Kanchi Dividend Review Monitor

Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Use when users ask for 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks.

No API

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Detect abnormal dividend-risk signals and route them into a human review queue. Treat automation as anomaly detection, not automated trade execution.

2. When to Use

Use this skill when the user needs:

  • Daily/weekly/quarterly anomaly detection for dividend holdings.
  • Forced review queueing for T1-T5 risk triggers.
  • 8-K/governance keyword scans tied to portfolio tickers.
  • Deterministic OK/WARN/REVIEW output before manual decision making.

3. Prerequisites

Provide normalized input JSON that follows:

  • references/input-schema.md

If upstream data is unavailable, provide at least:

  • ticker
  • instrument_type
  • dividend.latest_regular
  • dividend.prior_regular

4. Quick Start 

python3 skills/kanchi-dividend-review-monitor/scripts/build_review_queue.py \
  --input /path/to/monitor_input.json \
  --output-dir reports/

5. Workflow

1) Normalize input dataset

Collect per ticker fields in one JSON document:

  • Dividend points (latest regular, prior regular, missing/zero flag).
  • Coverage fields (FCF or FFO or NII, dividends paid, ratio history).
  • Balance-sheet trend fields (net debt, interest coverage, buybacks/dividends).
  • Filing text snippets (especially recent 8-K or equivalent alert text).
  • Operations trend fields (revenue CAGR, margin trend, guidance trend).

Use references/input-schema.md for field definitions and sample payload.

2) Run the rule engine

Run:

python3 skills/kanchi-dividend-review-monitor/scripts/build_review_queue.py \
  --input /path/to/monitor_input.json \
  --output-dir reports/

The script maps each ticker to OK/WARN/REVIEW based on T1-T5. Output files are saved to the specified directory with dated filenames (e.g., review_queue_20260227.json and .md).

3) Prioritize and deduplicate

If multiple triggers fire:

  • Keep all findings for audit trail.
  • Escalate final state to highest severity only.
  • Store trigger reasons as single-line evidence.

4) Generate human review tickets

For each REVIEW ticker, include:

  • Trigger IDs and evidence.
  • Suspected failure mode.
  • Required manual checks for next decision.

Use references/review-ticket-template.md output format.

6. Resources

References:

  • skills/kanchi-dividend-review-monitor/references/input-schema.md
  • skills/kanchi-dividend-review-monitor/references/review-ticket-template.md
  • skills/kanchi-dividend-review-monitor/references/trigger-matrix.md

Scripts:

  • skills/kanchi-dividend-review-monitor/scripts/build_review_queue.py