Work Journal

Other

Transform fragmented work records into structured daily/weekly/monthly reports with AI-powered analysis and quantification.

Install

openclaw skills install work-journal

AI Work Journal (work-journal)

Turn scattered work notes into polished, quantified reports. The skill follows a "fragments → insights → reports" pipeline: parse raw text, classify work types, quantify achievements, and render into beautiful Markdown reports.

Quick Start

clawhub run work-journal --entries "Fixed 3 payment bugs, attended 2 meetings, wrote API docs"

This produces a formatted daily report with quantified results, tomorrow's plan, and auto-saves to ~/.openclaw/data/work-journal/.

Features

Feature	Description
Multi-mode Input	Free text, keyword lists, interactive Q&A, date range queries
Smart Classification	Auto-detect work type: dev/meeting/docs/design/ops/management/learning
Quantification Engine	Transform fuzzy descriptions into measurable outcomes
6 Templates	Daily Brief, Daily Full, Weekly, Monthly, OKR Report, Team Summary
AI Polish	Professional language refinement with raw vs polished comparison
Local History	Auto-save to Markdown files, searchable by date/project/tag
Trend Analysis	Week-over-week workload comparison

Input Format

{
  "entries": [
    {
      "content": "Fixed payment module pagination bug in the morning...",
      "timestamp": "2026-06-14T09:30:00+08:00",
      "category": "开发",
      "project": "Payment Module",
      "tags": ["bug-fix", "backend"]
    }
  ],
  "options": {
    "template": "daily_brief",
    "output_format": "markdown",
    "language": "zh-CN",
    "polish_level": "professional",
    "quantify": true,
    "include_tomorrow_plan": true,
    "recipient_context": "self"
  }
}

Sample Prompts

Prompt 1: Quick Daily Report

clawhub run work-journal --entries "Morning: fixed payment module pagination query bug. Noon: had requirements review meeting with PM. Afternoon: wrote API docs"

Expected output: Formatted daily report with:

📌 Summary: 3 items completed
🔧 Dev: Fixed payment pagination bug (query performance from 3s to 200ms)
💬 Meeting: Requirements review, 3 feature priorities confirmed
📝 Docs: API docs covering 12 interfaces (~2000 words)
📅 Tomorrow Plan: [pending]

Prompt 2: Weekly Report Generation

clawhub run work-journal --template weekly --date-range 2026-06-08..2026-06-14

Expected output: Auto-reads all daily records for the week, produces:

Weekly overview (tasks completed, time distribution)
Per-project achievement summary
Key metrics and milestones
Next week plan and risk alerts

Prompt 3: Interactive Daily Journal

clawhub run work-journal --interactive

Expected output: Starts guided Q&A mode asking "What dev tasks?", "Any meetings?", "Blockers?", "Tomorrow's plan?" → Full daily report

Prompt 4: Team Summary

clawhub run work-journal --template team --entries @team-entries.json

Expected output: Team-level summary with overall output, member contributions, collaboration highlights, blockers

First-Success Path

Goal: First valuable report within 30 seconds.

Step 1: clawhub install work-journal
Step 2: clawhub run work-journal --entries "Fixed 3 bugs, attended 2 meetings"
Step 3: Internal pipeline:
  a. parser.py tokenizes and extracts activities
  b. classifier.py identifies "bug fix" → dev, "meeting" → meeting
  c. quantifier.py quantifies: "3 bugs" → "Fixed 3 production defects"
  d. daily-brief template selected
  e. formatter.py renders to Markdown
  f. storage.py saves to local directory
Step 4: User sees formatted report displayed and saved
Step 5: Value achieved: structured report from 10 chars of input

Key Metrics: Input→output < 3s, minimum 10 chars, success rate > 95%.

Architecture

work-journal/
├── SKILL.md
├── scripts/
│   ├── parser.py          # Input parsing & intent recognition
│   ├── classifier.py      # Work type classifier
│   ├── quantifier.py      # Achievement quantification engine
│   ├── templates/         # Report templates (Markdown)
│   │   ├── daily_brief.md
│   │   ├── daily_full.md
│   │   ├── weekly.md
│   │   ├── monthly.md
│   │   ├── okr.md
│   │   └── team.md
│   ├── formatter.py       # Output formatting
│   ├── storage.py         # Local storage & retrieval
│   └── llm.py             # LLM call wrapper with fallback
└── references/
    └── examples.json       # Sample input/output

Error Handling

Code	Scenario	Action
E001	Empty input	Show guidance + examples
E002	Invalid template	List available templates
E003	Bad date format	Show correct format
E004	LLM timeout	Fall back to simple template
E005	LLM format error	Retry once, then return raw text
E006	File write failure	Output to stdout + error
E007	History read failure	Skip history, generate from input
E008	Unparsable content	Skip bad chars, mark warning

Security

Input sanitization: Scans for passwords/tokens/API keys → warns but doesn't filter
Local storage only: Data stored at ~/.openclaw/data/work-journal/, zero network upload
LLM privacy: Sends only work content and template options, no metadata
Permission isolation: Reads/writes only own data directory