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remembering-conversations记住对话

Agent Skill

remembering-conversations 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

16,296

周安装

659

GitHub Stars

公开资料未说明

下载量

5,712
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:remembering-conversations(记住对话)
来源仓库:https://github.com/yumoezhung/remembering-conversations
安装命令:
openclaw skills install remembering-conversations
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install remembering-conversations

简介

回顾历史对话与代码探索过程,辅助解决开发难题与决策参考。

  • 适用于复杂问题回溯、最佳实践查找与知识沉淀场景。remembering-conversations 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 当遇到困难或疑问时自动检索过往交流记录,提供上下文线索。
  • 依赖本地对话存储机制,需确认数据保留周期与隐私策略。
  • 建议定期清理无关对话,避免信息过载影响检索效率。

SKILL.md

name
remembering-conversations
description
Use when user asks 'how should I...' or 'what's the best approach...' after exploring code, OR when you've tried to solve something and are stuck, OR for unfamiliar workflows, OR when user references past work. Searches conversation history.

Remembering Conversations

Core principle: Search before reinventing. Searching costs nothing; reinventing or repeating mistakes costs everything.

Mandatory: Use the Search Agent

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:

  1. Search with the search tool
  2. Read top 2-5 results with the show tool
  3. Synthesize findings (200-1000 words)
  4. Return actionable insights + sources

Saves 50-100x context vs. loading raw conversations.

When to Use

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:

  • User asks "how should I..." or "what's the best approach..."
  • You've explored current codebase and need to make architectural decisions
  • User asks for implementation approach after describing what they want

When you're stuck:

  • You've investigated a problem and can't find the solution
  • Facing a complex problem without obvious solution in current code
  • Need to follow an unfamiliar workflow or process

When historical signals are present:

  • User says "last time", "before", "we discussed", "you implemented"
  • User asks "why did we...", "what was the reason..."
  • User says "do you remember...", "what do we know about..."

Don't search first:

  • For current codebase structure (use Grep/Read to explore first)
  • For info in current conversation
  • Before understanding what you're being asked to do

Direct Tool Access (Discouraged)

You CAN use MCP tools directly, but DON'T:

  • mcp__plugin_episodic-memory_episodic-memory__search
  • mcp__plugin_episodic-memory_episodic-memory__show

Using these directly wastes your context window. Always dispatch the agent instead.

See MCP-TOOLS.md for complete API reference if needed for advanced usage.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

77.74%
按下载量换算4,441

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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