kanchi-dividend-review-monitor

kanchi-dividend-review-monitor is a skill for Codex from BaggaT236/AI-Trading-Skills. It costs 81 tokens per session (1,183 once invoked), scanned A, a copy of kanchi-dividend-review-monitor, MIT.

A dividend-portfolio monitor that detects unusual dividend and company-governance risks and places holdings into OK, WARN, or REVIEW states for human checking. A dividend is a payment a company makes to shareholders.

In plain words
What is it for?
Use it for recurring dividend-risk checks, reduced-dividend detection, 8-K governance keyword scans, and routing holdings to a warning or immediate human-review queue.
Why use it?
It creates a consistent review queue for possible dividend cuts or related warning signs without automatically selling investments.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it for recurring dividend-risk checks, reduced-dividend detection, 8-K governance keyword scans, and routing holdings to a warning or immediate human-review queue.

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Install with agentmods
npx agentmods add skills/baggat236/ai-trading-skills/kanchi-dividend-review-monitor
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add BaggaT236/AI-Trading-Skills --skill kanchi-dividend-review-monitor
Clone the repo
git clone --depth 1 https://github.com/BaggaT236/AI-Trading-Skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for kanchi-dividend-review-monitor

README.md
[![agentmods](https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/kanchi-dividend-review-monitor/github.svg)](https://agentmods.dev/skills/baggat236/ai-trading-skills/kanchi-dividend-review-monitor)
Your own site
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/kanchi-dividend-review-monitor"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/kanchi-dividend-review-monitor/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for kanchi-dividend-review-monitor

Your own site · 80×15
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/kanchi-dividend-review-monitor"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/kanchi-dividend-review-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,183 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00081 $0.01183
Opus 5 $0.00041 $0.00592
Sonnet 5 $0.00016 $0.00237
Haiku 4.5 $0.00008 $0.00118

Measured 13d ago against content hash 20da33d7dc9c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

kanchi-dividend-review-monitor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/build_review_queue.py, scripts/tests/conftest.py, scripts/tests/test_build_review_queue.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to kanchi-dividend-review-monitor — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/kanchi-dividend-review-monitor/SKILL.md · 130 lines

How it starts

The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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).

Read the full file on GitHub · 130 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 130 lines · 81 tokens per session scan A 20da33d7dc9c

Subscribe to this mod's changes

kanchi-dividend-review-monitor is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 81 tokens to every session and 1,183 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to kanchi-dividend-review-monitor, differing in 0 lines, and is treated as a copy.

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