--- name: kalodata-monitor description: Scheduled research runs with auto-alerts for NEW viral products. Runs on configurable schedule (hourly/daily/weekly), detects new products by comparing with previous runs, alerts on revenue threshold crossings, and sends notifications via Slack webhook. domain: integrations tags: - api - integrations - kalodata - monitor - slack - third-party - webhook metadata: model: sonnet --- # Kalodata Monitor Skill Scheduled research automation with intelligent new product detection and Slack alerts. ## Overview Enables scheduled research automation with intelligent new product detection and Slack alerts. Runs on configurable schedules (hourly/daily/weekly), detects new products by comparing with previous runs, alerts on revenue threshold crossings, and sends notifications via Slack webhook. ## When to Use - User wants continuous monitoring of TikTok Shop product categories - User needs automatic detection of NEW products entering the market - User wants alerts when products cross revenue thresholds - User prefers Slack notifications for urgent product discoveries - User needs scheduled research runs without manual intervention ## The Process 1. **Configure monitoring profile** – Set up profile with category, goal, schedule, and Slack webhook 2. **Set alert conditions** – Define revenue thresholds and other alert criteria 3. **Run or schedule monitoring** – Execute profile manually or let it run on schedule 4. **Review alerts** – Check Slack for notifications and new product findings 5. **Take action** – Investigate new products and make business decisions ## When NOT to Use - Task is outside your authorization scope - You need to implement controls (use implementing-* skills) - Task is about analysis, not action (use analyzing-* skills) - You don't have access to target systems - Task requires compliance expertise (consult professionals) - Task is about defense, not offense (use defensive skills) ## Red Flags - Expecting real-time monitoring (this skill runs on scheduled intervals, not streaming) - Using this for one-time research tasks (use kalodata-research-automation instead) - Monitoring unrelated categories or markets (stick to TikTok Shop Indonesia) - Ignoring alert configuration (alerts without thresholds are just noise) ## Verification - Monitor profile saves without errors and loads on subsequent runs - New products are correctly detected by comparing with previous run data - Slack alerts send successfully and appear in configured channel - Revenue threshold alerts trigger when products cross configured limits - Scheduled runs execute without manual intervention ## Do Not Use This Skill When - User only needs one-time research (use kalodata-research-automation) - User needs real-time product tracking (this is scheduled, not real-time) - Task is unrelated to TikTok Shop Indonesia market monitoring ## Quick Start This section covers quick start for the kalodata-monitor skill. Key operations include input validation, core processing, and output verification. Refer to the skill overview for detailed usage instructions. ### Basic Usage ```typescript import { createMonitor, loadMonitorConfig, createProfile } from './index.js'; const config = loadMonitorConfig('./.kalodata-monitor'); const monitor = createMonitor(config); const profile = createProfile({ id: 'beauty-trending', name: 'Beauty Trending', category: 'Beauty', goal: 'trending', schedule: 'daily', slackWebhook: 'https://hooks.slack.com/services/xxx', }); monitor.saveProfile(profile); const result = await monitor.runProfile('beauty-trending'); console.log(`Found ${result.newProducts.length} new products`); ``` ### CLI Usage ```bash # Set credentials export KALODATA_SESSION=your_session export KALODATA_CF_CLEARANCE=your_cf_clearance # List profiles node index.js list # Add a new monitor node index.js add "Beauty Trends" Beauty trending daily # Run a specific profile node index.js run profile-123 # Run all enabled profiles node index.js run ``` ## Core Features - Core operation execution with comprehensive error handling - Input validation and output quality assurance - Integration with existing workflows and toolchains - Detailed logging for debugging and audit trails ### 1. Monitor Profiles Each profile defines what to monitor and when: ```typescript interface MonitorProfile { id: string; name: string; category: string; // Product category name or ID goal: string; // Research goal (trending, emerging, etc.) schedule: 'hourly' | 'daily' | 'weekly'; alerts: AlertCondition; topProducts?: number; // Default: 20 slackWebhook?: string; enabled?: boolean; } ``` ### 2. Alert Conditions Configure when to trigger notifications: ```typescript interface AlertCondition { revenueMin?: number; // Absolute revenue threshold revenueGrowthMultiplier?: number; // Multiplier vs previous run opportunityScoreMin?: number; // Minimum opportunity score (0-100) revenueCrosses?: number; // Alert when crossing this revenue } ``` ### 3. New Product Detection Automatically compares current run with previous run to identify: - Products that didn't exist in the last run - Products crossing revenue thresholds - Products with rising opportunity scores ### 4. Slack Notifications Rich Slack messages with: - Profile summary - New products list with key metrics - Revenue threshold crossings - Click-through to product details ### 5. Data Persistence Stores in `.kalodata-monitor/` directory: - `config.json` - All profile configurations - `{profile-id}.json` - Previous run snapshots for comparison ## Configuration ```json { "enabled": true, "autoRun": false, "timeout": 30000, "retries": 3 } ``` Set environment variables as needed for authentication and endpoints. ### Environment Variables ```bash KALODATA_SESSION=your_session_cookie KALODATA_CF_CLEARANCE=your_cf_clearance_token ``` ### Profile Configuration Create monitors via CLI or programmatically: | Parameter | Description | Required | |-----------|-------------|----------| | id | Unique identifier | Yes | | name | Display name | Yes | | category | Product category | Yes | | goal | Research goal | Yes | | schedule | hourly/daily/weekly | Yes | | slackWebhook | Slack webhook URL | No | ## CLI Commands | Command | Description | |---------|-------------| | `list` | List all monitor profiles | | `add [webhook]` | Add new profile | | `run [profile-id]` | Run profile or all profiles | | `scheduled` | Run profiles due for their schedule | | `remove ` | Remove a profile | | `enable ` | Enable a profile | | `disable ` | Disable a profile | | `config` | Show full configuration | ## Examples ``` # Basic usage invoke with appropriate parameters # Advanced usage with options invoke --option value --verbose ``` ### Daily Beauty Trends Monitor ```bash node index.js add "Daily Beauty" Beauty trending daily $SLACK_WEBHOOK ``` ### Weekly Emerging Products ```bash node index.js add "Weekly Emerging" Beauty emerging weekly ``` ### Hourly High-Opportunity Products ```bash node index.js add "Hourly High-Opp" Electronics trending hourly ``` ## Integration with Research Automation The monitor uses kalodata-product-research under the hood: ```typescript const result = await monitor.runProfile(profileId); // Result contains: { profileId: string, products: ProductSnapshot[], // All products from this run newProducts: ProductSnapshot[], // NEW since last run crossedThreshold: ProductSnapshot[], // Products crossing thresholds alerts: Alert[], // Generated alerts } ``` ## Scheduling For continuous monitoring, add to cron: ```bash # Run every hour 0 * * * * cd /path/to/project && node index.js scheduled # Run daily at 6 AM 0 6 * * * cd /path/to/project && node index.js scheduled ``` ## Dependencies This skill builds on: - kalodata-product-research: Core research capabilities - kalodata-research-automation: Full research workflow ## Output Format CLI output is designed for human readability: ``` 📋 Monitor Profiles: ✅ Beauty Trending Category: Beauty | Goal: trending | Schedule: daily Last run: 2/19/2026, 10:30:00 AM 🚀 Running all enabled profiles... 📊 Run Results: New products: 5 Threshold crossings: 2 Alerts: 3 ✅ Slack notification sent successfully ``` ## Process 1. Analyze the task requirements 2. Apply domain expertise 3. Verify output quality