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simple-random-interaction-designer简单的随机交互设计师

Agent Skill

simple-random-interaction-designer 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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

17,841

周安装

736

GitHub Stars

2

下载量

5,829
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:simple-random-interaction-designer(简单的随机交互设计师)
来源仓库:https://github.com/fjrevoredo/simple-random-interaction-designer
安装命令:
openclaw skills install simple-random-interaction-designer
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 simple-random-interaction-designer

简介

simple-random-interaction-designer 用于补充效率相关能力,适合在 OpenClaw 中让 Agent 发送自发消息时使用。

  • 主要功能是决定 OpenClaw 是否应在定期检查期间发送休闲消息,并选择自然交互类型。
  • 通过 openclaw skills install 命令从 ClawHub 安装,需确认权限范围。
  • 建议在使用前检查维护状态及是否会触发消息发送或定时调度操作。
  • 适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
simple-random-interaction-designer
description
Decide whether OpenClaw should send a spontaneous casual message during periodic checks, and when it should, choose a natural interaction type plus concise guidance for how to deliver it. Use when scheduling or executing human-like proactive chat check-ins.
metadata
{"homepage":"https://docs.openclaw.ai/tools/skills","env":[],"network":"optional","version":"2.0.0","notes":"Uses local randomness to return only a final yes/no decision and, on yes, an interaction type plus interaction description for natural outreach, including grounded OpenClaw-accessible real-world context when relevant"}

Simple Random Interaction Designer

Use this skill to decide whether to send a casual proactive message and, when the answer is yes, what kind of interaction to deliver. Use {baseDir}/scripts/random_interaction_designer.py as the default execution path.

Workflow

  1. Run the script once per scheduled check interval.
  2. Read decision from the JSON output.
  3. Stop immediately if decision is no.
  4. If decision is yes, use both interaction_type and interaction_description to draft the outgoing message.
  5. If the selected interaction is data-aware, use any relevant OpenClaw-accessible tools, skills, or integrations to fetch live context before drafting the message.
  6. Keep the final message brief, casual, and easy to ignore without social pressure.
  7. Prefer recent chat context when it is clearly present.
  8. Do not mention the random process, scheduled checks, or why this interaction was selected.

Primary Tooling

  • Script path: {baseDir}/scripts/random_interaction_designer.py
  • Runtime: Python 3, standard library only.

Preferred command:

  • python3 {baseDir}/scripts/random_interaction_designer.py

Output Contract

When the result is no:

{"decision":"no"}

When the result is yes:

{
  "decision": "yes",
  "interaction_type": "Playful opener",
  "interaction_description": "Send a brief playful line that feels spontaneous and easy to ignore."
}

Contract rules:

  • decision is always present and is either yes or no.
  • interaction_type is present only when decision is yes.
  • interaction_description is present only when decision is yes.
  • Do not expect debug fields, probability values, roll values, or fallback metadata.

Interaction Design Rules

  • Treat the JSON as execution guidance, not user-facing text.
  • Keep the final message to one or two short chat lines.
  • Prefer soft phrasing over transactional or assistant-like framing.
  • Avoid defaulting to "just checking in" language.
  • Ask at most one question in a single ping.
  • Do not fabricate recent context, external facts, or account-backed data.
  • For data-aware categories, prefer real-world grounding when OpenClaw can actually access the relevant source.
  • Use smart-home, weather, calendar, traffic, news, or market context only when the information is reliable, fresh, and genuinely relevant to the user.
  • If interaction_type depends on context or fresh data and that support is unavailable, rerun once to try for a non-data interaction; if rerunning is not practical, keep the message general and low-pressure instead of pretending specificity.
  • Vary tone and wording from recent interactions when possible so the behavior feels casual rather than patterned.

Interaction Catalog

Use the selected interaction_type and follow the matching guidance from interaction_description.

  1. Playful opener

Start with a short playful line that feels light and spontaneous.

  1. Curious check-in

Ask one low-stakes question that is easy to answer or ignore.

  1. Light shared observation

Make a casual observation that feels conversational rather than task-driven.

  1. Tiny celebration

Briefly acknowledge a small win or effort when the chat supports it.

  1. Smart device status

If OpenClaw can access relevant device state, share one useful smart-device status or gentle suggestion naturally.

  1. Weather-aware check-in

Use current weather only when fresh reliable data is available and clearly relevant.

  1. Calendar-aware nudge

Turn calendar context into a soft human-sounding reminder or prompt, not an alert.

  1. Context-aware follow-up

Build on a recent chat detail only when it is clearly present in the current conversation.

  1. Practical nudge

Offer one concise optional nudge that may help the user.

  1. Optional real-world update

Share one brief real-world update such as traffic, news, or market context only when reliable relevant data is already available.

Error Handling

  • If execution fails, surface the Python error message and rerun.
  • If output is not valid JSON, treat it as a hard failure and rerun.
  • If decision is missing or is not yes or no, rerun and discard the invalid result.
  • If decision is yes and either interaction_type or interaction_description is missing, rerun and discard the invalid result.

Minimal Examples

python3 {baseDir}/scripts/random_interaction_designer.py
python3 {baseDir}/scripts/random_interaction_designer.py --seed 42
python3 "{baseDir}/scripts/random_interaction_designer.py"
python3 "{baseDir}/scripts/random_interaction_designer.py" --seed 42

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

73.54%
按下载量换算4,287

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

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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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