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-us-tax-accountinggit 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-us-tax-accounting)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/kanchi-dividend-us-tax-accounting"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/kanchi-dividend-us-tax-accounting/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-us-tax-accounting"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/kanchi-dividend-us-tax-accounting.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 | $0.00065 | $0.00987 |
| Opus 5.5 | $0.00026 | $0.00395 |
| Sonnet 5.5 | $0.00013 | $0.00197 |
| Haiku 4.5 | $0.00006 | $0.00099 |
Grade A, and why
kanchi-dividend-us-tax-accounting 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.
This is a copy
95% identical to kanchi-dividend-us-tax-accounting — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kanchi Dividend Us Tax Accounting
Overview
Apply a practical US-tax workflow for dividend investors while keeping decisions auditable. Focus on account placement and classification, not legal/tax advice replacement.
When to Use
Use this skill when the user needs:
- US dividend tax classification planning (qualified vs ordinary assumptions).
- Holding-period checks before year-end tax planning.
- Account-location decisions for stock/REIT/BDC/MLP income holdings.
- A standardized annual dividend tax memo format.
Prerequisites
Prepare holding-level inputs:
tickerinstrument_typeaccount_typehold_days_in_window(if available)
Expected JSON Input Format
{
"holdings": [
{
"ticker": "JNJ",
"instrument_type": "stock",
"account_type": "taxable",
"security_type": "common",
"hold_days_in_window": 75
},
{
"ticker": "O",
"instrument_type": "reit",
"account_type": "ira",
"hold_days_in_window": 100
}
]
}
For deterministic output artifacts, provide JSON input and run:
python3 skills/kanchi-dividend-us-tax-accounting/scripts/build_tax_planning_sheet.py \
--input /path/to/tax_input.json \
--output-dir reports/
Guardrails
Always state this clearly: tax outcomes depend on individual facts and jurisdiction. Treat this skill as planning support, then escalate final filing decisions to a tax professional.
Workflow
1) Classify each distribution stream
For each holding, classify expected cash flow into:
- Potential qualified dividend.
- Ordinary dividend/non-qualified distribution.
- REIT/BDC-specific distribution components where applicable.
Use references/qualified-dividend-checklist.md
for holding-period and classification checks.
2) Validate holding-period eligibility assumptions
For potential qualified treatment:
- Check ex-dividend date windows.
- Check required minimum holding days in the measurement window.
- Flag positions at risk of failing holding-period requirement.
What ships with it
8 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 136 B
- README.md 4.6 KB
- references/account-location-matrix.md 1.3 KB
- references/annual-tax-memo-template.md 843 B
- references/qualified-dividend-checklist.md 1.6 KB
- scripts/build_tax_planning_sheet.py 5.9 KB runs code
- scripts/tests/conftest.py 154 B runs code
- scripts/tests/test_build_tax_planning_sheet.py 2.1 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 · 140 lines · 65 tokens per session scan A 026f8af7872a
kanchi-dividend-us-tax-accounting is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 65 tokens to every session and 987 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to kanchi-dividend-us-tax-accounting, differing in 22 lines, and is treated as a copy.
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