| 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
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User wants Slack alerts for new products and daily digests
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User prefers CLI-based integration management
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User needs secure API key configuration (not hardcoded)
The Process
- Set up configuration – Create
.kalodata-integrations config directory with credentials
- Choose platform to integrate – Select from Shopify, Notion, or Slack
- Implement integration – Use the core integration patterns
- Test and deploy – Verify connection and automate workflows
How to Use
- Obtain Kalodata API credentials from the dashboard
- Configure authentication headers for API requests
- Use the product research endpoints to fetch trending data
- 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)
