kanchi-dividend-review-monitor
tradermonty/claude-trading-skills
Monitor dividend portfolios for risk anomalies with Kanchi-style T1-T5 triggers, routing findings to human review without auto-selling.
What is kanchi-dividend-review-monitor?
Detects abnormal dividend-risk signals in holdings and routes them into a review queue using a deterministic state machine (OK/WARN/REVIEW). Use this skill when you need daily/weekly/quarterly anomaly detection, forced review queueing for dividend cuts, coverage deterioration, or governance concerns, with the guarantee that no trades execute automatically.
- Scan for T1-T5 dividend-risk triggers: cuts/suspensions, coverage deterioration, proxy credit stress, SEC filing keywords, and structural decline scoring
- Convert anomalies into deterministic OK/WARN/REVIEW states with audit-trail evidence
- Generate human review tickets with trigger IDs, suspected failure modes, and required manual checks
- Support daily T1/T4 scans, weekly T3 checks, and quarterly T2/T5 analysis
- Integrate with SEC filing data (8-K keyword scanning) while respecting fair-access guidelines
- Deduplicate multiple triggers and escalate to highest severity only
How to install kanchi-dividend-review-monitor
npx skills add https://github.com/tradermonty/claude-trading-skills --skill kanchi-dividend-review-monitor- Normalized input JSON following the provided input-schema.md with ticker, instrument_type, dividend history, coverage metrics, balance-sheet trends, and optional filing text snippets
- Python 3 environment to run the build_review_queue.py script
- Access to SEC filing data (either pre-fetched snippets or live EDGAR access with compliant User-Agent and caching)
How to use kanchi-dividend-review-monitor
- 1.Collect per-ticker dividend, coverage, balance-sheet, and filing data into a single normalized JSON document using the input-schema.md template
- 2.Run the rule engine: python3 skills/kanchi-dividend-review-monitor/scripts/build_review_queue.py --input /path/to/monitor_input.json --output-dir reports/
- 3.Review the generated review_queue JSON and markdown dashboard to identify OK, WARN, and REVIEW tickers
- 4.Prioritize REVIEW findings by trigger severity and generate manual review tickets using the review-ticket-template.md format
- 5.Feed REVIEW results back to kanchi-dividend-sop for re-underwriting and position-size decisions; share account-type context with kanchi-dividend-us-tax-accounting if relocation is needed
Use cases
- Daily monitoring of dividend holdings for cuts or suspension signals (T1) and governance red flags (T4)
- Weekly stress-check of dividend coverage ratios to detect deterioration trends (T2/T3)
- Quarterly structural-decline scoring to identify long-term dividend sustainability risks (T5)
- Automated review-queue generation for portfolio managers to triage dividend-risk findings before any position changes
- Governance scanning of recent 8-K filings for material weakness, auditor resignation, or SEC investigation keywords tied to dividend payers
- Dividend-focused portfolio managers and analysts
- Traders managing income-oriented accounts who need systematic risk detection
- Compliance and risk teams requiring audit trails for dividend-position decisions
- Investors using forced-review workflows to separate anomaly detection from trade execution
kanchi-dividend-review-monitor FAQ
No. This skill is strictly anomaly detection and review queueing. It creates WARN and REVIEW states for human confirmation only; it never auto-sells based on machine triggers alone.
T1 = dividend cut/suspension; T2 = coverage deterioration; T3 = proxy credit stress; T4 = SEC filing keyword scan (8-K governance); T5 = structural decline scoring. See references/trigger-matrix.md for thresholds and cadence.
Treat flat-dividend cadence as a WARN for cadence confirmation, not proof of deterioration. Many quarterly payers repeat the same dividend between annual raise cycles. Escalate to REVIEW only if T1/T2/T3/T4/T5 evidence also supports it.
Yes. If filing snippets are unavailable, the skill can fetch recent 8-K filings directly from SEC EDGAR using company_tickers.json and CIK lookups, with compliant User-Agent, caching, and throttling.
It consumes ticker universe and baseline assumptions from kanchi-dividend-sop, feeds REVIEW results back for re-underwriting, and shares account-type context with kanchi-dividend-us-tax-accounting for position-relocation decisions.
Full instructions (SKILL.md)
Source of truth, from tradermonty/claude-trading-skills.
name: kanchi-dividend-review-monitor description: 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.
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/REVIEWoutput before manual decision making.
Prerequisites
Provide normalized input JSON that follows:
references/input-schema.md
If upstream data is unavailable, provide at least:
tickerinstrument_typedividend.latest_regulardividend.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.
Flat-dividend cadence caveat
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.
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-Agentstring (name + email). - Use caching and throttling.
- Respect SEC fair-access guidance.
- In scheduled portfolio reviews where upstream filing snippets are empty, use SEC
company_tickers.jsonplushttps://data.sec.gov/submissions/CIK##########.jsonto 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.
Output Contract
Always return:
- Queue JSON with summary counts and ticker-level findings.
- Markdown dashboard for quick triage.
- List of immediate
REVIEWtickets.
Multi-Skill Handoff
- Consume ticker universe and baseline assumptions from
kanchi-dividend-sop. - Feed
REVIEWresults back tokanchi-dividend-sopfor re-underwriting and position-size review. - Share account-type context with
kanchi-dividend-us-tax-accountingwhen 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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