Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Use when…
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.
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by @clawhub