kanchi-dividend-review-monitor

SKILL.md

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.