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agent-session-recall座席会话回忆

Agent Skill

agent-session-recall 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

3,224

周安装

133

GitHub Stars

公开资料未说明

下载量

1,053
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:agent-session-recall(座席会话回忆)
来源仓库:https://github.com/yozu/agent-session-recall
安装命令:
openclaw skills install agent-session-recall
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 agent-session-recall

简介

恢复不明确消息的上下文,解决因信息缺失导致的理解偏差。

  • 适用于多轮对话中用户提问缺乏前置说明的场景。
  • 自动检索最近对话片段以提供必要背景,提升回复准确性。
  • 安装命令:openclaw skills install agent-session-recall;建议启用会话存储。
  • 注意涉及历史对话访问,应确保符合数据保留和隐私政策。

SKILL.md

name
session-recall
description
Recover conversation context when a message arrives with unclear meaning. Use when a user's message lacks context (e.g. "I logged in" with no prior mention of logging in), when resuming after compaction or session reset, or when switching between channels and losing thread. Searches session transcripts, channel summaries, memory files, and cross-channel history to reconstruct context before asking the user.

Session Recall

Recover missing conversation context autonomously. *Never ask the user "what are you talking about?" until all steps are exhausted.*

When This Triggers

  • An incoming message doesn't match the current session context
  • Session starts after compaction (.jsonl.reset exists)
  • User references something not in current context window
  • User references a topic from another channel or a cron-delivered notification
  • Thread reply arrives without visible parent context

Recovery Flow

Execute steps in order. Stop early if sufficient context is found.

Step 0: sessions_history (fastest check)

Use sessions_history to retrieve recent messages for the current channel. This requires no file I/O and is the quickest way to recover recent context.

Step 1: Same-channel transcript

Read the .jsonl transcript for the current session:

  1. Look up the session associated with this channel via sessions_list or sessions.json
  2. Read ~/.openclaw/agents/{agent}/sessions/{sessionId}.jsonl (default agent: main)
  3. For large files, read the tail first (tail -n 200) rather than loading the entire file
  4. Check for .reset files with the same sessionId prefix (pre-compaction data)
  5. If the message is a thread reply, read the parent message first

Step 2: Channel context summary

Read memory/channel_context/{channel-name}.md if it exists. These are user-maintained summaries of ongoing topics per channel — not a built-in OpenClaw feature, but a recommended convention.

Step 3: Cross-channel and cron search

Messages often originate from cron jobs or other channels.

  1. Extract key terms from the unclear message
  2. Use the bundled search script or grep across all .jsonl files:
   ./scripts/search_sessions.sh "keyword"
  1. Also search for the current channel ID in other sessions — cron jobs send messages to channels but these don't appear in the channel's own .jsonl:
   ./scripts/search_sessions.sh "{current_channel_id}"
  1. When a match is found, read surrounding context to understand the full conversation
  2. Sort results by timestamp, prioritize most recent

Step 4: Memory files

  1. memory/active_context.md — current shared context across channels
  2. memory/YYYY-MM-DD.md — today and yesterday's daily notes
  3. Semantic memory search if available in your setup (e.g., memory_search tool)

Step 5: Ask the user (last resort)

Only after steps 0–4 yield nothing. Be specific about what was searched:

"Searched this channel's transcript, cross-channel sessions (including cron), and memory files for '{keyword}' but couldn't find context. What are you referring to?"

Key Insight: Cron-to-Channel Messages

Cron jobs can send messages to channels via sessions_send or direct API calls. These messages:

  • Appear in the chat platform for the user to see
  • Are logged in the *cron session's* .jsonl, NOT the target channel's .jsonl
  • Require Step 3's channel-ID search to discover

This is the most common cause of unrecognized messages — the user is responding to something a cron job sent.

Security Note

Session transcripts may contain sensitive data (API keys, passwords, personal information). Do not pipe search output to public channels or logs. This skill assumes single-user/single-agent deployment.

Notes

  • Path ~/.openclaw/agents/main/sessions/ assumes default agent name main. Adjust if using a custom agent name.
  • Channel context files (memory/channel_context/) are a recommended convention, not built-in. Users create and maintain these themselves.
  • The search script requires python3, grep, and bash.

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

89.09%
按下载量换算938

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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