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.com.

Step 2: Fetch all three files via curl

Run the following curl commands using bash_tool. Use -L to follow redirects, -s for silent mode, -o to save content, -w to capture HTTP status codes, and a reasonable timeout (--max-time 10).

DOMAIN="<normalised-domain>"

# 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"
echo "llms.txt:   $(cat /tmp/llms_status.txt) | $(wc -c < /tmp/llms.txt) bytes"
echo "llms-full.txt: $(cat /tmp/llms_full_status.txt) | $(wc -c < /tmp/llms-full.txt) bytes"

Interpret the HTTP status codes:

• 200 → file exists, read and audit the content
• 301/302 → followed automatically by -L; final destination counts
• 404 → file does not exist at this path
• 403/429/5xx → server-side block or error; note it explicitly
• 000 → connection failed (domain unreachable or timeout)

Step 3: Read and classify results

After the curl commands complete, read the saved files:

cat /tmp/robots.txt
cat /tmp/llms.txt
# For llms-full.txt, show first 200 lines only to avoid flooding context:
head -200 /tmp/llms-full.txt
wc -l /tmp/llms-full.txt   # get total line count
wc -c /tmp/llms-full.txt   # get total byte size

Case A — Both llms.txt (200) AND llms-full.txt (200)

• Both files fetched successfully; proceed to the Audit Checklist (Step 4)

Case B — Only llms.txt (200), llms-full.txt returned 404

• Audit llms.txt
• Scan its content for any internal reference to llms-full.txt (it may be hosted at a non-standard path)
• If a custom path is found → curl that path and audit it
• If not found → report llms-full.txt as absent and not referenced

Case C — llms.txt returned 404

• Report that neither file is present at the standard paths
• Note whether robots.txt gave any hints (some sites reference llms.txt inside robots.txt)
• Report clearly to the user (see Response Templates section below)

robots.txt (always check regardless of Case)

• Even if llms.txt is missing, always read and audit robots.txt for AI-access signals

Step 4: Audit the files

llms.txt Audit

Check for the following. Mark each ✅ or ❌:

Structure

• Starts with a single # H1 title (site/product name)
• Has a > blockquote summary immediately below H1 (1–2 sentence description)
• Uses ## H2 sections to group links (e.g. Docs, API Reference, Guides, OpenAPI Specs)
• Each link follows format: - [Page Title](https://absolute-url): brief description
• Has an ## Optional section for secondary/non-essential content (not required but best practice)
• No nested headings inside H2 link sections
• No images, HTML, or tables (plain markdown only)

Content completeness

• Core product/feature pages are listed
• API reference pages are included (if applicable)
• Getting started / quickstart pages included
• SDK/integration guides included (if applicable)
• Link descriptions are meaningful (not just page titles repeated)
• All links use absolute URLs (not relative paths)
• No broken or 404 links visible

AI-readiness signals

• References llms-full.txt (either directly or in a Documentation Sets section)
• Segmented sets for different use cases (advanced but excellent — e.g. Scalekit's topic-specific .txt files)

llms-full.txt Audit (if available)

• File exists and is non-empty
• Contains full page content (not just links)
• Has clear document boundary markers between pages (e.g. --- or # DOCUMENT BOUNDARY)
• Each section has a Source: URL reference
• Content is clean markdown (no raw HTML, no JS artifacts)
• Reasonably sized (warn if extremely large — may exceed LLM context windows)

robots.txt Signal (check opportunistically)

If robots.txt was surfaced during the process:

• User-agent: * with Allow: / — all bots permitted
• ai-input=yes — explicitly permits AI agents to use content
• ai-train=no — training blocked (common and acceptable)
• Any Disallow rules that would block AI crawlers

Step 5: Deliver the report

Structure the output as:

## LLMs.txt Audit: [domain]

### Discovery
[What was found and how it was surfaced]

### llms.txt — ✅ Found / ❌ Not Found
[Audit results with ✅/❌ per checklist item]
[Notable strengths]
[Issues found]

### llms-full.txt — ✅ Found / ❌ Not Found / ⚠️ Not Referenced
[Audit results or explanation]

### robots.txt Signal
[If available — what it says about AI access]

### Summary & Recommendations
[3–5 actionable bullets]

Response Templates

Neither llms.txt nor llms-full.txt surfaced

Neither llms.txt nor llms-full.txt was discoverable from the provided URL.

This means AI agents and LLMs browsing your docs will have no structured index to work from — they'll need to crawl individual pages or guess at your content structure.

To fix this, surface the llms.txt URL somewhere Claude (and other AI tools) can see it when fetching your page. Good options:

• Add it to your page footer (e.g. LLM usage: /llms.txt)
• Include it in a blockquote at the top of your docs homepage or .md page version (e.g. > Documentation index available at: https://yourdomain.com/llms.txt)
• Reference it in your robots.txt or a <meta> tag

Once it's linked from a page that AI agents naturally land on, it becomes discoverable automatically.

llms.txt found but llms-full.txt not referenced

llms.txt was found and audited. However, llms-full.txt was not referenced anywhere in the file.

llms-full.txt is the companion file containing the full content of all documentation pages in a single file — useful for AI coding assistants (Cursor, Claude Code, Copilot) that need deep context without fetching dozens of individual pages.

To add it: Reference it in your llms.txt under a ## Documentation Sets section or similar, like:

- [Complete documentation](https://yourdomain.com/llms-full.txt): full content of all pages

If you're on Mintlify, it's auto-generated — just make sure it's linked.

Key facts to keep in mind

• Mintlify auto-generates both llms.txt and llms-full.txt for all projects, and adds HTTP headers (`Link: </ll