| name | daily-log |
| description | Use when recording work sessions, tracking decisions and outcomes, or documenting lessons learned |
| version | 1.1 |
Daily Log Skill
Overview
Generate comprehensive daily operation logs to track work, decisions, and lessons learned.
When to Use
Use this skill at the end of a work session or day to:
- Record completed tasks and their outcomes
- Track token usage and time spent
- Document key decisions and their rationale
- Capture lessons learned and mistakes
- Maintain continuity across sessions
Log Format Templates
Template A: Full Detail (Legacy)
Use for: Important milestones, detailed project records
See: FULL_TEMPLATE
Template B: Attention-Driven (Recommended)
Use for: Daily work logging, quick review
See below ⬇️
Attention-Driven Log Format (v1.1)
# YYYY-MM-DD Operation Log
## 📅 Session Overview
- **Date**: YYYY-MM-DD
- **Work Period**: HH:MM - HH:MM (X hours X minutes)
- **Core Outcomes**: [One-sentence summary of the day's most important output]
- **Key Decisions**: [X]
- **Lessons Learned**: [X]
- **Token Consumption**: ~XX,XXX
---
## ⏱️ Time Distribution
| Time Slot | Task | Duration | Attention Weight |
|-----------|------|----------|-----------------|
| HH:MM-HH:MM | [Task 1] | X min | 9/10 |
| HH:MM-HH:MM | [Task 2] | X min | 7/10 |
| ... | ... | ... | ... |
**Time Analysis**:
- High-attention task time: X% (mainly XX:XX-XX:XX)
- Interruptions/switches: X
- Peak efficiency period: XX:XX-XX:XX
---
## 🎯 High-Attention Tasks (Weight 8-10)
### [Task Name] (Weight: X/10, Time Slot: HH:MM-HH:MM, Duration: X min)
**One-sentence Summary**: [Core outcome or decision]
**Key Details**:
- [Specific data/numbers]
- [File paths/names]
- [Decision rationale]
- [Verification results]
**Lessons Learned** (if applicable):
- [Key takeaways]
---
## 📋 Medium-Attention Tasks (Weight 5-7)
| Task | Weight | Time Slot | Key Outcome |
|------|--------|-----------|-------------|
| [Task name] | 7/10 | HH:MM-HH:MM | [One-sentence description] |
| [Task name] | 6/10 | HH:MM-HH:MM | [One-sentence description] |
---
## 📝 Low-Attention Tasks (Weight 0-4)
- [HH:MM-HH:MM] [Task name] - [Status]
- [HH:MM-HH:MM] [Task name] - [Status]
---
## 📊 Today's Statistics
| Item | Value |
|------|-------|
| High-attention tasks | X |
| Medium-attention tasks | X |
| Low-attention tasks | X |
| Code files created | X |
| Code files modified | X |
| Skill created/updated | X |
| Token consumption | ~XX,XXX |
| Git commits | X |
---
## 💡 Today's Biggest Lesson
**One-sentence Summary**: [Core lesson]
**Background**: [What happened]
**Root Cause**: [Why it happened]
**Improvement Measures**: [How to improve]
---
## 🔗 Key File Locations
### High-Value Outputs
- `path/to/key/file1` - [One-sentence description]
- `path/to/key/file2` - [One-sentence description]
---
*Log generated at: YYYY-MM-DD HH:MM*
*Attention score: High[X] Medium[X] Low[X]*
Attention Scoring System
How to Score Task Attention (0-10)
| Factor | Weight | Indicator | Examples |
|---|
| Key Decision | +3 | Changed direction or approach | Choose plan B, approve implementation, confirm specification |
| Lesson/Mistake | +3 | Discovered and fixed issues | Violate rules, compile error, logic bug |
| Milestone | +2 | Important milestone completed | MVP completion, release, feature acceptance |
| File Changes | +1/ea | Create/modify/delete files | Create new Skill, modify config, refactor code |
| Routine Operations | 0 | Routine queries or checks | Check status, read files, check logs |
Attention Level Guidelines
Score 8-10 (High):
→ Full detail: summary + key details + lessons
Score 5-7 (Medium):
→ Brief: one sentence summary + key outcomes
Score 0-4 (Low):
→ Minimal: title + status only
Examples
Task: "Design MissionSystem Architecture"
- Key decision: +3 (Chose TK_SERIAL plan)
- Milestone: +2 (Design completed)
- Score: 8/10 → High attention
Task: "Fix Compile Error"
- Lesson: +3 (Learned BinaryReader→TK conversion)
- File changes: +8 files modified = +1 (max)
- Score: 9/10 → High attention
Task: "Check git status"
- Routine operation: 0
- Score: 2/10 → Low attention
Workflow
Step 1: Review Session
At end of session/day:
- List all tasks completed
- Identify major decisions made
- Note any mistakes or lessons
- Check for milestones reached
Step 2: Score Each Task
Apply attention scoring:
For each task:
- Did it involve a key decision? (+3)
- Was there a mistake/lesson? (+3)
- Was it a milestone? (+2)
- How many files changed? (+1 per, max 2)
- Sum → Attention Score (0-10)
Step 3: Categorize by Attention Level
- High (8-10): Write detailed section
- Medium (5-7): Add to table
- Low (0-4): List as bullet points
Step 4: Extract Key Information
For high-attention tasks, extract:
- One-sentence summary
- Key details (numbers, paths, outcomes)
- Lessons learned (if applicable)
Step 5: Generate Log
Write to memory/YYYY-MM-DD.md using attention-driven template
Step 6: Update Long-term Memory (Optional)
If significant decisions or patterns emerged, update MEMORY.md
Best Practices
✅ Do
- Score honestly - Not every task is high attention
- Focus on value - What would you want to remember in a month?
- Quantify - Use numbers, file counts, token estimates
- Link key files - Only high-value outputs need paths
- One lesson max - Focus on the most important lesson of the day
❌ Don't
- Don't over-document low-attention tasks
- Don't skip lessons learned section
- Don't include full conversation transcripts
- Don't log routine checks (git status, etc.) unless relevant
- Don't wait too long (score while memory is fresh)
Comparison: Full Detail vs Attention-Driven
Scenario: MissionSystem MVP Implementation Day
Full Detail Version: ~500 lines, ~95,000 tokens to read
- Every task fully documented
- All file paths listed
- Complete error descriptions
- Full conversation context
Attention-Driven Version: ~150 lines, ~20,000 tokens to read
- 2-3 high-attention tasks detailed
- 3-4 medium tasks in table
- 5+ low tasks as bullets
- Key decisions and lessons highlighted
Review Time:
- Full Detail: 10-15 minutes to scan
- Attention-Driven: 2-3 minutes to understand
Version History
