daily-log

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 ⬇️

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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]

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## 🔗 Key File Locations

### High-Value Outputs
- `path/to/key/file1` - [One-sentence description]
- `path/to/key/file2` - [One-sentence description]

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*Log generated at: YYYY-MM-DD HH:MM*  
*Attention score: High[X] Medium[X] Low[X]*

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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

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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

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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)

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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

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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