Agent skill

Kanchi Dividend Sop

by tradermonty in tradermonty/claude-trading-skills

Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure.

MITAuto-check passedBusiness, Finance & HR

Install Kanchi Dividend Sop

skills CLI
$ npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tradermonty/claude-trading-skills kanchi-dividend-sop --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/kanchi-dividend-sop .claude/skills/kanchi-dividend-sop && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
kanchi-dividend-sop
GitHub stars
3k
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
1,302 words
Files
26 (incl. scripts, references)
Skills in repo
74
Repo updated
First seen
Licence
MIT

At a glance

Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure.

  • Works in 8 steps: Define mandate before screening → Build the investable universe → Apply Kanchi Step 1 (yield filter with… → …
  • Users ask for かんち式配当投資
  • SKILL.md covers Overview, When to Use, Prerequisites and Workflow, plus 5 more sections
  • Runs Python scripts from its folder; calls python3; needs FMP_API_KEY

What it does

Kanchi Dividend Sop is an agent skill from tradermonty/claude-trading-skills. 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.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 29 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/default-thresholds.md` and `references/sector-step2-modules.md`).

It sits in Business, Finance & HR, covering Operations and SOPs. The repository describes itself as: Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development. The licence is MIT.

When your agent uses it

  • Users ask for かんち式配当投資
  • Dividend screening
  • Dividend growth quality checks
  • PERxPBR adaptation for US sectors

Example prompts

  • “/kanchi-dividend-sop”

Requirements

  • Python 3
  • A credential in FMP_API_KEY

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  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)
  7. Apply Kanchi Step 5 (buy on weakness with rules)
  8. Produce standardized outputs

What it can do on your machine

Read from SKILL.md and the folder at commit c8d58f0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 10 files in scripts/ (Python, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • FMP_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Kanchi Dividend Sop loads about 3.1k tokens when it runs, and up to ~5.9k if it reads all its reference files. Until then it costs about 94 tokens; SKILL.md has 1,302 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from tradermonty/claude-trading-skills at commit c8d58f0, republished under its MIT licence (© tradermonty). 1,302 words, ~3,103 tokens.

Download SKILL.mdSave it as .claude/skills/kanchi-dividend-sop/SKILL.md (or your agent's skills folder). This skill also uses 25 other files; get the full folder from GitHub.
name
kanchi-dividend-sop
description
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.

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_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).
Show full SKILL.md (462 more words)Show less
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.

© tradermonty, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 25 other files (scripts, references) in skills/kanchi-dividend-sop of tradermonty/claude-trading-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/default-thresholds.md
  • references/sector-step2-modules.md
  • references/stock-note-template.md
  • references/valuation-and-one-off-checks.md
  • requirements.txt
  • scripts/build_entry_signals.py
  • scripts/build_sop_plan.py
  • scripts/dividend_basis.py
  • scripts/event_scanner.py
  • scripts/payout_safety.py
  • scripts/tests/conftest.py
  • scripts/tests/test_build_entry_signals.py
  • scripts/tests/test_build_sop_plan.py
  • scripts/tests/test_dividend_basis.py
  • scripts/tests/test_event_scanner.py
  • … and 9 more

Open the folder on GitHubat commit c8d58f0

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in tradermonty/claude-trading-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Kanchi Dividend Sop

What does Kanchi Dividend Sop do?

Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Kanchi Dividend Sop is an agent skill from tradermonty/claude-trading-skills. Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure.

When should I use Kanchi Dividend Sop?

Kanchi Dividend Sop fits situations like: users ask for かんち式配当投資; dividend screening; dividend growth quality checks; PERxPBR adaptation for US sectors.

How do I install Kanchi Dividend Sop in Claude Code?

Run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a claude-code`. Or copy the skill folder (skills/kanchi-dividend-sop in tradermonty/claude-trading-skills) into .claude/skills/kanchi-dividend-sop in your project. Claude Code loads it when a task matches its description.

How do I install Kanchi Dividend Sop in Codex?

Run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a codex`. Or copy the skill folder (skills/kanchi-dividend-sop in tradermonty/claude-trading-skills) into .agents/skills/kanchi-dividend-sop in your project. Codex loads it when a task matches its description.

Can I use Kanchi Dividend Sop in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kanchi-dividend-sop, .gemini/skills/kanchi-dividend-sop, .github/skills/kanchi-dividend-sop and .opencode/skills/kanchi-dividend-sop in your project.

What does Kanchi Dividend Sop need to run?

Going by SKILL.md and its folder, Kanchi Dividend Sop needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named FMP_API_KEY. Our summary lists: Python 3; A credential in FMP_API_KEY.

Does Kanchi Dividend Sop access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Kanchi Dividend Sop safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Kanchi Dividend Sop use?

Kanchi Dividend Sop is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Kanchi Dividend Sop use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.8k tokens, read only when the agent opens those files.

What are the alternatives to Kanchi Dividend Sop?

Skills that share tags, products or a category with Kanchi Dividend Sop: Cc Sdd New Agent (gotalab/cc-sdd, 3.7k stars), DBS Business Toolkit Entry (dontbesilent2025/dbskill, 11k stars), Agent Sop Author (strands-agents/agent-sop, 1.2k stars) and Diffusion Narrative Denouncing (canwhite/Krebs, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kanchi Dividend Sop?

tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,982 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 11, 2026.

Source: tradermonty/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.