scratchpad-skill

name	scratchpad-skill
description	Working memory protocol for multi-step tasks. Use the scratchpad to record intermediate findings without bloating LLM context.

Scratchpad Protocol

You have access to a file-based scratchpad that lives OUTSIDE your conversation context. Use it for any task that involves multiple steps, partial results, or large intermediate data.

When to Use

• Multi-step research: competitor analysis, multi-ASIN lookups, price comparisons
• Large tool outputs: Keepa product data, BigQuery results, web research findings
• Iterative refinement: collecting data across multiple tool calls before synthesizing

Procedure

• Step 1: Name your pad — pick a short descriptive name (e.g., competitor-research, price-analysis)
• Step 2: Write as you go — after each tool call that produces useful data, scratchpad_write(name, findings) to record it
• Step 3: Read when ready — when you have enough data, scratchpad_read(name) to review everything at once
• Step 4: Synthesize — produce your final answer from the scratchpad contents
• Step 5: Clean up — scratchpad_clear(name) when the task is done

Tools

Tool	Purpose
scratchpad_write(name, content)	Append findings to a named pad
scratchpad_read(name)	Read full pad contents
scratchpad_replace(name, content)	Overwrite with a condensed summary
scratchpad_clear(name)	Delete when done
scratchpad_list()	See all active pads

Gotchas

• Don't dump raw JSON — extract the relevant fields before writing. A scratchpad full of raw API responses is as useless as no scratchpad.
• Don't skip the read step — the pad is outside your context. You must explicitly read it to use the data.
• One pad per task — don't mix unrelated research in the same pad.
• Scratchpads are session-scoped — they're cleaned up on /reset. Don't use them for permanent storage; use remember_info for that.