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 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks.
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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 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks.
Detect abnormal dividend-risk signals and route them into a human review queue. Treat automation as anomaly detection, not automated trade execution.
Use this skill when the user needs:
OK/WARN/REVIEW output before manual decision making.Version history
Provide normalized input JSON that follows:
references/input-schema.mdIf upstream data is unavailable, provide at least:
tickerinstrument_typedividend.latest_regulardividend.prior_regularNever auto-sell based only on machine triggers.
Always create WARN or REVIEW evidence for human confirmation first.
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.
When T6 is driven only by freeze_flag / latest regular dividend equal to prior regular dividend, treat it as a WARN for cadence confirmation, not as proof of dividend deterioration. Many quarterly dividend payers repeat the same dividend for several quarters between annual raise cycles. In reports, phrase this as “confirm next dividend-growth cadence / pause optional adds until checked” and avoid implying a cut or broken thesis unless T1/T2/T3/T4/T5 evidence also supports escalation.
Collect per ticker fields in one JSON document:
Use references/input-schema.md for field definitions
and sample payload.
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).
If multiple triggers fire:
For each REVIEW ticker, include:
Use references/review-ticket-template.md output format.
When implementing live SEC fetchers:
User-Agent string (name + email).company_tickers.json plus https://data.sec.gov/submissions/CIK##########.json to enumerate recent 8-K / 8-K/A filings for each holding, then scan primary filing documents for the T4 keyword family (Item 4.02, non-reliance, restatement, material weakness, SEC investigation, subpoena, going concern, auditor resignation, internal control). Record the scan window, recent 8-K count, and whether hits were found. Treat "no keyword hits" as a narrow T4 scan result, not a full governance clearance.Always return:
REVIEW tickets.kanchi-dividend-sop.REVIEW results back to kanchi-dividend-sop for re-underwriting and position-size review.kanchi-dividend-us-tax-accounting when risk events imply account relocation decisions.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.In these kits
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