kanchi-dividend-sop

kanchi-dividend-sop is a skill for Codex from BaggaT236/AI-Trading-Skills. It costs 83 tokens per session (3,210 once invoked), scanned A, a copy of kanchi-dividend-sop, MIT.

A repeatable process for choosing US dividend stocks and planning cautious entry prices. It screens candidates, checks dividend and business quality, adapts valuation checks such as price-to-earnings and price-to-book ratios by sector, and creates a one-page investment memo.

In plain words
What is it for?
Use it to screen dividend stocks, review dividend growth and quality, plan pullback limit orders, document an investment thesis and invalidation conditions, and prepare monitoring and tax-account handoffs.
Why use it?
It replaces ad-hoc dividend picking with the same checks and decision points each time. It also records when an investment idea should be rejected or reconsidered.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to screen dividend stocks, review dividend growth and quality, plan pullback limit orders, document an investment thesis and invalidation conditions, and prepare monitoring and tax-account handoffs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/baggat236/ai-trading-skills/kanchi-dividend-sop
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-sop
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-sop

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/kanchi-dividend-sop"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/kanchi-dividend-sop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,210 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 92% 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 $0.00083 $0.03210
Opus 5 $0.00042 $0.01605
Sonnet 5 $0.00017 $0.00642
Haiku 4.5 $0.00008 $0.00321

Measured 12d ago against content hash 5718999fd220, 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-sop 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 12d ago.

The scan reads SKILL.md. This mod also ships 19 executable files (scripts/build_entry_signals.py, scripts/build_sop_plan.py, scripts/dividend_basis.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

92% identical to kanchi-dividend-sop — 8 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-sop/SKILL.md · 285 lines

How it starts

The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Kanchi Dividend Sop

Overview

Implement Kanchi's 5-step method as a deterministic workflow for US dividend investing. Prioritize safety and repeatability over aggressive yield chasing.

When to Use

Use this skill when the user needs:

  • Kanchi-style dividend stock selection adapted for US equities.
  • A repeatable screening and pullback-entry process instead of ad-hoc picks.
  • One-page underwriting memos with explicit invalidation conditions.
  • A handoff package for monitoring and tax/account-location workflows.

Prerequisites

API Key Setup

The entry signal script requires FMP API access:

export FMP_API_KEY=your_api_key_here

Input Sources

Prepare one of the following inputs before running the workflow:

  1. Output from skills/value-dividend-screener/scripts/screen_dividend_stocks.py.
  2. Output from skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth.py.
  3. User-provided ticker list (broker export or manual list).
Expected JSON Input Format

When using --input, provide JSON in one of these formats:

{
  "profile": "balanced",
  "candidates": [
    {"ticker": "JNJ", "bucket": "core"},
    {"ticker": "O", "bucket": "satellite"}
  ]
}

Or simplified:

{
  "tickers": ["JNJ", "PG", "KO"]
}

For deterministic artifact generation, provide tickers to:

python3 skills/kanchi-dividend-sop/scripts/build_sop_plan.py \
  --tickers "JNJ,PG,KO" \
  --output-dir reports/

For Step 5 entry timing artifacts. --yield-floor is mandatory — it is the Step-1 yield gate; without it every row fail-safes to STEP1-RECHECK (a row can never reach a PASS tier without Step 1). Pass --profile / --safety-bias for run_context, and --events-json for the Step 4b scan (absent ⇒ every row is treated as SKIPPED and a TRIGGERED name is capped to HOLD-REVIEW — never silently clean):

python3 skills/kanchi-dividend-sop/scripts/build_entry_signals.py \
  --tickers "JNJ,PG,KO" \
  --alpha-pp 0.5 \
  --yield-floor 3.0 \
  --profile balanced --safety-bias medium \
  --events-json reports/kanchi_events_2026-05-17.json \
  --output-dir reports/

Read the full file on GitHub · 285 lines

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. 12d ago First seen · 285 lines · 83 tokens per session scan A 5718999fd220

Subscribe to this mod's changes

kanchi-dividend-sop is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 83 tokens to every session and 3,210 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to kanchi-dividend-sop, differing in 8 lines, and is treated as a copy.

Related

Other skills, from other repositories

ib-pmcc-advisor

Analyze PMCC (Poor Man's Covered Call / diagonal spread) positions from IB portfolio. For each diagonal spread, reports short leg risk (delta, IV, assignment probability), daily P&L projections, top-3 roll candidates, and a side-by-side comparison table. Requires TWS or IB Gateway running locally.

staskh/trading_skills · 71 tokens

scanner-pmcc

Scan stocks for Poor Man's Covered Call (PMCC) suitability. Analyzes LEAPS and short call options for delta, liquidity, spread, IV, yield, trend direction, and earnings proximity. Use when user asks about PMCC candidates, diagonal spreads, or LEAPS strategies.

staskh/trading_skills · 63 tokens

ib-stop-loss

Downside stop-loss management for PMCC, naked LEAPS, and stock positions in IB. Computes stop prices, detects alerts, and places conditional combo orders. Dry-run by default. Requires TWS or IB Gateway running locally.

staskh/trading_skills · 50 tokens

ib-trailing-stop

Server-side trailing stop management for stocks and naked LEAPS in IB. Places native TRAIL orders that auto-ratchet the stop as price climbs. Dry-run by default. Requires TWS or IB Gateway running locally.

staskh/trading_skills · 49 tokens

stock_analyzer

A stock and market analysis skill that returns structured information about trends, prices, news, risks, catalysts, and possible trading plans.

EthanAlgoX/LLM-TradeBot · 27 tokens

ib-collar

Generate tactical collar strategy reports for protecting PMCC positions through earnings or high-risk events. Requires TWS or IB Gateway running locally.

staskh/trading_skills · 30 tokens