mcpbeat

Rfq Quote

langchain-ai/lca-deepagents-rfq-quote

Process an incoming request for quote (RFQ) from a customer: read the email, look up the customer and catalogue prices, compute a quote, have it reviewed, draft the reply, and log it. Use whenever a customer asks for a price, a quote, or to license/buy a batch of tracks.

This is a copy. The original lives at langchain-ai/rfq-quote.

631 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
19
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/langchain-ai/lca-deepagents --skill rfq-quote

The instruction itself

9 sections, as written by the author

Processing a Request for Quote

Follow these steps in order. Keep a todo list so Jane can see progress.

1. Read the request

  • Ask inbox-manager to find the request and read it in full. Pull out: who

is asking, their company/email, and exactly what they want (which

genres/tracks, how many of each).

2. Identify the customer

  • Ask chinook-analyst to find the customer by email (and name as a

fallback).

  • If they are not in the system, ask chinook-analyst to add them with

add_customer. The system pauses automatically for Jane to approve — the

analyst should just make the call, not ask in prose. Once approved, continue.

3. Get the prices

  • Ask chinook-analyst for the unit prices needed:
  • For "N tracks in genre X": the count of available tracks and the standard

UnitPrice for that genre (tracks are normally $0.99; verify, don't assume).

  • For "best-selling tracks": ask the analyst for the top sellers by quantity.
  • Get real numbers from the database; never guess a price.

4. Compute the quote (exactly)

  • Use the code interpreter to do the arithmetic — quantities × unit prices,

any line discounts, and the total. Never hand-add money.

  • A reasonable default volume discount: 10% off when the order is 50+ tracks.

State the discount explicitly in the quote.

5. Have it reviewed

  • Send the line items and totals to quote-reviewer. Apply its corrections

before drafting.

6. Draft the reply

  • Write a short, friendly reply from Jane: thank them, list each line

(description, qty, unit price, line total), show any discount and the grand

total, and offer next steps.

  • Ask inbox-manager to save it as a draft to the sender, passing the

subject and the full body. The system pauses automatically for Jane to

approve or edit the wording before it's saved — don't ask for permission in a

message first. Drafts are never auto-sent.

7. Log it

  • Append one line to /outputs/quotes_ledger.md recording: date, customer,

items summary, total, and the draft id. Create the file with a header row if

it doesn't exist yet.

Done

Tell Jane the draft is in her drafts folder and summarize the quote total.

How to use it

Copy the folder

Take langchain-ai/lca-deepagents-rfq-quote from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.