I built an open-source CLI that scores any site for AI-agent readiness (0-100)

TL;DR — agent-readiness-cli checks how well your site talks to ChatGPT, Claude, Perplexity and other AI agents. Single-file Python, standard library only, MIT. Repo: github.com/sspoisk/agent-readiness-cli Install:pip install agent-readiness-cli Every "AI SEO" article in the last six months tells you the same three things: - Add llms.txt - Add JSON-LD with the right @type - Decide what to do about GPTBot ,ClaudeBot ,PerplexityBot inrobots.txt What none of them give you is a tool that opens your site, looks at it the way a crawler would, and tells you what's actually missing right now. So I built one. $ agent-ready https://example.com ✓ llms.txt 10/15 present, 4.2 KB, 12 URLs ✓ json-ld 23/25 3 block(s), types: Article, Organization, BreadcrumbList ✗ ai-bots-robots.txt 0/20 ClaudeBot, GPTBot disallowed at root ✓ canonical+hreflang 12/15 canonical=set, hreflang langs=['en','ru'] ✗ mcp-card 0/10 no /.well-known/mcp.json (optional) ✓ meta 10/10 10/10 of common signals ✓ sitemap 5/5 valid, 1250 URLs Score: 60 / 100 Tier: C (middling — focus on ai-bots-robots.txt, mcp-card) One number for the whole site, plus seven sub-scores so you know what to fix first. Bands: A ≥ 90 · B ≥ 75 · C ≥ 55 · D ≥ 35 · F < 35. I picked these weights based on what I see most often missing in the wild — JSON-LD is heaviest because it's the highest-leverage signal an LLM uses to understand your page kind. Disagree? All weights are in agent_ready/cli.py and contributors are welcome to challenge any of them. agent-ready https://example.com # human summary (default) agent-ready --full https://example.com # human summary + every finding agent-ready --json https://example.com # machine-readable agent-ready --csv https://example.com # one row, append to your monitoring agent-ready --quiet https://example.com # just the score; exit code = band If you want your build to fail when the site drifts below a threshold: - name: Audit AI-agent readiness run: | pip install agent-readiness-cli SCORE=$(agent-ready --quiet https://your-site.example) echo "AI-readiness score: $SCORE" [ "$SCORE" -ge 75 ] || { echo "below threshold"; exit 1; } That single integer-on-stdout is why --quiet exists. - It does not crawl your whole site — one URL at a time. If you want the whole site, drive it from a sitemap loop. - It does not fix anything. It tells you what to fix; the fix is on you. - It does not check for vulnerabilities — use OWASP ZAP for that. - It does not validate JSON-LD against the full Schema.org grammar. It checks that types are recognised; for Schema-strict validation, use Google's Rich Results Test. - It does not score Core Web Vitals or accessibility. If any of those is what you actually need, this is not the right tool. The whole tool is one Python file plus tests. No third-party dependencies. Standard library only. A few reasons this matters: - Auditable. You can read every check in one go. No layer of abstraction hides what gets weighted. - Portable. It runs on any box with Python 3.10+. No pip install -and-pray. - No telemetry. It hits your URL only. Nothing else leaves the machine. - Forkable. If you want to add a check or change a weight, fork it. The whole thing is shorter than most config files. I think the future of small dev tools is a return to this — one file, one job, no surprise dependencies. There are excellent generators (e.g. firecrawl/llmstxt-generator) that produce llms.txt files for you. There are validators for JSON-LD (Google's web tool, schema linters). There are MCP doc tools like langchain-ai/mcpdoc for exposing llms-txt to IDEs. What didn't exist was the audit slice — a single CLI that opens your URL, looks at the agent-readiness surface end-to-end, and says "score 62, weakest links are X and Y." So I wrote that. pip install agent-readiness-cli agent-ready https://your.site Available on PyPI and GitHub. If you want continuous monitoring instead of one-off audits, I built it on top of Web-Audit Guardian — the same logic running every 30 min for a domain. The CLI is the audit slice as OSS; the continuous variant is the paid tier. Either way, the methodology is now public. - A --all mode that follows your sitemap and rolls up to a site-wide score - Optional check for ai.txt (Spawning's draft) - More AI bots if reasonable consensus emerges Issues, PRs and disagreements with the weights all welcome. The repo is small enough that the bar to contribute is low. Repo: github.com/sspoisk/agent-readiness-cli License: MIT Author: maintained by GuardLabs. If you run it, let me know what your number is. Always interested to hear which check most surprised people.