scratch-pad

```bash

# Initialize scratch pad

python scripts/scratch_pad.py init "Task Name"

# Add content

python scripts/scratch_pad.py append "Finding: The analysis shows..."

# Log tool calls

python scripts/scratch_pad.py log-tool "web_search" '{"query": "AI trends"}' "Found 10 results"

# Read current content

python scripts/scratch_pad.py read

```

Basic Operations

```bash

# Start new task

python scripts/scratch_pad.py --file /path/to/scratch.md init "Task Name"

# Add any content

python scripts/scratch_pad.py append "Content to add..."

# Add section header

python scripts/scratch_pad.py section "Research Findings"

# Read entire pad

python scripts/scratch_pad.py read

```

Structured Logging

```bash

# Log tool execution

python scripts/scratch_pad.py log-tool "tool_name" '{"param": "value"}' "result text"

# Add finding

python scripts/scratch_pad.py finding "Important discovery" --category "Analysis"

# Add checkpoint

python scripts/scratch_pad.py checkpoint "Phase 1 Complete"

# Add TODO

python scripts/scratch_pad.py todo "Follow up on this"

python scripts/scratch_pad.py todo "Completed task" --done

# Mark complete

python scripts/scratch_pad.py complete

```

ALWAYS use for:

• Tasks with 5+ tool calls
• Multi-source research ("조사해줘")
• Comparative analysis ("비교해줘")
• Information synthesis ("정리해줘")
• Step-by-step processing

```bash

# 1. Initialize at task start

SCRATCH_FILE="FILESYSTEM_BASE_DIR/files/{channel_id}/tmp/scratch_{timestamp}.md"

python scripts/scratch_pad.py --file $SCRATCH_FILE init "User request summary"

# 2. Log each tool call

python scripts/scratch_pad.py --file $SCRATCH_FILE log-tool "mcp__perplexity__search" '{"query": "..."}'

# Execute tool...

python scripts/scratch_pad.py --file $SCRATCH_FILE append "Result: Found X relevant items"

# 3. Add findings

python scripts/scratch_pad.py --file $SCRATCH_FILE finding "Key insight from research"

# 4. Read content for reference (DO NOT include raw content in response)

CONTENT=$(python scripts/scratch_pad.py --file $SCRATCH_FILE read)

# Use $CONTENT as reference to write organized response in mcp__slack__answer

# 5. Clean up (REQUIRED)

rm $SCRATCH_FILE

```

Important:

• The scratch pad is for YOUR reference only
• DO NOT copy/paste the raw markdown into responses
• USE it to organize and write a proper answer
• ALWAYS delete the scratch file after use

The markdown file is structured for easy reading:

```markdown

# 📋 Task Name

Created: 2025-11-05 10:00:00

Status: 🔄 In Progress

---

Task: Research competitor products

Started: 2025-11-05 10:00:00

---

[10:05:30] Found 3 main competitors...

🔧 [10:06:15] Tool: web_search

Parameters:

```json

{

"query": "competitor analysis"

}

```

Result:

```

Found 10 relevant results

```

---

✅ Checkpoint: Initial Research Complete

Time: 10:15:00

Gathered basic information on all competitors

---

```

1. Use descriptive section headers - Makes content easy to navigate
2. Log tools immediately - Capture parameters before execution
3. Add findings as you go - Don't wait until the end
4. Use checkpoints - Mark major milestones
5. Reference, don't copy - Use scratch pad as reference for organized response
6. Always clean up - Delete scratch files after task completion
7. Keep it concise - Focus on key information for YOUR reference

```python

from scripts.scratch_pad import ScratchPadManager

# Initialize

manager = ScratchPadManager('/tmp/task.md')

manager.init("Complex Analysis Task")

# Add content

manager.add_section("Research Phase")

manager.append("Starting research on topic X...")

# Log tool

manager.log_tool("web_search", {"query": "topic X"}, "10 results found")

# Add finding

manager.add_finding("Topic X is growing 50% annually", "Market Trends")

# Read content

content = manager.read()

# Mark complete

manager.complete()

```

• Location: Always in FILESYSTEM_BASE_DIR/files/{channel_id}/tmp/
• Naming: scratch_{timestamp}.md or scratch_{task_id}.md
• Cleanup: Delete after task completion
• Size limit: Keep under 1MB for performance

• ✅ Human-readable format
• ✅ No JSON parsing overhead
• ✅ Direct append operations
• ✅ Easy to include in responses
• ✅ Can be viewed/edited manually
• ✅ Natural structure for documentation