القائمة
CSV transaction parsing, categorization, and tax deduction identification for financial planning
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/dapperdivers/roundtable-arsenal --skill tax-prepيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Synthesize domain knight summaries into a comprehensive Daily Briefing for Derek's Obsidian vault. Your output IS the briefing file — write the full document, not a summary of it.
Procedure for the morning-briefing verify_claims step: re-run the check behind every mechanically-verifiable claim in the domain summaries, build the authoritative knight-liveness map from vault file mtimes, update the open-items ledgers, and emit one JSON verification report that the synthesizer treats as the only source of numbers.
Protocol for the per-domain open-items ledger at /vault/Briefings/Ledger/<domain>.json. Replaces prose memory as the carry-forward mechanism for tracked items: Day-N counters are computed from first_seen, items are re-verified before being carried, and resolutions are recorded machine-readably so they can never be silently lost.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | tax-prep |
| description | CSV transaction parsing, categorization, and tax deduction identification for financial planning |
Parse bank and credit card CSV exports, categorize transactions, and flag tax-relevant items.
Most bank exports follow common formats. Detect columns automatically:
| Bank Style | Date Column | Description | Amount | Notes |
|---|---|---|---|---|
| Chase | "Transaction Date" | "Description" | "Amount" | Negative = charge |
| Amex | "Date" | "Description" | "Amount" | Positive = charge |
| Generic | First date-like col | Longest string col | Numeric col | Varies |
Assign each transaction to exactly one category:
Use substring matching on description (case-insensitive):
# Housing
mortgage, rent, hoa, property tax, homeowner
# Utilities
electric, power, gas company, water, internet, comcast, att, verizon, tmobile
# Transportation
shell, exxon, chevron, bp, uber, lyft, parking, toll
# Food
grocery, kroger, heb, walmart grocer, whole foods, restaurant, mcdonald, starbucks, doordash, grubhub
# Medical
pharmacy, cvs, walgreens, doctor, hospital, dental, optom, therapy, labcorp
# Business:Software
aws, azure, google cloud, digitalocean, github, cloudflare, namecheap
# Charitable
donation, charity, united way, red cross, church, tithe
Flag transactions with tax relevance:
| Flag | Criteria | Tax Form |
|---|---|---|
deductible | Charitable donations, state/local taxes | Schedule A |
business_expense | Any Business:* category | Schedule C |
hsa | HSA contributions or qualified medical | Form 8889 |
education | Tuition, student loan interest | Form 8863/1098-E |
home_office | Internet, utilities (pro-rated if WFH) | Form 8829 |
estimated_tax | IRS/state tax payments | Form 1040-ES |
python3 scripts/categorize.py transactions.csv --output categorized.json
python3 scripts/categorize.py transactions.csv --output categorized.json --year 2025
{
"summary": {
"total_income": 85000.00,
"total_expenses": 42000.00,
"by_category": {"Food": -5200.00, "Housing": -18000.00},
"tax_flags": {"deductible": -3200.00, "business_expense": -8500.00}
},
"transactions": [
{
"date": "2025-01-15",
"description": "STARBUCKS #1234",
"amount": -5.75,
"category": "Food",
"tax_flags"
uncategorized list — add keywords or manually assignbusiness_expense flags — ensure legitimacydeductible totals with tax softwarefinance/paperless-ops)--split flag to separate by cardholder if supported