Samvida — Agentic Contracts for Your Business

Overview

This skill crawls a business website, extracts structured information, and generates a properly formatted llms.txt file — the standard that makes any business readable and transactable by AI agents.

It follows the llmstxt.org specification with business-specific extensions:

• - ## Team — builds agent trust in the people behind the business
• INLINECODE2 — social proof for agent decision-making
• INLINECODE3 — how agents can interact (or a clear "coming soon" notice)

Read references/llms_txt_spec.md before generating any output.

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Workflow

Step 1 — Get the URL

If the user didn't provide a URL, ask:

"What's the website URL?"

Normalize it (add https:// if missing).

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Step 2 — Crawl

Run the crawler:
CODEBLOCK0

Read /tmp/llms_business_info.json. Note:

• - What pages were crawled
• What was found vs missing (team, pricing, testimonials, API)
• Whether an existing llms.txt was found

Tell the user briefly:

"Crawled {domain} ({N} pages). Found: {what was found}. I'll ask about a few things I couldn't determine."

If the crawl found an existing llms.txt, note it:

"I noticed you already have a llms.txt at {domain}/llms.txt. I'll generate a fresh one — you can compare and decide which to keep."

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Step 3 — Ask for additional sources (always ask this first)

"Are there any other pages I should read? (docs, API reference, existing llms.txt, press page — anything useful)"

If they provide URLs, re-run the crawl with those extras:
CODEBLOCK1

If they say no/skip, continue.

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Step 4 — Generate Pass 1 draft + gap report

Generate a draft llms.txt now using what you have from the crawl. Use all heuristic signals (team_found, testimonials_found, pricing_found, etc.) and the raw_text_summary.

Write the draft. For any section you couldn't populate confidently, use a clear [NOT FOUND] placeholder.

Then show it to the user with a gap report:

"Here's a first draft of your llms.txt:

> {draft}
> 

Found automatically: {brief list — e.g. emails, pricing page, testimonials from Wybrid + Cital}
Couldn't determine: {brief list — e.g. team, pricing figures, API}
Two questions to start:

1. 1. {Most important gap — e.g. "Who's on the founding team? Names, roles, and an email if you're comfortable."}
2. {Second most important — e.g. "What's your pricing model? Even a rough description — per-candidate, subscription, etc."}

_(I have a few more after these. Also — say 'dig deeper' if you'd rather I try to find it myself.)"

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Step 4b — Handle "dig deeper" (Pass 2)

If the user says "dig deeper" (or similar — "try again", "re-crawl", "look harder"):

Re-run the crawl in deep mode:
CODEBLOCK3

This returns pages_raw — the full raw text of every crawled page. Use it to extract structure with the LLM. In your generation prompt (Step 5), add:

CODEBLOCK4

Tell the user:

"Doing a deeper crawl — this takes a bit longer but I'll extract everything I can from the raw page content."

After Pass 2, show the updated draft with the same gap report format. Whatever still can't be found, ask the user directly.

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Step 5 — Conversational gap-filling (for anything still missing)

Ask questions one at a time — only for things still [NOT FOUND] after Pass 1/2. Wait for each answer. Stop as soon as you have enough to finalize.

Use your judgment — if the user has already filled most gaps conversationally, skip remaining questions and generate.

Q1 — Core value for agents (always ask):

"In one or two sentences: what should an AI agent understand about what it can do or get by working with {domain}?"

Q2 — Team (ask if team not found in crawl):

"I didn't find team info publicly. Want to add a Team section? It helps agents trust who's behind the business. Just names, roles, and emails if you're comfortable."

Q3 — Clients / testimonials (ask if not found):

"Any existing clients or testimonials I can include? Even a couple of company names or a one-line quote builds agent trust. Totally optional."

Q4 — API / integration (ask if api_found=false):

"Is there a public API or docs page agents can reference? (skip if not applicable)"

Q5 — Pricing (ask if pricing_found=false):

"What's the pricing model? Even a rough description helps — like 'per assessment' or 'monthly subscription'."

Q6 — ICP / agent-buyers (ask if not obvious from context):

"Who are the kinds of agents or automated systems most likely to want to work with you? (e.g. HR bots, recruiting pipelines)"

Q7 — Anything else (optional, ask last):

"Anything else agents should know before working with you? (geographic limits, onboarding steps, etc.)"

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Step 6 — Generate final llms.txt

Read references/llms_txt_spec.md now if you haven't already.

Generate the complete llms.txt using ALL information gathered:

• - The crawled business_info JSON (and pages_raw if deep mode ran)
• The user's answers from the conversation
• The spec from INLINECODE20

Generation rules:

1. 1. Follow the spec format exactly: H1 title → blockquote summary → H2 sections → named links
2. Every bullet = - [Title](url): description — no plain text bullets
3. Section order: Services → Team → Clients & Testimonials → Compliance → Reviews → For Agents → Pricing → API → Links → Optional
4. ## Team: Always include. Use crawled/user-provided data. If none available, omit silently.
5. ## Clients & Testimonials: Always try to include. Structure:

- ICP bullets first (who the business serves)
- Then a ### subsection per named client where you have a real quote or case study detail
- Each subsection: blockquote with verbatim/lightly-cleaned quote, optional Problem: and Outcome: lines
- If you only have a name + one-liner with no detail, a single bullet is fine
- Never invent quotes or outcomes

1. 6. ## Compliance: Include if any certifications or standards (SOC 2, ISO 27001, GDPR, HIPAA, etc.) are mentioned anywhere on the site or by the user. Omit if none found.
2. ## Reviews: Include if any third-party ratings, scores, awards, or recognitions (G2, ProductHunt, Trustpilot, Gartner, Capterra, Forbes, YC, etc.) are mentioned. Omit if none found.
3. ## For Agents: ALWAYS include. If no API info: add the "coming soon" notice + contact email. Never skip.
4. ## Pricing: If unknown, link to pricing page with no summary. If no pricing page, omit.
5. ## API: Document URL only — no auth details, no secrets.
6. ## Optional: FAQs, blog, case studies, anything supplementary.
7. Do NOT invent facts. If something is unknown and user didn't provide it, either omit it or note it clearly.
8. Keep it tight — this is for agents, not humans. No marketing fluff.

Write the final llms.txt to /tmp/samvida_llms.txt.

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Step 7 — Show and confirm

Show the full llms.txt to the user in a code block, then ask:

"Here's your llms.txt 👆
Does this look right? You can:

• - Tell me what to change
• Say 'save' to download it
• Say 'deploy' when you're ready to push it live (Phase 2)"

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Step 8 — Handle revisions

If the user asks for changes, make them and show the updated version. Repeat until satisfied.

If they say 'save': tell them the file is at /tmp/samvida_llms.txt and they can copy it to their project.

If they say 'deploy': proceed to Step 9.

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Step 9 — Deploy

If an existing llms.txt was found during crawl, warn first:

"⚠️ I found an existing llms.txt at {domain}/llms.txt. Deploying will replace it. Want to see a diff first, or go ahead?"

Show a simple diff if requested (old vs new, first 20 lines each).

First, detect the platform — check the crawl data for CMS detection, or ask:

"Which platform is {domain} hosted on? (Webflow / Framer / Cloudflare / other)"

Then follow the relevant path below.

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9a — Cloudflare Workers (any site with Cloudflare DNS)

Best for: any site whose DNS goes through Cloudflare (the orange cloud ☁️ is enabled).

"To deploy to {domain}/llms.txt via Cloudflare Workers, I need 3 things:

1. 1. API Token — Cloudflare dashboard → My Profile → API Tokens → Create Token → 'Edit Cloudflare Workers' template
2. Account ID — top-right of your Cloudflare dashboard
3. Zone ID — Cloudflare dashboard → click your domain → right sidebar under 'API'

These are only used for this deployment and never stored."

CODEBLOCK5

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9b — Webflow (fully automated)

Best for: sites hosted on Webflow (webflow.io or custom domain via Webflow hosting).

"To deploy to Webflow, I need your Webflow Site API Token:
Webflow dashboard → your site → Site Settings → Integrations → API Access → Generate API Token
Scopes to enable: Assets (Read/Write), Sites (Read), Redirects (Read/Write), Publishing (Publish)
Optionally: your Site ID (visible in the Webflow dashboard URL — auto-detected if omitted)."

CODEBLOCK6

How it works: Uploads llms.txt to Webflow's CDN → adds a 301 redirect /llms.txt → CDN URL → publishes. Agents follow the redirect transparently.

Note: Redirect API requires Webflow Basic plan or above. If the user is on Starter, Samvida will output manual redirect steps.

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9c — Framer (instructions-only)

Framer has no public REST API for file hosting or redirect management. No credentials needed — just run the script and relay the output.

CODEBLOCK7

The script outputs three options (A/B/C) with step-by-step instructions and prints the full llms.txt content for the user to save. Relay all of it clearly to the user.

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9d — CMS detected (Cloudflare Worker deployed but CMS takes priority)

On CMS detected (output contains SAMVIDA_CMS:{name}):

"The Worker deployed successfully, but {CMS} is serving /llms.txt directly from their servers — so it takes priority over the Worker.
Run the right deploy command for your platform:
{paste the CMS-specific instructions from the script output}

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On any error: relay the script's human-readable error message directly with a suggested fix.

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Notes

• - Existing llms.txt: If the crawl found one, mention it early: "I noticed you already have a llms.txt. I'll generate a fresh one — you can compare and decide which to keep."
• Anchor-only links (e.g. /#section): Skip for Level 2 crawling — they don't load new content.
• The For Agents section is mandatory — even if empty of details, it signals intent to support agents and provides a contact path.
• Never ask all questions at once — it's a conversation, not a form.