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idle-reward-optimizer闲置奖励优化器

Agent Skill

idle-reward-optimizer 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

2,448

周安装

87

GitHub Stars

公开资料未说明

下载量

792
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:idle-reward-optimizer(闲置奖励优化器)
来源仓库:https://github.com/harrylabsj/idle-reward-optimizer
安装命令:
openclaw skills install idle-reward-optimizer
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 idle-reward-optimizer

简介

设计低摩擦微交互任务,适配碎片时间与低能量状态下的持续参与。

  • 适用于习惯养成类应用或轻度游戏化工作效率提升场景。
  • 生成轻量级操作建议如短问答、滑动选择等减少认知负担。
  • 强调保护用户恢复力,避免过度消耗造成疲劳累积。idle-reward-optimizer 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 需结合具体应用场景定制奖励机制与进度反馈方式。

SKILL.md

name
idle-reward-optimizer
description
Design low-friction idle, light-interaction, and micro-progress actions for fragmented or low-energy time while protecting recovery. Use when the user wants gentle gains from waiting windows, transitions, or tired periods without over-optimizing every minute.

Idle Reward Optimizer

Chinese name: 挂机收益优化

Purpose

Help the user turn fragmented or low-energy windows into gentle progress loops without stealing recovery. This skill is descriptive only. It does not create reminders, automations, or time-tracking systems.

Use this skill when

  • The user keeps losing small pockets of time to mindless scrolling.
  • The user wants useful actions for waiting, commuting, transitions, or recovery periods.
  • The user has low energy and needs options lighter than full-focus work.
  • The user wants a repeatable “idle reward” system that feels kind instead of punishing.

Inputs to collect

  • Fragmented time windows and their usual length.
  • Low-energy periods, common locations, and interruption level.
  • Tasks or themes that benefit from tiny amounts of progress.
  • Recovery needs, boundaries, and times that should stay empty.

Workflow

  1. Map the user’s fragmented windows, low-energy zones, and common waiting scenes.
  2. Sort candidate actions into idle, light interaction, micro-progress, and maintenance buckets.
  3. Match each scene with one low-friction action pack that fits the real energy cost.
  4. Add reuse rules so the user can repeat the pack without re-deciding every time.
  5. End with leave-blank rules for windows that should stay restful.

Output Format

  • Fragmented time map with scene, energy level, and safe action intensity.
  • Idle reward actions that need almost no thought.
  • Micro-progress actions that fit inside one to five minutes.
  • Leave-blank rules that protect rest and recovery.

Quality bar

  • Protect recovery first, instead of trying to monetize every spare minute.
  • Every suggested action must be genuinely light enough for the stated context.
  • Include at least one reusable action loop that can compound over time.
  • Keep the plan realistic for family life, commuting, or interruptions.

Edge cases and limits

  • If the user sounds depleted, prioritize restorative idle options before productivity ideas.
  • If time windows are highly unpredictable, use scene-based menus rather than fixed schedules.
  • Do not present this skill as a replacement for timers, trackers, or automation tools.

Compatibility notes

  • Can pair conceptually with game-inventory-manager and boss-fight-stamina-manager.
  • Works well for family life, commuting gaps, transition time, and recovery periods.
  • Text only, with no reminder or scheduling integration.

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

82.53%
按下载量换算654

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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