Kanchi dividend review monitor

Skill xonevn-ai/xone-trading-skills/skills/kanchi-dividend-review-monitor

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Install

npx -y skills add xonevn-ai/xone-trading-skills --skill kanchi-dividend-review-monitor

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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 dividend-cut detection, 8-K governance monitoring, dividend safety monitoring, REVIEW queue automation, or periodic dividend risk checks.

SKILL.md

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Kanchi Dividend Review Monitor

Overview

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

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.

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

Non-Negotiable Rule

Never auto-sell based only on machine triggers. Always create WARN or REVIEW evidence for human confirmation first.

State Machine

OK: no action.
WARN: add to next check cycle and pause optional adds.
REVIEW: immediate human review ticket + pause adds.

Use references/trigger-matrix.md for trigger thresholds and actions.

Monitoring Cadence

Daily:
- T1 dividend cut/suspension.
- T4 SEC filing keyword scan (8-K oriented).
Weekly:
- T3 proxy credit stress checks.
Quarterly:
- T2 coverage deterioration and T5 structural decline scoring.

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.

SEC Filing Guardrail

When implementing live SEC fetchers:

Include a compliant User-Agent string (name + email).
Use caching and throttling.
Respect SEC fair-access guidance.

Output Contract

Always return:

Queue JSON with summary counts and ticker-level findings.
Markdown dashboard for quick triage.
List of immediate REVIEW tickets.

Multi-Skill Handoff

Consume ticker universe and baseline assumptions from kanchi-dividend-sop.
Feed REVIEW results back to kanchi-dividend-sop for re-underwriting and position-size review.
Share account-type context with kanchi-dividend-us-tax-accounting when risk events imply account relocation decisions.

Resources

scripts/build_review_queue.py: local rule engine for T1-T5.
scripts/tests/test_build_review_queue.py: unit tests for T1-T5 and report rendering.
references/trigger-matrix.md: trigger definitions, cadence, and actions.
references/input-schema.md: normalized input schema and sample JSON.
references/review-ticket-template.md: standardized manual-review ticket layout.