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.