Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mphinance/alpha-skills --skill kanchi-dividend-sopgit clone --depth 1 https://github.com/mphinance/alpha-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mphinance/alpha-skills/kanchi-dividend-sop)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/kanchi-dividend-sop"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/kanchi-dividend-sop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mphinance/alpha-skills/kanchi-dividend-sop"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/kanchi-dividend-sop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.01689 |
| Opus 5.5 | $0.00033 | $0.00676 |
| Sonnet 5.5 | $0.00017 | $0.00338 |
| Haiku 4.5 | $0.00008 | $0.00169 |
Grade A, and why
kanchi-dividend-sop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 108 B
- README.md 6.9 KB
- references/default-thresholds.md 2.8 KB
- references/stock-note-template.md 1.1 KB
- references/valuation-and-one-off-checks.md 1.6 KB
- scripts/build_entry_signals.py 13 KB runs code
- scripts/build_sop_plan.py 4.5 KB runs code
- scripts/tests/conftest.py 149 B runs code
- scripts/tests/test_build_entry_signals.py 2.3 KB runs code
- scripts/tests/test_build_sop_plan.py 1.5 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 203 lines · 83 tokens per session scan A 2496c0307626
kanchi-dividend-sop is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 83 tokens to every session and 1,689 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-14.
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