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)

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:

1. List all tasks completed
2. Identify major decisions made
3. Note any mistakes or lessons
4. 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

• v1.1 (2026-02-12) - Added Attention-Driven logging
  • Attention scoring system (0-10)
  • Three-level detail format
  • Focus on high-value information
  • Reduced log size by 60-70%
• v1.0 (2026-02-10) - Initial release
  • Standardized log format
  • 7-section structure
  • Statistics tracking
  • Lessons learned framework