From Prototype to $10K MRR: Monetize Your Agent on the Swarms Marketplace

from swarms import Agent

SYSTEM_PROMPT = """You are the Spreadsheet Cleaner Agent.

Your job is to take a messy financial or operational spreadsheet
(CSV or TSV) and return:

1) A cleaned CSV with these guarantees:
   - All dates normalised to ISO 8601 (YYYY-MM-DD)
   - All currency normalised to USD as a float (no symbols, no commas)
   - One header row, snake_case column names, no merged cells
   - Empty/whitespace-only rows removed
   - All numeric columns typed as numbers, not strings
   - Inferred but missing column names labelled column_1, column_2, ...

2) A short CHANGE_LOG section describing exactly what you changed,
   grouped by column. Be specific: name the columns, count the rows
   affected, and call out any data you dropped.

3) A QUALITY_FLAGS section listing rows or columns the user should
   review manually (suspected duplicates, currency you couldn't infer,
   dates older than 1970, etc.).

Output format (strict):
---CLEANED_CSV---
<the cleaned CSV>
---CHANGE_LOG---
<bulleted list>
---QUALITY_FLAGS---
<bulleted list>

Never invent data. If a value is ambiguous, leave it and flag it."""

spreadsheet_cleaner = Agent(
    agent_name="Spreadsheet-Cleaner-Agent",
    agent_description=(
        "Takes a messy financial spreadsheet and returns a cleaned "
        "CSV plus a change log. Built for ops consultants, "
        "bookkeepers, and finance teams."
    ),
    model_name="gpt-4.1-mini",
    system_prompt=SYSTEM_PROMPT,
    max_loops=1,
    max_tokens=8192,
)

import os
import requests

API_KEY = os.environ["SWARMS_API_KEY"]

payload = {
    "name": "Spreadsheet Cleaner Agent",
    "description": (
        "Upload a messy financial or operational CSV. Get back a "
        "cleaned CSV with normalised dates, normalised currency, "
        "snake_case columns, and a change log. Built for ops "
        "consultants, bookkeepers, and finance teams who spend the "
        "first hour of every engagement cleaning files."
    ),
    "language": "python",
    "agent": SYSTEM_PROMPT,  # the system prompt from Step 1
    "useCases": [
        {
            "title": "Quarterly P&L Cleanup",
            "description": (
                "Drop a messy quarterly P&L export. Receive a cleaned "
                "CSV with normalised dates and currency, plus a "
                "change log of every modification."
            ),
        },
        {
            "title": "Bookkeeper Handoff",
            "description": (
                "Normalise a client's raw bank-export CSV so it can "
                "be imported into QuickBooks or Xero without manual "
                "column-mapping."
            ),
        },
        {
            "title": "Operational Dashboard Prep",
            "description": (
                "Take a multi-sheet ops export and return one clean, "
                "typed CSV ready to load into a BI tool."
            ),
        },
    ],
    "tags": "spreadsheet,csv,cleaning,finance,ops,bookkeeping",
    "category": "finance",
    "is_free": False,
    "price_usd": 19.00,
    "seller_wallet_address": os.environ["SOLANA_WALLET_ADDRESS"],
}

resp = requests.post(
    "https://swarms.world/api/add-agent",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    },
    json=payload,
    timeout=60,
)

data = resp.json()
resp.raise_for_status()

print("Listing URL:", data["listing_url"])
print("Agent ID:", data["id"])

import os
import requests

API_KEY = os.environ["SWARMS_API_KEY"]
SOLANA_PRIVATE_KEY = os.environ["SOLANA_PRIVATE_KEY"]

payload = {
    "name": "Spreadsheet Cleaner Agent",
    "description": (
        "Upload a messy financial CSV. Get back a clean one — "
        "normalised dates, normalised currency, snake_case columns, "
        "full change log. Built for ops consultants and bookkeepers."
    ),
    "ticker": "CLEAN",
    "private_key": SOLANA_PRIVATE_KEY,
    "image": "https://your-cdn.example.com/clean-agent-icon.png",
    "fee_selection": "market",   # "frenzy" for 2x fees + leaderboard
    "quote_mint": "SOL",         # or "USDC"
}

resp = requests.post(
    "https://swarms.world/api/token/launch",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    },
    json=payload,
    timeout=120,
)

data = resp.json()
resp.raise_for_status()

print("Listing:", data["listing_url"])
print("Token mint:", data["token_address"])
print("Pool:", data.get("pool_address"))

1/ I clean ~30 client spreadsheets a month. This used to take 90 min each.
   Now my agent does it in 30 seconds.

   Here's what a real run looked like this morning. [image: messy input]

2/ Same file, 30 seconds later. [image: cleaned output]

3/ And the change log — every modification, by column, with row counts.
   This is the part my clients actually care about. [image: change log]

4/ Live here: swarms.world/agent/<your-id>. $19/mo, unlimited runs.

# Sketch — a FastAPI endpoint your demo page calls.
# Marketplace agents can be invoked via the standard Swarms API
# once you have the agent's system prompt in your code.

from fastapi import FastAPI, UploadFile
from swarms import Agent

app = FastAPI()
demo_agent = Agent(
    agent_name="Spreadsheet-Cleaner-Demo",
    model_name="gpt-4.1-mini",
    system_prompt=SYSTEM_PROMPT,   # same as your listed agent
    max_loops=1,
)

@app.post("/demo")
async def demo(file: UploadFile):
    raw = (await file.read()).decode("utf-8")
    # Cap demo input to keep your spend bounded.
    if len(raw) > 50_000:
        return {"error": "Demo limited to 50KB. Subscribe for unlimited."}
    cleaned = demo_agent.run(f"Clean this CSV:\n\n{raw}")
    return {"cleaned": cleaned}

import os
import requests

payload = {
    "ca": os.environ["TOKEN_MINT_ADDRESS"],    # from Step 4 response
    "privateKey": os.environ["SOLANA_PRIVATE_KEY_BASE58"],
}

resp = requests.post(
    "https://swarms.world/api/product/claimfees",
    headers={"Content-Type": "application/json"},
    json=payload,
    timeout=60,
)

data = resp.json()
resp.raise_for_status()

if data.get("amountClaimedSol") is not None:
    print(f"Just claimed: {data['amountClaimedSol']:.4f} SOL")

if data.get("fees"):
    print(f"Unclaimed:    {data['fees']['unclaimedSol']:.4f} SOL")
    print(f"Lifetime claimed: {data['fees']['claimedSol']:.4f} SOL")
    print(f"Lifetime total:   {data['fees']['totalSol']:.4f} SOL")

print("Tx signature:", data.get("signature"))