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 BaggaT236/AI-Trading-Skills --skill kanchi-dividend-us-tax-accountinggit clone --depth 1 https://github.com/BaggaT236/AI-Trading-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/baggat236/ai-trading-skills/kanchi-dividend-us-tax-accounting)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/kanchi-dividend-us-tax-accounting"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-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/baggat236/ai-trading-skills/kanchi-dividend-us-tax-accounting"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-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 | $0.00032 | $0.00494 |
| Sonnet 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 12d 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
7 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
- references/account-location-matrix.md 1.3 KB
- references/annual-tax-memo-template.md 843 B
- references/qualified-dividend-checklist.md 2.2 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.
- 12d 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 BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 65 tokens to every session and 987 once invoked, about $0.0003 per session on Opus 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.
Other skills, from other repositories
ib-pmcc-advisor
Analyze PMCC (Poor Man's Covered Call / diagonal spread) positions from IB portfolio. For each diagonal spread, reports short leg risk (delta, IV, assignment probability), daily P&L projections, top-3 roll candidates, and a side-by-side comparison table. Requires TWS or IB Gateway running locally.
scanner-pmcc
Scan stocks for Poor Man's Covered Call (PMCC) suitability. Analyzes LEAPS and short call options for delta, liquidity, spread, IV, yield, trend direction, and earnings proximity. Use when user asks about PMCC candidates, diagonal spreads, or LEAPS strategies.
ib-stop-loss
Downside stop-loss management for PMCC, naked LEAPS, and stock positions in IB. Computes stop prices, detects alerts, and places conditional combo orders. Dry-run by default. Requires TWS or IB Gateway running locally.
ib-trailing-stop
Server-side trailing stop management for stocks and naked LEAPS in IB. Places native TRAIL orders that auto-ratchet the stop as price climbs. Dry-run by default. Requires TWS or IB Gateway running locally.
stock_analyzer
A stock and market analysis skill that returns structured information about trends, prices, news, risks, catalysts, and possible trading plans.
ib-collar
Generate tactical collar strategy reports for protecting PMCC positions through earnings or high-risk events. Requires TWS or IB Gateway running locally.