kanchi-dividend-sop

kanchi-dividend-sop is a skill for Claude Code, Codex from mphinance/alpha-skills. It costs 83 tokens per session (1,689 once invoked), scanned A, original, MIT.

A repeatable process for selecting US dividend stocks, checking their quality, and planning purchases during pullbacks. A pullback is a temporary price decline within a broader investment plan.

In plain words
What is it for?
It helps screen dividend stocks, review dividend growth and valuation, plan limit orders for pullbacks, and create one-page stock memos for monitoring and tax-account workflows.
Why use it?
It replaces improvised stock picking with defined screening, review, entry planning, and conditions that would invalidate the investment idea.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the quant-skills plugin — 60 skills shipped together

Good fit It helps screen dividend stocks, review dividend growth and valuation, plan limit orders for pullbacks, and create one-page stock memos for monitoring and tax-account workflows.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mphinance/alpha-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 mphinance/alpha-skills --skill kanchi-dividend-sop
Clone the repo
git clone --depth 1 https://github.com/mphinance/alpha-skills

Made for: Claude Code, Codex.

Or install quant-skills, the plugin that ships this one along with the rest of its 60 skills.

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/mphinance/alpha-skills/kanchi-dividend-sop/github.svg)](https://agentmods.dev/skills/mphinance/alpha-skills/kanchi-dividend-sop)
Your own site
<a href="https://agentmods.dev/skills/mphinance/alpha-skills/kanchi-dividend-sop"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-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/mphinance/alpha-skills/kanchi-dividend-sop"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-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 1,689 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 original No closer match found 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 $0.01689
Opus 5.5 $0.00033 $0.00676
Sonnet 5.5 $0.00017 $0.00338
Haiku 4.5 $0.00008 $0.00169

Measured 1mo ago against content hash 2496c0307626, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo ago.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/build_entry_signals.py, scripts/build_sop_plan.py, scripts/tests/conftest.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.

plugins/quant-skills/skills/kanchi-dividend-sop/SKILL.md · 203 lines

How it starts

The opening of the file, as written. The whole thing — 203 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:

python3 skills/kanchi-dividend-sop/scripts/build_entry_signals.py \
  --tickers "JNJ,PG,KO" \
  --alpha-pp 0.5 \
  --output-dir reports/

Workflow

1) Define mandate before screening

Collect and lock the parameters first:

  • Objective: current cash income vs dividend growth.
  • Max positions and position-size cap.
  • Allowed instruments: stock only, or include REIT/BDC/ETF.
  • Preferred account type context: taxable vs IRA-like accounts.

Load references/default-thresholds.md and apply baseline settings unless the user overrides.

Read the full file on GitHub · 203 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. 1mo ago First seen · 203 lines · 83 tokens per session scan A 2496c0307626

Subscribe to this mod's changes

kanchi-dividend-sop is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 83 tokens to every session and 1,689 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-14.

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