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:

Output from

skills/value-dividend-screener/scripts/screen_dividend_stocks.py

.

Output from

skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth.py

.

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.

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:

skills/value-dividend-screener/scripts/screen_dividend_stocks.py

output (FMP/FINVIZ).

skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth_rsi.py

output.

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:

forward_dividend_yield >= 3.5%

Trap controls:

Flag extreme yield (

>= 8%

) as

deep-dive-required

.

Flag sudden jump in payout as potential special dividend artifact.

Output:

PASS

or

FAIL

per ticker.

deep-dive-required

flag for potential yield traps.

4) Apply Kanchi Step 2 (growth and safety)

Require:

Revenue and EPS trend positive on multi-year horizon.

Dividend trend non-declining over the review period.

Add safety checks:

Payout ratio and FCF payout ratio in reasonable range.

Debt burden and interest coverage not deteriorating.

When trend is mixed but not broken, classify as

HOLD-FOR-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.

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%

.

Require one-sentence sanity check before each add: "thesis intact vs structural break".

8) Produce standardized outputs

Always produce three artifacts:

Screening table (

PASS

,

HOLD-FOR-REVIEW

,

FAIL

with evidence).

One-page stock memo (use

references/stock-note-template.md

).

Limit-order plan with split sizing and invalidation condition.

Output

Return and/or generate:

SOP screening summary in markdown.

Underwriting memo set based on

references/stock-note-template.md

.

Optional plan artifact file generated by

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

in

reports/

.

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:

To

kanchi-dividend-review-monitor

for daily/weekly/quarterly anomaly detection.

To

kanchi-dividend-us-tax-accounting

for account-location and tax classification planning.

Guardrails

Do not issue blind buy calls without Step 4 and safety checks.

Do not treat high yield as value before validating coverage quality.

Keep assumptions explicit when data is missing.

Resources

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

deterministic SOP plan generator.

skills/kanchi-dividend-sop/scripts/tests/test_build_sop_plan.py

tests for plan generation.

skills/kanchi-dividend-sop/scripts/build_entry_signals.py

Step 5 target-buy calculator (

5y avg yield + alpha

).

skills/kanchi-dividend-sop/scripts/tests/test_build_entry_signals.py

tests for signal calculations.

references/default-thresholds.md

baseline thresholds and profile tuning.

references/valuation-and-one-off-checks.md

sector valuation map and one-off checklist.

references/stock-note-template.md

one-page memo template for each candidate.