Skill: chipmunkrpa/usa-tax-return-review-1040 (version 1.0.0)

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


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