⚡

// Skill profile

Form 1040 Review

Name: Form 1040 Review
Author: chipmunkrpa

name: form-1040-review

by chipmunkrpa · published 2026-03-22

数据处理自动化任务

Total installs

Stars

★ 0

Last updated

2026-03

// Install command

$ claw add gh:chipmunkrpa/chipmunkrpa-usa-tax-return-review-1040

View on GitHub

// Full documentation

---

name: form-1040-review

description: Review U.S. individual income tax returns (Form 1040/1040-SR) for the most recent tax year, compare major return items against current-year tax rules, check consistency across historical returns when multiple years are provided, generate a standalone DOCX risk register, and estimate audit likelihood from return content. Use when tasks involve 1040 compliance review, multi-year consistency analysis, tax-law validation, or audit-risk assessment.

---

# Form 1040 Review

Overview

Run a structured review of normalized Form 1040 data for the latest tax year in the provided set. Produce three artifacts: a detailed findings JSON file, a markdown summary, and a separate DOCX risk report listing major items and related risks.

Quick Start

1. Prepare normalized input JSON using [references/input_schema.json](references/input_schema.json).

2. Confirm current-law parameters in [references/current_tax_law_2025.json](references/current_tax_law_2025.json) before use.

3. Run:

python scripts/review_1040.py --input <normalized_returns.json> --output-dir output/form-1040-review

4. Review outputs:

`review_summary.md`

`review_findings.json`

`form-1040-risk-report.docx`

Workflow

1. Identify the current return

Select the highest `tax_year` in the input as the current return.

Treat all prior years as historical comparison returns.

2. Run current-year law checks

Validate internal arithmetic and line-to-line relationships.

Compare major current-year items to law parameters:

Standard deduction by filing status and age/blind additions.

Regular-rate tax computation when no preferential income is present.

Child Tax Credit and ACTC limits.

Self-employment tax and Additional Medicare tax thresholds.

3. Run multi-year consistency checks

Compare current return against the most recent prior year.

Flag large year-over-year movement in wages, AGI, taxable income, credits, payments, and refund/amount owed.

Flag filing-status and dependent-count shifts for explanation.

4. Produce risk outputs

Generate a structured findings file (`review_findings.json`).

Generate a human-readable summary (`review_summary.md`).

Generate a standalone DOCX risk register (`form-1040-risk-report.docx`) that lists each major item, severity, observations, and recommended documentation.

Produce an audit-likelihood estimate based on weighted findings and return complexity.

Inputs

Use the normalized schema in [references/input_schema.json](references/input_schema.json). At minimum, include:

`tax_year`

`filing_status`

`major_items` for core 1040 lines (AGI, deduction, taxable income, tax, payments, refund/amount owed)

Use [references/major_items_reference.md](references/major_items_reference.md) for canonical key mapping.

Law Source Discipline

Update [references/current_tax_law_2025.json](references/current_tax_law_2025.json) when the filing year changes or IRS issues revisions.

Use only official IRS/SSA sources for numeric thresholds.

If law data is older than the analyzed return year, flag the result as stale and require manual update before final sign-off.

Script

`scripts/review_1040.py` performs:

Current-year arithmetic and law checks.

Prior-year consistency checks.

Weighted audit-risk scoring.

DOCX risk report generation with `python-docx`.

If `python-docx` is missing, install it:

python -m pip install --user python-docx

Output Interpretation

Treat findings as risk signals, not final legal determinations.

Require CPA/EA review for filing decisions.

Present audit likelihood as a heuristic estimate derived from return patterns and detected issues, not a guarantee.

Example Command

python scripts/review_1040.py \
  --input references/example_returns.json \
  --output-dir output/form-1040-review

// Comments

// Related skills

More tools from the same signal band

Order food/drinks (点餐) on an Android device paired as an OpenClaw node. Uses in-app menu and cart; add goods, view cart, submit order (demo, no real payment).

Sign plugins, rotate agent credentials without losing identity, and publicly attest to plugin behavior with verifiable claims and authenticated transfers.

The philosophical layer for AI agents. Maps behavior to Spinoza's 48 affects, calculates persistence scores, and generates geometric self-reports. Give your...

日历管理数据处理

1 installs★ 0