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Skills/tradermonty/claude-trading-skills/kanchi-dividend-sop
kanchi-dividend-sop logo

kanchi-dividend-sop

tradermonty/claude-trading-skills
901 installs3K stars
Run it on Hostinger, 20% off →Your friend gets 20% off too, using this linkFree API →|View on GitHub|Create your own skill →

Installation

npx skills add https://github.com/tradermonty/claude-trading-skills --skill kanchi-dividend-sop

Summary

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.

SKILL.md

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_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:

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

Or simplified:

{
  "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:

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/

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_flag → FAIL (CALM-style; not an income base).
  • cut_flag → FAIL; suspension_flag → FAIL.
  • freeze_flag → HOLD-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-RECHECK — never 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.

  1. 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[]).

  1. Limit-order plan with split sizing, blocker gate, and invalidation.
  2. 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.

  1. Optional plan artifact file generated by

skills/kanchi-dividend-sop/scripts/build_sop_plan.py in reports/.

  1. 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.

Score

0–100
65/ 100

Grade

C

Popularity17/30

901 installs — growing adoption. Source repo has 2,591 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

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Kanchi Dividend Sop FAQ

How do I install the Kanchi Dividend Sop skill?

Run “npx skills add https://github.com/tradermonty/claude-trading-skills --skill kanchi-dividend-sop” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

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. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Kanchi Dividend Sop skill free?

Yes. Kanchi Dividend Sop is a free, open-source skill published from tradermonty/claude-trading-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Kanchi Dividend Sop work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Kanchi Dividend Sop works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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