kanchi-dividend-review-monitor

Detect dividend risk signals and route anomalies into a human review queue.

2|Updated Jun 14, 2026
One-click install
npx skills add https://github.com/IhsanDanish25/claude-trading-skills --skill kanchi-dividend-review-monitor-ihsandanish25
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: kanchi-dividend-review-monitor
Source: https://github.com/IhsanDanish25/claude-trading-skills/tree/main/skills/kanchi-dividend-review-monitor
Command: npx skills add https://github.com/IhsanDanish25/claude-trading-skills --skill kanchi-dividend-review-monitor-ihsandanish25

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps traders monitor and review dividend portfolio risk by detecting abnormal signals and routing them for human review.

Core Features & Use Cases

  • Dividend Risk Monitoring: Detects dividend risk signals such as dividend cuts, coverage issues, and governance alerts.
  • Risk Triggers: Utilizes Kanchi-style forced-review triggers (T1-T5) to identify potential risks.
  • Human Review Queue: Routes detected anomalies into a human review queue for further analysis.
  • Use Case: For a trader managing a dividend portfolio, this Skill can automatically detect when a company's dividend is at risk, such as a potential cut or other issues, and notify for review.

Quick Start

Run the skill to monitor dividend portfolio risk using the command: python3 skills/kanchi-dividend-review-monitor/scripts/build_review_queue.py --input /path/to/monitor_input.json --output-dir reports/

Frequently Asked Questions about kanchi-dividend-review-monitor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I monitor dividend portfolio risk and detect potential cuts?

You can monitor dividend portfolio risk by running an automated script that analyzes financial metrics to detect abnormal signals like dividend cuts, coverage issues, and governance alerts, routing them to a human review queue.

What financial data do I need to provide for dividend risk monitoring?

Dividend risk monitoring requires input data structured as a JSON file containing tickers, dividend information, and related financial metrics to accurately detect coverage issues and governance alerts.

How do I generate a human review queue for dividend risk signals?

You generate a review queue by executing the Python build script with your input JSON path and an output directory, which processes the financial metrics and flags anomalies for manual analysis.

Can I use this for SEC filing monitoring to detect governance alerts?

Yes, the skill monitors SEC filings and related financial metrics to detect governance alerts and coverage issues, routing them into a forced-review queue for further human analysis.

What are the limitations of using automated dividend cut detection?

Automated dividend cut detection routes anomalies into a human review queue rather than executing trades. It requires accurate input data and serves as a forced-review mechanism, meaning manual analysis is still necessary.