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Kanchi Dividend Sop

CommunityPopular
tradermonty
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.

Overview

Publishertradermonty
Repositoryclaude-trading-skills
Skill namekanchi-dividend-sop
Stars
2.8K
Forks
647
Bundled files
25
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 25 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tradermonty on GitHub. Read the source before you install it.

Installation

Install the Kanchi Dividend Sop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/kanchi-dividend-sop .claude/skills/kanchi-dividend-sop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kanchi Dividend Sop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kanchi Dividend Sop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kanchi Dividend Sop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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_rsi.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"}
  ]
}

Or simplified:

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

The optional value-dividend-screener and dividend-growth-pullback-screener handoffs use stocks[].symbol. Both build_sop_plan.py --input and build_entry_signals.py --input accept that shape directly, as well as the native candidates[].ticker and tickers[] shapes above.

For deterministic artifact generation, provide tickers to:

bash
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):

bash
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/

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.

2) Build the investable universe

Start with a quality-biased universe:

  • Core bucket: long dividend growth names (for example, Dividend Aristocrats style quality set).
  • Satellite bucket: higher-yield sectors (utilities, telecom, REITs) in a separate risk bucket.

Use explicit source priority for ticker collection:

  1. skills/value-dividend-screener/scripts/screen_dividend_stocks.py output (FMP/FINVIZ).
  2. skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth_rsi.py output.
  3. User-provided broker export or manual ticker list when APIs are unavailable.

Return a ticker list grouped by bucket before moving forward.

3) Apply Kanchi Step 1 (yield filter with trap flag)

Primary rule:

  • Step-1 yield = the regular forward yield = latest_declared_regular dividend × cadence-implied frequency / price (WS-1 dividend_basis.py). Never use profile.lastDividend / TTM — it lags the latest declared raise (defect D5) and silently bundles specials (D4).
  • Apply the profile floor (income-now 4.0% / balanced 3.0% / growth-first 1.5%) to the regular yield only.

Trap & freshness controls (machine-emitted by dividend_basis.py):

  • special_dividend_flag → exclude specials; report regular vs ttm yield.
  • variable_policy_flagFAIL (CALM-style; not an income base).
  • cut_flagFAIL; suspension_flagFAIL.
  • freeze_flagHOLD-REVIEW (income cash-cow exception decided in Step 8 synthesis only if safety is clean & unblocked).
  • Data Freshness Gate: if the regular yield is within ±0.20pp of the floor (floor_borderline) and the latest declared dividend is not confirmed from an authoritative source, emit STEP1-RECHECKnever a hard FAIL (this is the CFR D5 fix).

4) Apply Kanchi Step 2 (growth and safety) — sector-dispatched

Safety is sector-specific — a uniform GAAP/FCF triad mis-judges banks (FCF meaningless) and regulated utilities (FCF structurally negative). Use references/sector-step2-modules.md; the deterministic dispatch is scripts/payout_safety.py.

  • Always compute the payout triad: GAAP-EPS payout, Adjusted-EPS payout, FCF payout. The safety verdict uses Adjusted-EPS + FCF (consumer), or the sector module (bank / utility / insurer).
  • adjusted_eps_source = UNAVAILABLE ⇒ cap HOLD-REVIEW (fail-safe; never a silent PASS).
  • GAAP↔Adjusted EPS divergence > 25% ⇒ Step-4 one-off flag.
  • A merger completed within 4 quarters presumes GAAP EPS is distorted ⇒ force the adjusted path or HOLD-REVIEW (FITB/Comerica golden case).
  • Regulated utilities: negative FCF is not an auto-FAIL — judge on FFO/debt + allowed ROE + rate-case + equity-issuance risk.

When trend is mixed but not broken, classify as HOLD-REVIEW instead of hard reject.

5) Apply Kanchi Step 3 (valuation) with US sector mapping

Use references/valuation-and-one-off-checks.md and apply sector-specific valuation logic:

  • Financials: PER x PBR can remain primary.
  • REITs: use P/FFO or P/AFFO instead of plain P/E.
  • Asset-light sectors: combine forward P/E, P/FCF, and historical range.

Always report which valuation method was used for each ticker.

6) Apply Kanchi Step 4 (one-off event filter)

Reject or downgrade names where recent profits rely on one-time effects:

  • Asset sale gains, litigation settlement, tax effect spikes.
  • Margin spike unsupported by sales trend.
  • Repeated "one-time/non-recurring" adjustments.

Record one-line evidence for each FAIL to keep auditability.

6b) Apply Kanchi Step 4b (forward structural-event scan)

Step 4 is backward-looking; Step 4b catches pending/recent structural events (the MKC-Unilever miss, D3). For each surviving candidate, run a WebSearch + issuer-IR/SEC check using the source hierarchy: issuer IR → SEC filing (8-K/10-Q/10-K/proxy/S-4) → exchange/company deck → reputable wire → finance portals (secondary only). Record findings into a curated events JSON and pass it via build_entry_signals.py --events-json.

  • Only a major structural event caps the verdict to HOLD-REVIEW (tx > 10% mcap, share issuance > 10–20%, leverage +0.5x EBITDA, control/listing/HQ change, merger-of-equals / RMT / spin-off / large asset sale, dividend/rating/leverage-policy change, sector-specific materiality, or rolling-24m cumulative M&A > 15% mcap). Minor bolt-ons are a CAUTION note only.
  • Pessimistic cap: FAILED-DEGRADED / SKIPPED / NO_EVENT_FOUND on a Step-5 TRIGGERED name ⇒ HOLD-REVIEW + T1 BLOCKED. WebSearch unavailable (web app / offline) is treated the same — never a silent skip. CLEAN_CONFIRMED (primary source checked) is stronger than NO_EVENT_FOUND (search only).

7) Apply Kanchi Step 5 (buy on weakness with rules)

Set entry triggers mechanically:

  • Yield trigger: current yield above 5y average yield + alpha (default +0.5pp).
  • Valuation trigger: target multiple reached (P/E, P/FFO, or P/FCF).

Execution pattern:

  • Split orders: 40% -> 30% -> 30%.
  • Pre-order blockers: if a candidate has any unresolved pre_order_blockers[] (from WS-1/2/3 — variable/cut/suspension, adjusted-EPS-unavailable, GAAP/Adj divergence, bank credit, utility FFO/debt, event-scan failed/skipped, stale dividend, …) OR t1_blocked is true, the first tranche is blocked or downsized to a ≤20% tracking tranche — not 40%.
  • Sector cluster risk: when ≥ SECTOR_CLUSTER_WARN_COUNT same-sector names pass (e.g. many small banks share one macro beta), emit a portfolio-level CLUSTER-RISK warning.
  • Require one-sentence sanity check before each unblocked add: "thesis intact vs structural break".

8) Produce standardized outputs

Always produce:

  1. Screening table with the actionable verdict tier: CLEAN-PASS, PASS-CAUTION, CONDITIONAL-PASS, HOLD-REVIEW, STEP1-RECHECK, FAIL (synthesized by verdict.py from Step 1 + Step 2 + Step 4b + blockers). Include evidence per row.
  2. One-page stock memo (use references/stock-note-template.md) with the per-ticker provenance block (price/dividend/payout/event sources, unresolved_blockers, evidence_refs[]).
  3. Limit-order plan with split sizing, blocker gate, and invalidation.
  4. Top-level run_context (profile, yield_floor_pct, safety_bias, universe_source, excluded_asset_types) so a 3%-run result is never silently reused inside a 4%-run.

Output

Return and/or generate:

  1. SOP screening summary in markdown.
  2. Underwriting memo set based on references/stock-note-template.md.
  3. Optional plan artifact file generated by skills/kanchi-dividend-sop/scripts/build_sop_plan.py in reports/.
  4. Optional Step 5 entry-signal artifacts generated by skills/kanchi-dividend-sop/scripts/build_entry_signals.py in reports/.

Cadence

Use this minimum rhythm:

  • Weekly (15 min): check dividend and business-news changes only.
  • Monthly (30 min): rerun screening and refresh order levels.
  • Quarterly (60 min): deep safety review using latest filings/earnings.

Multi-Skill Handoff

Run this skill first, then hand off outputs:

  1. To kanchi-dividend-review-monitor for daily/weekly/quarterly anomaly detection.
  2. To kanchi-dividend-us-tax-accounting for account-location and tax classification planning.

Guardrails

  • Do not issue blind buy calls without Step 4, Step 4b and safety checks.
  • Do not treat high yield as value before validating coverage quality.
  • Use the regular forward yield for Step 1, never a special/TTM-inclusive figure; near-floor + unconfirmed ⇒ STEP1-RECHECK, not FAIL.
  • A failed/skipped event scan on a TRIGGERED name ⇒ HOLD-REVIEW + T1 blocked. Never silently skip Step 4b.
  • Keep assumptions explicit; adjusted_eps/data missing ⇒ fail-safe HOLD-REVIEW, never silent PASS.

Resources

  • scripts/thresholds.py: single source of truth for all SOP thresholds + SCHEMA_VERSION (downstream schema-evolution guard).
  • scripts/dividend_basis.py: WS-1 regular/special/variable/freeze/cut + Data Freshness Gate engine (pure, offline).
  • scripts/payout_safety.py: WS-2 sector-aware GAAP/Adjusted/FCF payout triad + completed-merger linkage.
  • scripts/event_scanner.py: WS-3 isolated forward/recent corporate-action scanner + materiality gate + pessimistic cap.
  • scripts/verdict.py: WS-5 actionable-tier synthesis + run_context + evidence_ref helpers.
  • scripts/build_entry_signals.py: orchestrator (Step 5 targets + WS-1/2/3/5 integration). Flags: --yield-floor, --events-json, --profile, --safety-bias, --universe-source.
  • scripts/build_sop_plan.py: deterministic SOP plan scaffold generator.
  • scripts/tests/test_golden_p0.py: P0 merge gate — end-to-end frozen verdicts for CALM/ORI/CMCSA/MKC/CFR/cut (run via scripts/run_all_tests.sh).
  • references/default-thresholds.md: human-readable threshold mirror.
  • references/sector-step2-modules.md: Step 2 safety indicators by sector.
  • references/valuation-and-one-off-checks.md: Step 3 valuation + Step 4 one-off.
  • references/stock-note-template.md: one-page memo + provenance block.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Kanchi Dividend Sop AI 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.

Why use Kanchi Dividend Sop on TypingMind?

Because you install it once and use it with any model. Kanchi Dividend Sop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kanchi Dividend Sop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-sop. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Kanchi Dividend Sop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kanchi Dividend Sop?

As many as you like. As long as a model supports skills, you can use Kanchi Dividend Sop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kanchi Dividend Sop AI skill free?

Yes. It is published on GitHub by tradermonty under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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