llms-txt-checker
Audits any domain's AI-readiness by using curl to directly probe robots.txt, llms.txt, and llms-full.txt, then scores each file against a structured checklist and delivers a formatted report with pass/warn/fail findings and actionable fixes. Use this skill whenever a user provides a domain or URL and wants to know if llms.txt or llms-full.txt is available, discoverable, or properly structured. Trigger on phrases like "check llms.txt for", "does this site have llms.txt", "find llms.txt", "check llms for this url", "audit llms.txt", "is llms-full.txt available", or any time a user shares a domai
npx skills add Infrasity-Labs/dev-gtm-claude-skills --skill llms-txt-checker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLMs.txt Checker Skill Audits any domain's AI-readiness by using `curl` to directly probe `robots.txt`, `llms.txt`, and `llms-full.txt`, then scores each file against a structured checklist and delivers a formatted report with pass/warn/fail findings and actionable fixes. The user provides **only a domain** (e.g. `anthropic.com` or `docs.example.com`). Claude uses `bash_tool` with `curl` commands to directly probe the domain — no guessing, no page-scraping required. --- ## How it works Instead of relying on web_fetch and hoping links surface organically, this skill uses **curl via bash_tool** to directly request the well-known paths for `robots.txt`, `llms.txt`, and `llms-full.txt`. This is reliable, fast, and works regardless of how the site is built. The curl commands follow HTTP redirects, capture response codes, and save content to temp files for auditing. --- ## Step-by-Step Workflow ### Step 1: Normalise the domain Take the user-provided input and strip any trailing slashes, `http://`, `https://`, or path segments to get a clean base domain (e.g. `docs.anthropic.com`). If the user provides a full URL like `https://docs.anthropic.com/en/home`, extract just `docs.anthropic.co
- How it works
- Step-by-Step Workflow
- Step 1: Normalise the domain
- Step 2: Fetch all three files via curl
- Step 3: Read and classify results
- Step 4: Audit the files
- Step 5: Deliver the report
- Response Templates
- Neither llms.txt nor llms-full.txt surfaced
- llms.txt found but llms-full.txt not referenced
- Key facts to keep in mind
Ensure files exist to prevent "No such file or directory" errors if curl fails
touch /tmp/robots.txt /tmp/llms.txt /tmp/llms-full.txt
Fetch robots.txt
curl -L -s -o /tmp/robots.txt -w "%{http_code}" --max-time 10 "https://$DOMAIN/robots.txt" > /tmp/robots_status.txt
Fetch llms.txt
curl -L -s -o /tmp/llms.txt -w "%{http_code}" --max-time 10 "https://$DOMAIN/llms.txt" > /tmp/llms_status.txt
Fetch llms-full.txt
curl -L -s -o /tmp/llms-full.txt -w "%{http_code}" --max-time 10 "https://$DOMAIN/llms-full.txt" > /tmp/llms_full_status.txt
Print status codes and file sizes for inspection
echo "robots.txt: $(cat /tmp/robots_status.txt) | $(wc -c < /tmp/robots.txt) bytes"What does the llms-txt-checker skill do?
Audits any domain's AI-readiness by using curl to directly probe robots.txt, llms.txt, and llms-full.txt, then scores each file against a structured checklist and delivers a formatted report with pass/warn/fail findings and actionable fixes. Use this skill whenever a user provides a domain or URL and wants to know if llms.txt or llms-full.txt is available, discoverable, or properly structured. Trigger on phrases like "check llms.txt for", "does this site have llms.txt", "find llms.txt", "check llms for this url", "audit llms.txt", "is llms-full.txt available", or any time a user shares a domai
How do I install it?
Run `npx skills add Infrasity-Labs/dev-gtm-claude-skills --skill llms-txt-checker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Infrasity-Labs/dev-gtm-claude-skills, a repository with 97 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.