kanchi-dividend-sop
Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Use when users ask for かんち式配当投資, dividend screening, dividend growth quality checks, PERxPBR adaptation for US sectors, pullback limit-order planning, or one-page stock memo creation. Covers screening, deep dive, entry planning, and post-purchase monitoring cadence.
npx skills add BaggaT236/AI-Trading-Skills --skill kanchi-dividend-sop --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: ```bash 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: ```json { "profile": "balanced", "candidates": [ {"ticker": "JNJ", "bucket": "core"}, {"ticker": "O", "bucket": "satellite"} ] } ```
- Overview
- When to Use
- Prerequisites
- API Key Setup
- Input Sources
- Workflow
- 1) Define mandate before screening
- 2) Build the investable universe
- 3) Apply Kanchi Step 1 (yield filter with trap flag)
- 4) Apply Kanchi Step 2 (growth and safety) — sector-dispatched
- 5) Apply Kanchi Step 3 (valuation) with US sector mapping
- 6) Apply Kanchi Step 4 (one-off event filter)
- 6b) Apply Kanchi Step 4b (forward structural-event scan)
- 7) Apply Kanchi Step 5 (buy on weakness with rules)
export FMP_API_KEY=your_api_key_here python3 skills/kanchi-dividend-sop/scripts/build_sop_plan.py \ python3 skills/kanchi-dividend-sop/scripts/build_entry_signals.py \
What does the kanchi-dividend-sop skill do?
Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Use when users ask for かんち式配当投資, dividend screening, dividend growth quality checks, PERxPBR adaptation for US sectors, pullback limit-order planning, or one-page stock memo creation. Covers screening, deep dive, entry planning, and post-purchase monitoring cadence.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill kanchi-dividend-sop --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.