Ai Readiness

Generates a complete set of AI readiness files for any website from a single command. Invoke this skill whenever the user types `/ai-readiness [url]`, asks to "create AI readiness files for [site]", "make my site AI-ready", "generate llms.txt for [url]", "build AI files for [domain]", or any variation of wanting to optimize a website for AI systems (ChatGPT, Claude, Gemini, Perplexity, etc.). Also trigger when the user asks about AI crawlers, llmstxt.org, ai.txt, RAG indexing of a website, or improving a site's visibility in AI search. The output is a complete folder of 18 production-ready files — dropped into the workspace, ready to deploy to the web root.

AI Readiness File Generator

What This Produces

A complete AI Readiness File Set for any website: 18 files that tell AI systems (ChatGPT, Claude, Gemini, Perplexity, and others) exactly what the site is, what it offers, and where to send users. When deployed to a web root, these files improve how the site appears in AI-generated answers, summaries, and recommendations.

Invocation

text

/ai-readiness [url]

Examples:

• /ai-readiness shopify.com
• /ai-readiness https://www.acmecorp.com
• create AI readiness files for hubspot.com

Normalize the input to a bare domain (e.g., shopify.com) and a canonical base URL (e.g., https://www.shopify.com). If the user provides a path, strip it to the root domain.

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Phase 1 — Research (do this before writing a single file)

The quality of every file depends on the research. Do not skip or shortcut this phase. Run at minimum 10 targeted searches using WebSearch and/or WebFetch. Save what you find — you will use it throughout Phase 3.

Required searches

Run all of these:

1. site:[domain] — discover indexed pages and site structure
2. site:[domain] about — company overview, founding, mission
3. site:[domain] products OR services OR solutions — what the site sells/offers
4. site:[domain] pricing OR plans — pricing pages
5. site:[domain] blog OR resources OR insights — content sections
6. site:[domain] contact OR team OR careers — company/org pages
7. "[Company Name]" description OR overview — how others describe this company
8. [domain] site:linkedin.com — often reveals employee count, HQ, founding year
9. (Industry-specific) For education: site:[domain] programs OR courses OR destinations For software: site:[domain] integrations OR API OR documentation For e-commerce: site:[domain] categories OR collections
10. site:[domain] sitemap or attempt WebFetch on [baseURL]/sitemap.xml

Also attempt WebFetch on:

• The homepage ([baseURL]/)
• 2-3 key product/service/program pages discovered in searches
• [baseURL]/about or [baseURL]/about-us

What to extract and record

As you research, build up a working picture of the site. You need:

• Company/org name (exact legal/brand name)
• Industry (InsurTech, Education, E-commerce, SaaS, Healthcare, etc.)
• What they sell/offer — products, services, programs, courses, destinations
• Key entity taxonomy — categories, types, sub-types (e.g., for study abroad: program types, destinations, disciplines)
• Confirmed URLs — pages you've seen in search results or fetched that almost certainly return 200. Note: only include URLs you have reasonable confidence exist. Do NOT fabricate paths.
• Brand differentiators — what makes this org stand out vs competitors
• Contact info — email, phone, address if findable
• Social links — LinkedIn, Twitter/X, Facebook, YouTube, Instagram
• Schema type — what Schema.org type fits best: Organization, EducationalOrganization, SoftwareApplication, Store, etc.

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Phase 2 — Site Classification

Before writing files, classify the site. This determines defaults for schema types, program taxonomies, and file emphasis.

Site Type	Schema Type	Emphasis
B2B Software / SaaS	SoftwareApplication + Organization	Product categories, integrations, use cases
Education / Study Abroad	EducationalOrganization + EducationalOccupationalProgram	Programs, locations, disciplines, financial aid
E-commerce / Retail	Store + Organization	Product categories, collections, shipping
Healthcare	MedicalOrganization + Organization	Services, specialties, locations
Professional Services	Organization	Service lines, industries served, team
Nonprofit	NGO + Organization	Mission, programs, impact, donate
Media / Publishing	Organization + WebSite	Topics, authors, sections

Adapt all file content to the site type. An e-commerce site needs product taxonomies; an education site needs program types and locations; a SaaS site needs product categories and use cases.

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Phase 3 — File Generation

Generate all 18 files. Save to a folder named after the domain (e.g., shopify/ for shopify.com) in the workspace outputs directory.

Order matters — generate in this sequence, since later files reference earlier ones:

1. ai.txt
2. llms.txt
3. llms-full.txt
4. ai-sitemap.xml
5. sitemap.md
6. ai-entities.json
7. ai-intent.json
8. ai-schema.json
9. rag-index.json
10. rag-index.jsonl ← auto-generate via script (see below)
11. ai-disclosure.txt
12. training-data-policy.txt
13. .well-known/ai-plugin.json
14. structured-data-guide.md
15. manifest.json
16. deployment-checklist.md
17. README.md
18. robots.txt

See references/file-specs.md for the detailed specification of each file. That file contains the required structure, key fields, and a condensed example for each.

Generating rag-index.jsonl

After writing rag-index.json, auto-generate the JSONL version with this inline command (substitute the actual output path for [output_dir]):

bash

python3 -c "
import json
with open('[output_dir]/rag-index.json') as f:
    records = json.load(f)
with open('[output_dir]/rag-index.jsonl', 'w') as out:
    for r in records:
        out.write(json.dumps(r) + '\n')
print(f'Generated {len(records)} lines')
"

The scripts/gen_jsonl.py bundled with this skill does the same thing and accepts two arguments: python3 gen_jsonl.py <input.json> <output.jsonl>.

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Quality Principles

Only use real URLs. Every URL in every file should be one you have strong reason to believe returns a 200 response. If you're not sure a page exists, use a higher-level URL that definitely does (e.g., use /blog/ rather than /blog/post-that-might-not-exist).

Be specific to this site. Generic filler text defeats the purpose. Every file should read as authoritative, first-person content about this specific company — not a template with the name swapped in. Use the exact brand name, real product names, confirmed URLs.

Match the industry. Study abroad sites need program types and destinations. SaaS sites need product categories and use cases. Don't apply a one-size-fits-all structure.

Prioritize the llms files. ai.txt, llms.txt, and llms-full.txt are the most important — they're what AI systems actually read. Spend the most time making these rich, accurate, and comprehensive.

Keep robots.txt CMS-agnostic. Do not include WordPress paths (/wp-admin/, /wp-login.php) unless you have confirmed the site uses WordPress. Only add CMS-specific disallow rules you can verify.

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Output

When all 18 files are written, tell the user:

1. How many files were created and where they are
2. A brief summary of what was found/generated (company, industry, key entities count)
3. Link to the folder so they can open it
4. Note any URLs or details you weren't able to confirm and how they handled it

Do NOT present a long recap of every file. One short paragraph + the file link is enough. The user can read the README.md for the full picture.