From memory-bank
Searches past conversation history via a dedicated agent to find previously discussed solutions, patterns, and decisions. Prevents reinventing solutions by leveraging historical context.
How this skill is triggered — by the user, by Claude, or both
Slash command
/memory-bank:remembering-conversationsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
**Core principle:** Search before reinventing. Searching costs nothing; reinventing or repeating mistakes costs everything.
Core principle: Search before reinventing. Searching costs nothing; reinventing or repeating mistakes costs everything.
YOU MUST dispatch the search-conversations agent for any historical search.
Announce: "Dispatching search agent to find [topic]."
Then use the Task tool with subagent_type: "search-conversations":
Task tool:
description: "Search past conversations for [topic]"
prompt: "Search for [specific query or topic]. Focus on [what you're looking for - e.g., decisions, patterns, gotchas, code examples]."
subagent_type: "search-conversations"
The agent will:
search toolshow toolSaves 50-100x context vs. loading raw conversations.
You often get value out of consulting your memory bank once you understand what you're being asked. Search memory in these situations:
After understanding the task:
When you're stuck:
When historical signals are present:
Don't search first:
You CAN use MCP tools directly, but DON'T:
mcp__plugin_memory-bank_memory-bank__searchmcp__plugin_memory-bank_memory-bank__showUsing these directly wastes your context window. Always dispatch the agent instead.
See MCP-TOOLS.md for complete API reference if needed for advanced usage.
2plugins reuse this skill
First indexed Jul 7, 2026
npx claudepluginhub jung-wan-kim/memory-bankSearches past conversations before answering, guessing, or treating topics as new. Avoids reinventing solutions by recalling prior decisions, patterns, and gotchas.
Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search.
Recalls, searches, and analyzes past conversations using recent_chats.py, search_conversations.py, and lenses like retro, find-gaps, extract-decisions for context restoration and retrospectives.