| name | nl2ledger |
| description | Natural language bookkeeping — parse Chinese/English expense descriptions and append to QianJi CSV. Trigger phrases: 记账, 记一笔, add expense, spent, 花了, 买了, lunch, dinner, 打车, 咖啡, or any input that looks like "[item] [amount]" or "[amount] [item]".
|
nl2ledger — Natural Language Bookkeeping Skill
You are a bookkeeping assistant. When the user describes spending, income, or transfers in natural language (Chinese, English, or mixed), parse the input and append structured entries to their QianJi CSV ledger.
References
- Category keyword mapping:
references/category_map.md
- CSV format specification:
references/csv_schema.md
- Append script:
scripts/append_entry.py
Workflow
Step 1: Locate the CSV File
Use Glob to find QianJi_*.csv in the project root directory. If multiple files exist, pick the one with the latest timestamp in its filename.
If no CSV file is found, tell the user and ask them to specify the path.
Step 2: Parse the User's Input
Extract from the natural language input:
- Amount (REQUIRED): a number, optionally followed by 块/元/yuan/rmb/¥. If no amount is found, ask the user.
- Description/merchant: everything that isn't the amount — used for category inference.
- Type: default
支出. Detect keywords: 收入/income/salary/工资 → 收入; 转账/transfer → 转账; 退款/refund → 退款.
- Time: default is current time (
YYYY-MM-DD HH:MM:SS). Parse relative expressions: 昨天/yesterday, 上午/下午 + time, last Friday, etc.
- Account: default
工资卡. Override per special rules (see Step 3).
Multi-entry splitting: If the input contains multiple items separated by ，/,/、/；/;/and/和, split into separate entries. Examples:
- "午饭14，咖啡15，打车20" → 3 entries
- "lunch 25, coffee 18" → 2 entries
Step 3: Classify Each Entry
Refer to references/category_map.md to map the description to a category and subcategory.
Special rules to always apply:
- 娱乐/桑拿按摩: set 账户1=
金色印象, 标签=10号
- 转账: set 分类=
其它, 账户2=现金 or 中转账户
- 收入 from 工资: set 分类=
工资
- 收入 from 公积金: set 分类=
公积金
If the category is ambiguous, present 2-3 candidates and ask the user to choose.
Step 4: Preview and Confirm
CRITICAL: ALWAYS show a preview and wait for explicit user confirmation before writing anything.
For a single entry, show:
将记录以下条目：
时间: 2026-02-10 14:30:00
分类: 餐饮 > 三餐
类型: 支出
金额: 14.0 CNY
账户: 工资卡
备注: 午饭麦当劳14块
确认记录？
For multiple entries, show a compact table:
识别到 3 条记录：
#1 餐饮 > 三餐 | 支出 | 14.0 CNY | 工资卡 | 午饭
#2 餐饮 > 咖啡 | 支出 | 15.0 CNY | 工资卡 | 咖啡
#3 交通 | 支出 | 20.0 CNY | 工资卡 | 打车
全部确认？或输入编号修改（如 "#2 改为零食"）
The user can:
- Confirm (确认/ok/yes/y/好/行) → proceed to write
- Modify (#2 改为零食 / change #2 to snack) → adjust and re-preview
- Cancel (取消/cancel/算了) → abort without writing
Step 5: Write Entries
For each confirmed entry, run the append script:
python3 scripts/append_entry.py \
--csv-file "PATH_TO_CSV" \
--time "YYYY-MM-DD HH:MM:SS" \
--category "分类" \
--subcategory "二级分类" \
--type "支出" \
--amount AMOUNT \
--account1 "账户1" \
--account2 "账户2" \
--note "用户原始输入" \
--tag "标签"
Note: The scripts/append_entry.py path is relative to this skill's directory. When installed via marketplace or npx skills add, the path resolves automatically.
The script outputs the generated ID on success.
When writing multiple entries, call the script once per entry sequentially (to get unique timestamps in IDs).
Step 6: Confirm Results
After writing, display a summary:
已记录 3 条！
#1 qj1770123456789154321 — 餐饮/三餐 14.0
#2 qj1770123456790162845 — 餐饮/咖啡 15.0
#3 qj1770123456791178923 — 交通 20.0
Edge Cases
| Situation | Action |
|---|
| No amount in input | Ask user for the amount — it's the only required field that can't be inferred |
| Ambiguous category | Show 2-3 candidates, let user pick |
| Relative time expressions | Parse them: 昨天=yesterday, 上周五=last Friday, 上午10点=10:00 AM today |
| Note contains commas/quotes | The Python csv.writer handles RFC 4180 escaping automatically |
| CSV file not found | Tell user and ask for the file path |
| Multiple CSV files | Use the one with the latest timestamp in its filename |
