Life Capture
by @epitomizelu
capture daily-life notes into markdown and sqlite. use when the user wants to record one or more life entries such as expenses, completed tasks, schedules, r...
clawhub install life-capture📖 About This Skill
name: life-capture description: capture daily-life notes into markdown and sqlite. use when the user wants to record one or more life entries such as expenses, completed tasks, schedules, reminders, or ideas; classify the content; generate tags; parse natural language into structured json; write a daily markdown note under life/daily; and sync structured fields into a local sqlite database. triggers include short single-line entries, mixed sentences containing multiple record types, or requests to log and organize personal information for later review and reporting.
life-capture
Turn natural-language life logs into durable records. This skill classifies each input item, generates tags, creates user-visible markdown, writes to a daily note under life/daily, and syncs structured data into life/db/life.db.
Default storage layout
Use these paths unless the user explicitly overrides them:
life/
daily/
ideas/
db/life.db
Create missing directories as needed. Never delete existing content. Append or update only.
Supported record types
Map every parsed item to exactly one primary type:
expense: spending, bills, purchases, subscriptions, refundstask: completed tasks, ongoing work, todos, chores, habitsschedule: calendar items, appointments, time blocks, plansidea: ideas, inspiration, possible projects, reflections worth savingWhen a sentence contains multiple items, split it into multiple records.
Output contract
For each user request:
1. Parse the message into one or more records.
2. Generate a stable id for each record using the pattern:
- exp_YYYYMMDD_NNN
- task_YYYYMMDD_NNN
- sched_YYYYMMDD_NNN
- idea_YYYYMMDD_NNN
3. Generate 1 to 4 short tags.
4. Show the user the organized result in markdown.
5. Save the records by running scripts/process_entry.py.
Always keep the original user wording in raw_text. Never invent missing fields. Leave unknown fields null.
User-visible response format
Because this skill is configured for visible output, show a concise but complete result after writing:
## 已整理记录1)
ID:
标签: #a #b
归档:
数据库: #### Markdown
#### JSON
If there are multiple records, repeat the block for each one.
Parsing rules
Use scripts/parse_entries.py for natural-language parsing. The parser now reads configurable rules from references/parser_config.json, so prefer editing that file instead of changing Python when you need new categories, tags, or keyword mappings.
Expense
Extract when present:
amountcurrency (default CNY only when the currency symbol or language implies RMB; otherwise null)categorysubcategorymerchantpay_methodDefault top-level tags often include 开销 plus one semantic tag such as 餐饮 or 交通.
Preferred categories:
Task
Extract when present:
status (todo, doing, done, cancelled)priority (low, normal, high)projectdue_datecompleted_atIf the user says they already did something, default status to done.
Schedule
Extract when present:
schedule_datestart_timeend_timelocationstatus (planned, done, skipped)If the user uses relative dates, resolve them from the current conversation date. Prefer passing --today YYYY-MM-DD to scripts/process_entry.py or scripts/parse_entries.py so relative dates like 明天 are stable across environments.
Idea
Extract when present:
idea_typestatus (captured, reviewing, used, archived)related_task_idDefault status to captured.
Configurable parsing rules
Before editing Python, check whether the change can be made in references/parser_config.json.
You can change:
To test a modified config without changing the bundled default file:
python scripts/parse_entries.py --config /path/to/custom_config.json --text "买咖啡 18 元，明天下午两点去体检"
Markdown writing rules
Write each record into the daily note for its effective date under one of these sections:
## 开销## 任务## 日程## 灵感Use this block structure:
###
时间：
标签：#tag1 #tag2
原始描述：
摘要：
Then add type-specific fields:
Execution workflow
End-to-end one-command flow
Use this when the user provides natural language and wants the records saved immediately:
python scripts/process_entry.py --root life --db life/db/life.db --today 2026-03-10 --text "今天中午牛肉面 26 元，下午整理了书桌，想到可以做一个生活数据看板"
The wrapper script will:
1. initialize the database if missing
2. parse text into {"records": [...]} with scripts/parse_entries.py
3. save markdown and sqlite rows with scripts/save_entry.py
4. print parsed records plus save results as json
Split-step flow
Use this when the user asks to inspect or verify the structured output before writing:
python scripts/parse_entries.py --text "明天下午两点去体检，买咖啡 18 元"
Then save:
python scripts/save_entry.py --root life --db life/db/life.db --stdin-json
Database init only
Use this once before first write if life/db/life.db does not exist and you are not using process_entry.py:
python scripts/init_db.py --db life/db/life.db
Database sync rules
The database design is:
entriesexpensestasksschedulesideastagsentry_tagsSee references/schema.md for the schema, references/examples.md for sample payloads and commands, and references/configuration.md plus references/parser_config.json for configurable parsing rules.