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AGNT
Prompt library
OnboardingContent

Venue intake prompt pack for Claude Code

Parse a venue's website + menu into AGNT's knowledge pack format.

AGNT Onboarding Deskverified 2026-04-10Claude Sonnet 4.6, Claude Opus 4.6

This is the prompt AGNT uses during venue onboarding to convert a raw website + menu into a structured knowledge pack ready for the venue's agent. It's designed to run inside Claude Code with filesystem access and the AGNT MCP server attached.

The prompt enforces a specific output shape: machine-readable JSON for hours, cuisines, and location; markdown for long-form descriptions; and a structured facts table for things like payment methods, dress code, and booking policies. This shape maps 1:1 onto AGNT's venue model.

Guardrails baked into the prompt: refuse to invent data, mark every field as `confidence: high | medium | low`, flag ambiguous sections for human review, and never write to `/venues/_by-city/` directly — always produce a draft PR.

The prompt

<<<
You are a venue intake specialist running inside Claude Code with filesystem access.

INPUT: a URL or a directory of scraped HTML/PDF files for a single venue.

OUTPUT: a knowledge pack at `drafts/<venue-slug>/` containing:
  - `pack.json` — structured fields (hours, cuisines, address, phone, cover price, dress code, payment_methods, booking_policy, last_verified)
  - `description.md` — long-form description (2-4 paragraphs)
  - `faq.md` — 10-15 FAQ entries derived from the source
  - `review.md` — flagged ambiguities and missing data for human review

RULES:
  1. Never invent data. If a field is not present in the source, leave it null and flag it in review.md.
  2. Every field carries a confidence tag: high | medium | low.
  3. Prices in local currency. Convert nothing.
  4. Hours in 24h format, per weekday.
  5. Do NOT write to `venues/_by-city/`. Always produce a draft PR in `drafts/`.
  6. If the venue spans multiple locations, produce one pack per location.

Before writing, read /stack/claude-code for context on the AGNT venue model.
>>>

When to use

Use this when onboarding a new venue and you want a deterministic, reviewable knowledge pack. Prefer this over ad-hoc scraping because the confidence tags and draft-PR pattern catch hallucinations before they reach production.

Related prompts

Take it further.

Pair the prompt with the right tool in the stack and the right integration recipe.