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.