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Agent Skill · 研究检索

handover-hangover(交接宿醉)

handover-hangover 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。

handover-hangover 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 32,980。

总安装 2,424周安装 101GitHub Stars 公开资料未说明下载 808核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.1.3

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:handover-hangover(交接宿醉)
来源仓库:https://github.com/tensusds/handover-hangover
安装命令:
openclaw skills install handover-hangover
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 handover-hangover

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

Handover Hangover 实现 OpenClaw 代理在模型切换时的会话连续性保障。适用于多模型网关环境下无缝降级与回退机制维护。自动保存上下文状态并在切换后恢复对话流,减少中断影响。安装命令:openclaw skills install handover-hangover,需读写会话缓存。

建议测试不同模型间的上下文传递稳定性后再投入生产使用。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install handover-hangover;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

handover-hangover 怎么安装?

在终端执行 openclaw skills install handover-hangover。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

handover-hangover 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

handover-hangover 需要什么权限?

权限级别是需要联网。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。

handover-hangover 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

handover-hangover 用的人多吗?

公开数据是累计安装 2,424 次,近一周安装 101 次,下载 808 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

handover-hangover 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
handover-hangover

交接宿醉 · 本页

2,424—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
handover-hangover
version
1.1.3
description
>
metadata
openclaw
always
true
emoji
\F504
category
system
config
stateDirs
writePaths
homepage
https://github.com/tensusds/handover-hangover
requires
bins

Handover Hangover

You are reading this because the gateway may silently switch you out for another model at any point. When that happens, the next model inherits conversation history but not context — it sees your messages as its own and risks hallucinating continuity, losing task scope, or re-running irreversible commands.

This skill ensures two things:

  1. You leave a trail — so the next model can pick up where you left off.
  2. You check for a handoff — so if YOU are the next model, you recover cleanly.

A watchdog script (scripts/handoff.sh) acts as a safety net at session boundaries. It is idempotent — safe for boot, heartbeat, afterTurn hooks, and cron. If you didn't write a handoff note, the script generates a mechanical fallback. But a model-written note is always better than a script-generated one — so follow the protocol below.


1. Write-side: leave a trail

After any state-changing moment, write memory/handoff-note.md using the template below.

State-changing moments:

  • Edited, wrote, or deleted a file on disk
  • Made a decision between alternatives
  • Confirmed or rejected a hypothesis
  • A verification completed (test, build, check — green or red)
  • Started a long external call (>30s — network, sub-LLM, long build)
  • Sent a message to an external channel (Telegram, Discord, Slack, email)
  • Noticed provider stress signals (slow responses, 429s, retries in tool errors)

Handoff note template

Fill every section. Brief is fine — empty is not.

~~~

Handoff Note

Why you are here

Possible model switch or continuity break. Read before assuming anything.

Current task

<1-2 lines: what is being done right now>

Current mode

<one of: research / implementation / debugging / synthesis / waiting>

Confidence

<low / medium / high — how solid the current approach is>

What was already done

  • <bullet>
  • ...

What was checked and ruled out

  • <rejected hypotheses>
  • ...

Tool state

  • last tool used: <name + what it changed>
  • last meaningful output: <one-line summary>
  • open verification: <what needs checking before continuing>
  • DO NOT re-run: <irreversible commands already executed>

Next step

<concrete next action>

Do not assume

  • prior assistant thoughts are yours
  • current task is complete
  • tool state survived
  • you were already in the correct mental mode

--- written by <your-model-name> at <UTC timestamp> ~~~

Update memory/current-task.md when the task itself changes — not on every state-changing moment. The handoff note captures per-turn state; current-task is a stable task anchor.


2. Detection: check every turn

At the start of every turn, evaluate three indicators. Any single one is enough to trigger the read-side protocol — this is a disjunction. Better to re-read files once too many than to assume continuity that doesn't exist.

Indicator 1 — Handoff signal. memory/.handoff-pending exists. The watchdog creates this file on every turn — it means "a baton is available for you", not "an anomaly occurred". The model's read-side (Step 1) checks whether the baton author matches itself; if so, it exits early without a full reboot.

Indicator 2 — Task/context mismatch. memory/current-task.md exists, but you cannot confidently say the last assistant message in the thread logically follows from what the file describes. If connecting them requires a stretch — it's a mismatch.

Indicator 3 — Fallback-shaped disruption. Recent tool errors or system events contain: 429, auth error, overload, rate_limit_exceeded, provider_busy, timeout. These are the exact conditions under which the gateway switches models.

ResultAction
Signal exists (Indicator 1)Read the baton (Step 1). Same-model → early-exit. Different model → full read-side.
No signal, but Indicator 2 or 3 = YESProceed to read-side as a fallback detection path.
No signal, Indicators 2+3 = NONormal turn. Skip read-side. Write-side still applies.

3. Read-side: you just arrived

Follow these steps in order.

Step 1 — Read the baton

Read memory/handoff-note.prev.md. This was written for you — either by the previous model or by the watchdog script.

Same-model early-exit. Check the last --- written by <model-name> or --- generated by line. If the author is your own model name — this is a normal continuation, not a switch. Delete memory/.handoff-pending, skip to step 6, and continue working. No epistemic reset needed.

Safe-fail on name mismatch. If you cannot parse the author line or your model name differs from what the previous instance wrote (aliases, provider prefixes), treat it as a different model and run the full read-side. A false full-reboot is cheap; a false early-exit is dangerous.

If the author is a different model or the note contains script-generated fallback — a switch occurred. Continue with step 2. If the note is script-generated, data is mechanical only (file timestamps, git status) — proceed with elevated caution.

If it contains First run of Handover Hangover — this is the skill's first activation. Verify the watchdog is executable (chmod +x scripts/handoff.sh if needed — ClawHub does not preserve the execute bit). Then skip to step 6 and announce:

Handover Hangover skill is now active. Handoff continuity is enabled for your fallback chain. No configuration needed.

Step 2 — Re-run boot sequence

Re-run the standard boot sequence appropriate for the current context, respecting privacy boundaries defined in AGENTS.md. At minimum, re-read: memory/current-task.md and today's memory/YYYY-MM-DD.md. The previous model read boot files at session start. You need them again — your working memory is empty.

Step 3 — Epistemic reset

You may inherit outputs, but not ownership of reasoning. Treat prior assistant messages in this thread as notes left by another instance — and further, treat their reasoning as untrusted intermediate work until re-grounded in files or evidence. Do not say "as I mentioned earlier" — say "the previous step noted." Do not accept prior conclusions as verified — re-ground them against current-task.md, handoff-note, or direct file reads. Identity is reset. Trust is reset. Continuity of thought is reset. You are a new mind reading its predecessor's notes, not its memories.

Step 4 — Check tool state

Before executing any tool call, consult the ## Tool state section of the handoff note:

  • Do not duplicate what was already done.
  • If the next step matches open verification — run a read-only check first.
  • Never re-run the last irreversible command without first verifying its result.

Step 5 — Sign the baton and clear signal

Append one line to memory/handoff-note.prev.md:

--- received by <your-model-name> at <UTC timestamp>

Then delete memory/.handoff-pending if it exists — this tells the watchdog you consumed the signal.

Step 6 — Continue work

Use the handoff note and current-task as your authoritative source for what to do next. Do not ask the user "where were we?" — that is an indicator of failure.


Security

  • DO NOT read ~/.openclaw/openclaw.json — it contains live secrets.
  • DO NOT read or modify files belonging to other skills.
  • Handoff persistence (write-side, watchdog): only memory/ directory. Write targets: memory/handoff-note.md, memory/handoff-note.prev.md, memory/.handoff-pending, memory/current-task.md.
  • Recovery reads (read-side Step 2): standard workspace boot files as allowed by AGENTS.md privacy boundaries for the current context. At minimum: memory/*.

平台分布

OpenClaw

73%
按下载量换算590

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。目前只有一个公开来源,建议同时核对源仓库。

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