kalodata-integrations

name	kalodata-integrations
description	Multi-platform integrations for Kalodata research. Connect Shopify for product listings, Notion for research reports, and Slack for alerts + daily digests. CLI-friendly with config-based API key management. Use when working with kalodata integrations.
domain	integrations
author	oyi77
license	Apache-2.0
subdomain	integrations
tags	["api","integrations","kalodata","notion","slack","third-party"]
metadata	{"model":"sonnet"}
version	1.0.0

Kalodata Integrations Skill

Multi-platform connections for Kalodata research automation.

Overview

Enables multi-platform integrations for Kalodata research automation, connecting Shopify for product listings, Notion for research reports, and Slack for alerts and daily digests. Provides CLI-friendly configuration management with secure API key handling.

When to Use

Trigger phrases:

• "kalodata integrations"

• "Multi-platform integrations for Kalodata research"

• User wants to create Shopify product listings from TikTok Shop research

• User needs to save research reports to Notion databases

• User wants Slack alerts for new products and daily digests

• User prefers CLI-based integration management

• User needs secure API key configuration (not hardcoded)

The Process

1. Set up configuration – Create .kalodata-integrations config directory with credentials
2. Choose platform to integrate – Select from Shopify, Notion, or Slack
3. Implement integration – Use the core integration patterns
4. Test and deploy – Verify connection and automate workflows

How to Use

1. Obtain Kalodata API credentials from the dashboard
2. Configure authentication headers for API requests
3. Use the product research endpoints to fetch trending data
4. Export findings to your analytics pipeline

API Integration

import requests

KALODATA_API = "https://api.kalodata.com/v1"
headers = {"Authorization": f"Bearer {KALODATA_TOKEN}"}

resp = requests.get(f"{KALODATA_API}/products/trending", headers=headers)
products = resp.json()["data"]

import csv
with open("trending.csv", "w", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=[, , ])
    writer.writeheader()
    writer.writerows(products)