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Record and query income/expense transactions with optional location. Use for: 가계부, 지출, 수입, 얼마 썼어, 이번 달 지출, 카드값, 식비, expense logging, spending history, 어디서 썼어, 영수증, receipt OCR, 주소, 이미지
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | python3 finance/scripts/finance.py |
| display_name | 가계부 |
| description | Record and query income/expense transactions with optional location. Use for: 가계부, 지출, 수입, 얼마 썼어, 이번 달 지출, 카드값, 식비, expense logging, spending history, 어디서 썼어, 영수증, receipt OCR, 주소, 이미지 |
| version | 1.1.0 |
| emoji | 💵 |
| category | finance |
| enabled_by_default | true |
| requires_api_key | false |
| platforms | web, telegram, api |
| uses_provider | false |
| requires | {"bins":["python3 finance/scripts/finance.py"]} |
| allowed-tools | ["Bash","exec"] |
| triggers | {"keywords":["가계부","지출","수입","소비","얼마 썼어","이번 달 지출","거래 내역","월급","식비","교통비","카드값","어디서","영수증","receipt","finance","expense","income","transaction","spending","salary"],"when_to_use":["User records an expense or income transaction","User asks how much they have spent in a category or time period","User wants to view their transaction history","User says \"커피 5000원 썼어\" or \"이번 달 식비 얼마야\"","User mentions a place/location alongside spending (\"스타벅스 강남점에서 6500원\")","User sends a receipt image for OCR processing","User sends a photo of a storefront, menu, or location sign","User provides an address string in their message"],"not_for":["Setting spending budgets (use budget skill)","Investment or stock tracking"]} |
Use python3 finance/scripts/finance.py to record income/expenses and query monthly totals.
Always pass --user-id {user_id} (the current user's ID from the system prompt).
⚠️ REQUIRED: Always pass
--textwith the EXACT original user message — this is how the script finds the location (store/place name). If you omit--text, no location will be saved even if the user mentioned one.
python3 finance/scripts/finance.py --user-id {user_id} save \
--amount {amount} --category {category} --description "{description}" \
--text "{exact user message}"
Wrong (location will be lost):
# ❌ missing --text
python3 finance/scripts/finance.py --user-id {id} save --amount -12000 --category 식비 --description "양재 나주곰탕"
Correct:
# ✅ --text always included
python3 finance/scripts/finance.py --user-id {id} save --amount -12000 --category 식비 --description "양재 나주곰탕" \
--text "양재 나주곰탕 가게에서 나주곰탕 11000원 사용"
Optionally override location explicitly — choose the right flag based on what you have:
# --address: structured address string → uses Naver Geocoding API (도로명/지번주소)
python3 finance/scripts/finance.py --user-id {user_id} save \
--amount {amount} --category {category} --description "{description}" \
--address "서울 강남구 테헤란로 101"
# --location-name: store/place name → uses Naver Local Search
python3 finance/scripts/finance.py --user-id {user_id} save \
--amount {amount} --category {category} --description "{description}" \
--location-name "스타벅스 강남점"
# --lat + --lng: exact GPS coordinates (e.g. from image EXIF)
python3 finance/scripts/finance.py --user-id {user_id} save \
--amount {amount} --category {category} --description "{description}" \
--lat 37.5012 --lng 127.0396 --location-name "{optional display name}"
Location flag selection guide:
| Input type | Flag to use | Example |
|---|---|---|
| Structured address (도로명/지번) | --address | --address "서울 마포구 와우산로 21" |
| Store/place name | --location-name | --location-name "올리브영 홍대점" |
| GPS coords from image EXIF | --lat + --lng | --lat 37.5012 --lng 127.0396 |
| Nothing explicit | (omit) | script auto-extracts from --text |
Amount rules:
-120003000000Categories: Infer from context in any language:
| User intent | Category |
|---|---|
| Food / dining / meals | 식비 |
| Transport / commute / fuel | 교통 |
| Shopping / retail | 쇼핑 |
| Entertainment / culture | 문화 |
| Medical / health | 의료 |
| Subscriptions / services | 구독 |
| Salary / income / allowance | 수입 |
| Other / miscellaneous | 기타 |
# All categories
python3 finance/scripts/finance.py --user-id {user_id} monthly
# Specific category
python3 finance/scripts/finance.py --user-id {user_id} monthly --category 식비
# Specific month
python3 finance/scripts/finance.py --user-id {user_id} monthly --month 2025-01
python3 finance/scripts/finance.py --user-id {user_id} list --limit 10
When the user sends any image, follow this decision tree:
Image received
│
├─ Is it a receipt / invoice / 영수증?
│ → Receipt OCR workflow (see below)
│
├─ Is it a photo of a place / storefront / menu / sign?
│ → Place photo workflow (see below)
│
└─ Is it a screenshot of a map / navigation app?
→ Map screenshot workflow (see below)
Use Claude Vision to extract:
--address1. Analyze the receipt image with Vision
2. Extract: merchant, address, total, date
3. Determine category from merchant type
4. If address found on receipt → --address "{road address}"
5. If no address but merchant name → --location-name "{merchant name}"
6. Run finance.py save
Receipt examples:
Receipt shows:
상호: 맥도날드 강남점
주소: 서울 강남구 테헤란로 101
합계: 12,500원
→ python3 finance/scripts/finance.py --user-id {id} save \
--amount -12500 --category 식비 \
--description "맥도날드 강남점" \
--address "서울 강남구 테헤란로 101"
---
Receipt shows:
Store: Whole Foods Market
123 Main St, San Francisco CA
Total: $47.82
→ python3 finance/scripts/finance.py --user-id {id} save \
--amount -47 --category 식비 \
--description "Whole Foods Market" \
--address "123 Main St San Francisco CA"
---
Receipt shows:
약국: 올리브영 홍대점
(no address on receipt)
결제: 8,900원
→ python3 finance/scripts/finance.py --user-id {id} save \
--amount -8900 --category 쇼핑 \
--description "올리브영 홍대점" \
--location-name "올리브영 홍대점"
When the user sends a photo of a place (not a receipt):
1. Identify the store/place name from the image (signage, branding)
2. Check image EXIF metadata for GPS coordinates if available
3. If GPS found in EXIF → --lat {lat} --lng {lng} --location-name "{place name}"
4. If no GPS → --location-name "{place name}"
5. Ask the user for the amount if not mentioned
When the user sends a screenshot of a map or navigation app:
1. Read the place name / address visible on the map
2. Use --address if an address is visible, or --location-name for a place name
3. Ask the user for the amount and category if not clear from context
After running any save command, always confirm to the user:
📍 Geocoded ... → lat, lng) or stored as name-onlyUser: "점심으로 김치찌개 8000원 먹었어"
python3 finance/scripts/finance.py --user-id abc123 save \
--amount -8000 --category 식비 --description "김치찌개 점심" \
--text "점심으로 김치찌개 8000원 먹었어"
User: "스타벅스 강남점에서 아메리카노 6500원 썼어"
python3 finance/scripts/finance.py --user-id abc123 save \
--amount -6500 --category 식비 --description "스타벅스 아메리카노" \
--text "스타벅스 강남점에서 아메리카노 6500원 썼어"
# → auto-extracts "스타벅스 강남점" and geocodes it
User: "이번 달 식비 얼마야?"
python3 finance/scripts/finance.py --user-id abc123 monthly --category 식비
User: "월급 350만원 들어왔어"
python3 finance/scripts/finance.py --user-id abc123 save \
--amount 3500000 --category 수입 --description "월급" \
--text "월급 350만원 들어왔어"
User: "서울 강남구 테헤란로 152에서 점심 15000원 먹었어"
python3 finance/scripts/finance.py --user-id abc123 save \
--amount -15000 --category 식비 --description "점심" \
--address "서울 강남구 테헤란로 152" \
--text "서울 강남구 테헤란로 152에서 점심 15000원 먹었어"
# → Geocoding API로 주소 좌표 변환
User: [sends receipt image — GS25 편의점, 서울 마포구 와우산로 21, 합계 3,200원]
python3 finance/scripts/finance.py --user-id abc123 save \
--amount -3200 --category 식비 --description "GS25 편의점" \
--address "서울 마포구 와우산로 21"
# → 영수증에 주소 있음 → --address 사용 (Geocoding API)
User: [sends receipt image — 올리브영 신촌점, 주소 없음, 결제 22,000원]
python3 finance/scripts/finance.py --user-id abc123 save \
--amount -22000 --category 쇼핑 --description "올리브영 신촌점" \
--location-name "올리브영 신촌점"
# → 영수증에 주소 없음 → --location-name 사용 (Local Search API)
User: [sends photo of storefront with GPS EXIF: lat=37.5172, lng=127.0473]
python3 finance/scripts/finance.py --user-id abc123 save \
--amount -8000 --category 식비 --description "카페 방문" \
--lat 37.5172 --lng 127.0473 --location-name "카페 이름"
# → EXIF GPS 좌표 직접 사용
One-shot import of Google Contacts (People API) into the user's Connect (인맥) list. Pages through every contact, dedupes against existing connections by email then phone, and inserts new rows with category=acquaintance and tags=['google_contacts']. Honors BR-SOCIAL-3 — never writes social_profiles even if Google returns LinkedIn URLs. Use for: 구글 연락처 가져오기, 연락처 동기화, contacts import, import google contacts, sync google contacts, google 연락처, 인맥 가져오기, 주소록 가져오기
Record and sync Connect (인맥) activity timeline entries. Pulls from Google Calendar + Gmail (shares google-workspace OAuth token), or accepts manual entries like '김철수와 어제 점심 먹었어'. Use for: 인맥 활동, 활동 기록, 만남 기록, 미팅 기록, 점심, 저녁, 통화 기록, 일정 동기화, 인맥 동기화, connect sync, connect activity, meeting log, met with, had lunch with
Add or replace Context Memo (context_notes) for a Connect (인맥) entry. Looks up the target connection by exact or fuzzy name when the agent doesn't already know the connection UUID. Use for: 인맥 메모, 인맥 메모 추가, 메모 추가, 메모 수정, 인물 메모, 사람 메모, connect memo, add note, update note, context note
Scan a business card image with Gemini Vision and extract structured contact fields (name, role, company, email, phone, address, etc.) to create a new Connect (인맥) entry. Use for: 명함, 명함 스캔, 명함 등록, 명함 인식, 명함 저장, 명함 추가, 인맥, 인맥 추가, 인맥 등록, 연락처 등록, 연락처 추가, 비즈니스 카드, business card, business card ocr, scan business card, contact card, register contact
"Analyze uploaded images using Gemini Vision, or generate/edit images with Gemini AI. Use for: 이미지 분석, 사진 분석, 이미지 생성, 그려줘, 그림 만들어줘, image generation, photo analysis"
Search the internet for up-to-date information and fetch web page content via Tavily. Use for: 검색해줘, 찾아줘, 최신 정보, 인터넷 검색, 뉴스, 요즘, web search, latest news, current information