Kanchi Dividend Us Tax Accounting logo

Kanchi Dividend Us Tax Accounting

CommunityPopular
tradermonty
kanchi-dividend-us-tax-accounting

Provide US dividend tax and account-location workflow for Kanchi-style income portfolios. Use when users ask about qualified vs ordinary dividends, 1099-DIV interpretation, REIT/BDC distribution treatment, holding-period checks, or taxable-vs-IRA account placement decisions for dividend assets.

Overview

Publishertradermonty
Repositoryclaude-trading-skills
Skill namekanchi-dividend-us-tax-accounting
Stars
2.8K
Forks
647
Bundled files
9
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tradermonty on GitHub. Read the source before you install it.

Installation

Install the Kanchi Dividend Us Tax Accounting AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/kanchi-dividend-us-tax-accounting .claude/skills/kanchi-dividend-us-tax-accounting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kanchi Dividend Us Tax Accounting in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kanchi Dividend Us Tax Accounting on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kanchi Dividend Us Tax Accounting is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

  • ticker
  • instrument_type
  • account_type
  • hold_days_in_window (if available)

Use the exact JSON contract and examples in references/input-schema.md.

For deterministic output artifacts, provide JSON input and run:

bash
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.

If data is incomplete, mark status as ASSUMPTION-REQUIRED.

3) Map to reporting fields

Map planning assumptions to expected tax-form buckets:

  • Ordinary dividend total.
  • Qualified dividend subset.
  • REIT-related components when reported separately.

Use form terminology consistently so year-end reconciliation is straightforward.

4) Build account-location recommendation

Use references/account-location-matrix.md to place assets by tax profile:

  • Taxable account for holdings likely to remain qualified-focused.
  • Tax-advantaged account for higher ordinary-income style distributions.

When constraints conflict (liquidity, strategy, concentration), explain the tradeoff explicitly.

5) Produce annual planning memo

Use references/annual-tax-memo-template.md and include:

  • Assumptions used.
  • Distribution classification summary.
  • Placement actions taken.
  • Open items for CPA/tax-advisor review.

Output

Always output:

  1. Holding-level distribution classification table.
  2. Account-location recommendation table with rationale.
  3. Open-risk checklist for unresolved tax assumptions.
  4. Optional generated artifacts from skills/kanchi-dividend-us-tax-accounting/scripts/build_tax_planning_sheet.py.

Cadence

Use this minimum rhythm:

  • Annually (60 min): full tax planning memo with account-location review.
  • Quarterly (15 min): refresh holding-period status for recent acquisitions.
  • Ad-hoc: rerun after material position changes, REIT/BDC additions, or triggered reviews from kanchi-dividend-review-monitor.

Multi-Skill Handoff

  • Receive candidate and holding list from kanchi-dividend-sop.
  • Receive risk-event context (WARN/REVIEW) from kanchi-dividend-review-monitor.
  • Return account-location constraints back to kanchi-dividend-sop before new entries.

Resources

  • skills/kanchi-dividend-us-tax-accounting/scripts/build_tax_planning_sheet.py: tax planning sheet generator.
  • skills/kanchi-dividend-us-tax-accounting/scripts/tests/test_build_tax_planning_sheet.py: tests for tax planning outputs.
  • references/qualified-dividend-checklist.md: classification and holding-period checks.
  • references/account-location-matrix.md: placement matrix by account type and instrument.
  • references/annual-tax-memo-template.md: reusable memo structure.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Kanchi Dividend Us Tax Accounting AI skill do?

Provide US dividend tax and account-location workflow for Kanchi-style income portfolios. Use when users ask about qualified vs ordinary dividends, 1099-DIV interpretation, REIT/BDC distribution treatment, holding-period checks, or taxable-vs-IRA account placement decisions for dividend assets.

Why use Kanchi Dividend Us Tax Accounting on TypingMind?

Because you install it once and use it with any model. Kanchi Dividend Us Tax Accounting is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kanchi Dividend Us Tax Accounting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-us-tax-accounting. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Kanchi Dividend Us Tax Accounting?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kanchi Dividend Us Tax Accounting?

As many as you like. As long as a model supports skills, you can use Kanchi Dividend Us Tax Accounting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kanchi Dividend Us Tax Accounting AI skill free?

Yes. It is published on GitHub by tradermonty under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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