Skip to content
All Skills

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.

Business, Marketing & Sales|v1|Updated 7/14/2026|GitHub source
MCP get_skill({ skillId: "kanchi-dividend-sop-9873cf55" })

Use this skill with your agent

Create a free account and connect via MCP

Get Started Free
# 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"}
  ]
}
```

Or simplified:

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

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_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.
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.
#work-life#productivity#finance#trading#investing#financial#analysispythonfmp-api

Related Skills

More skills in Business, Marketing & Sales

Ab Testing

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.

#work-life#productivityMIT

Ab Test Setup

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.

#work-life#productivityMIT

Ab Test Setup

Ab Test Setup linked from Corey Haines marketing skills, with the upstream skill instructions available on GitHub.

#work-life#productivityMIT

Ab Test Store Listing

When the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion rate optimization", "CPP", or "custom product pages". For screenshot design, see screenshot-optimization. For metadata optimization, see metadata-optimization.

#work-life#productivityMIT

Account Research

Research a company or person and get actionable sales intel. Works standalone with web search, supercharged when you connect enrichment tools or your CRM. Trigger with "research [company]", "look up [person]", "intel on [prospect]", "who is [name] at [company]", or "tell me about [company]".

#work-life#productivityApache-2.0

Account Research

Research a company using Common Room data. Triggers on 'research [company]', 'tell me about [domain]', 'pull up signals for [account]', 'what's going on with [company]', or any account-level question.

#work-life#productivityApache-2.0

Explore Other Categories

Skills from other categories with shared topics

Backtest Expert

Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

Data, AI & Research#work-life#productivity

Breadth Chart Analyst

This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. Also works WITHOUT chart images by fetching CSV data directly from public sources. All analysis and output are conducted in English.

Data, AI & Research#work-life#productivity

Breakout Trade Planner

Generate Minervini-style breakout trade plans from VCP screener output with worst-case risk calculation, portfolio heat management, and Alpaca-compatible order templates (stop-limit bracket for pre-placement, limit bracket for post-confirmation). Use when user has VCP screener results and wants actionable trade plans with entry/stop/target levels and position sizing.

Data, AI & Research#work-life#productivity