view — with formula-injection characters safely prefixed for Excel. What This Tool Detects That Others Don’t Most CSV viewers silently assume comma separation and a header row. This one states every assumption it makes: which delimiter it detected, how confident it is, and why it thinks the first row is or isn’t a header. When it’s wrong, you override it with a checkbox instead of re-formatting your file. It also catches problems other viewers ignore. Inconsistent column counts across rows are flagged with the exact row numbers. Cells starting with =, +, -, or @ are identified as potential formula injection risks — a security issue when CSV files are opened in spreadsheet applications. The sanitized export neutralises these before download.