remembering-conversations by obra/episodic-memory
npx skills add https://github.com/obra/episodic-memory --skill remembering-conversations核心原则: 先搜索,后重构。搜索无需成本;重构或重复错误则代价高昂。
对于任何历史搜索,您必须调用 search-conversations 智能体。
声明:"正在调用搜索智能体以查找 [主题]。"
然后使用 subagent_type: "search-conversations" 的 Task 工具:
Task tool:
description: "搜索过往对话以查找 [主题]"
prompt: "搜索 [具体查询或主题]。重点关注 [您要寻找的内容 - 例如,决策、模式、陷阱、代码示例]。"
subagent_type: "search-conversations"
该智能体将:
search 工具进行搜索show 工具阅读前 2-5 条结果相比加载原始对话,可节省 50-100 倍的上下文空间。
一旦理解了被询问的内容,查阅您的片段式记忆通常会带来价值。在以下情况下搜索记忆:
在理解任务之后:
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当您遇到困难时:
当出现历史信号时:
不要首先搜索:
您可以直接使用 MCP 工具,但请不要:
mcp__plugin_episodic-memory_episodic-memory__searchmcp__plugin_episodic-memory_episodic-memory__show直接使用这些工具会浪费您的上下文窗口。请始终调用智能体。
如需高级用法,请参阅 MCP-TOOLS.md 获取完整的 API 参考。
每周安装量
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首次出现
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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 episodic memory 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_episodic-memory_episodic-memory__searchmcp__plugin_episodic-memory_episodic-memory__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.
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97,600 周安装