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Agent skill

Kanchi Dividend Review Monitor

by tradermonty in tradermonty/claude-trading-skills

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

MITAuto-check passedData & Analytics

Install Kanchi Dividend Review Monitor

skills CLI
$ npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-review-monitor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tradermonty/claude-trading-skills kanchi-dividend-review-monitor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/kanchi-dividend-review-monitor .claude/skills/kanchi-dividend-review-monitor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
kanchi-dividend-review-monitor
GitHub stars
3k
Used in
1 other repo
Token cost
~1.3k tokens
SKILL.md length
576 words
Files
9 (incl. scripts, references)
Skills in repo
74
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 4 steps: Normalize input dataset → Run the rule engine → Prioritize and deduplicate → …
  • Convert anomalies into OK/WARN/REVIEW states without auto-selling
  • SKILL.md covers Overview, When to Use, Prerequisites and Non-Negotiable Rule, plus 7 more sections
  • Runs Python scripts from its folder; calls python3; reaches data.sec.gov

What it does

Kanchi Dividend Review Monitor is an agent skill from tradermonty/claude-trading-skills. 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.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/input-schema.md` and `references/review-ticket-template.md`).

It sits in Data & Analytics. The repository describes itself as: Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development. The licence is MIT.

When your agent uses it

  • Convert anomalies into OK/WARN/REVIEW states without auto-selling
  • Users ask for 減配検知
  • Periodic dividend risk checks

Example prompts

  • “/kanchi-dividend-review-monitor”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Normalize input dataset
  2. Run the rule engine
  3. Prioritize and deduplicate
  4. Generate human review tickets

What it can do on your machine

Read from SKILL.md and the folder at commit c8d58f0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • data.sec.gov

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Kanchi Dividend Review Monitor loads about 1.3k tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 70 tokens; SKILL.md has 576 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from tradermonty/claude-trading-skills at commit c8d58f0, republished under its MIT licence (© tradermonty). 576 words, ~1,296 tokens.

Download SKILL.mdSave it as .claude/skills/kanchi-dividend-review-monitor/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
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/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.

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:

bash
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.
Show full SKILL.md (217 more words)Show less
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.
  • In scheduled portfolio reviews where upstream filing snippets are empty, use SEC 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.

Output Contract

Always return:

  1. Queue JSON with summary counts and ticker-level findings.
  2. Markdown dashboard for quick triage.
  3. 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.

© tradermonty, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references) in skills/kanchi-dividend-review-monitor of tradermonty/claude-trading-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/input-schema.md
  • references/review-ticket-template.md
  • references/trigger-matrix.md
  • requirements.txt
  • scripts/build_review_queue.py
  • scripts/tests/conftest.py
  • scripts/tests/test_build_review_queue.py

Open the folder on GitHubat commit c8d58f0

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in tradermonty/claude-trading-skills, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Kanchi Dividend Review Monitor

What does Kanchi Dividend Review Monitor do?

Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Kanchi Dividend Review Monitor is an agent skill from tradermonty/claude-trading-skills. Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling.

When should I use Kanchi Dividend Review Monitor?

Kanchi Dividend Review Monitor fits situations like: convert anomalies into OK/WARN/REVIEW states without auto-selling; users ask for 減配検知; periodic dividend risk checks.

How do I install Kanchi Dividend Review Monitor in Claude Code?

Run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-review-monitor -a claude-code`. Or copy the skill folder (skills/kanchi-dividend-review-monitor in tradermonty/claude-trading-skills) into .claude/skills/kanchi-dividend-review-monitor in your project. Claude Code loads it when a task matches its description.

How do I install Kanchi Dividend Review Monitor in Codex?

Run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-review-monitor -a codex`. Or copy the skill folder (skills/kanchi-dividend-review-monitor in tradermonty/claude-trading-skills) into .agents/skills/kanchi-dividend-review-monitor in your project. Codex loads it when a task matches its description.

Can I use Kanchi Dividend Review Monitor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-review-monitor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kanchi-dividend-review-monitor, .gemini/skills/kanchi-dividend-review-monitor, .github/skills/kanchi-dividend-review-monitor and .opencode/skills/kanchi-dividend-review-monitor in your project.

What does Kanchi Dividend Review Monitor need to run?

Going by SKILL.md and its folder, Kanchi Dividend Review Monitor needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Kanchi Dividend Review Monitor access the network?

SKILL.md names 1 domain. In commands or code: data.sec.gov; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Kanchi Dividend Review Monitor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Kanchi Dividend Review Monitor use?

Kanchi Dividend Review Monitor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Kanchi Dividend Review Monitor use?

About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Kanchi Dividend Review Monitor?

Skills that share tags, products or a category with Kanchi Dividend Review Monitor: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Chart Visualization (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kanchi Dividend Review Monitor?

tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,982 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 11, 2026.

Source: tradermonty/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.