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Install in OpenClaw
/install 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...
README (SKILL.md)
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
- Map the user’s fragmented windows, low-energy zones, and common waiting scenes.
- Sort candidate actions into idle, light interaction, micro-progress, and maintenance buckets.
- Match each scene with one low-friction action pack that fits the real energy cost.
- Add reuse rules so the user can repeat the pack without re-deciding every time.
- 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.
Usage Guidance
This skill appears to be a harmless, text-only guidance helper: it only reads its own SKILL.md and user input and returns a formatted plan. Before installing, you may (1) review handler.py to confirm no network calls are added, (2) run the included tests locally if you run third-party code, and (3) note the small mismatch that metadata says 'instruction-only' even though Python files are present — this is not dangerous but is worth being aware of. If you want to be extra cautious, run the skill in an environment without sensitive credentials available (it doesn't need any).
Capability Analysis
Type: OpenClaw Skill
Name: idle-reward-optimizer
Version: 1.0.0
The idle-reward-optimizer skill is a purely descriptive tool designed to provide time-management advice. The handler.py script performs safe text parsing and formatting of the SKILL.md file to generate guidance cards, and the instructions in SKILL.md are focused on productivity and recovery without any malicious commands, network activity, or data exfiltration attempts.
Capability Assessment
Purpose & Capability
The name/description (design low-friction idle actions) matches the SKILL.md and the handler code: the handler reads SKILL.md, formats a guidance card, and returns text. The only minor mismatch is the metadata's claim that the skill is 'instruction-only' while repository includes handler.py and tests — but those files implement a harmless renderer for the instruction content and are coherent with the stated purpose.
Instruction Scope
SKILL.md instructs the agent to collect only user-provided context (fragmented time, energy, tasks) and to produce textual action packs. It explicitly states it does not create reminders/automations. The handler implementation only reads its local SKILL.md and the provided user input; it does not access other files, env vars, or network endpoints.
Install Mechanism
There is no install spec and no downloaded code at runtime. The skill ships with local Python files (handler and tests) which are readable and self-contained. No external package installs, URLs, or archive extraction are present.
Credentials
The skill declares no required environment variables, credentials, or config paths. The handler does not read environment variables or request secrets, which is proportionate to a purely textual guidance skill.
Persistence & Privilege
The skill does not request always:true and is user-invocable only; it does not modify other skills or system configuration. Autonomous invocation is allowed by platform default, but the skill's limited scope and lack of credential access keeps its privilege low.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install idle-reward-optimizer - After installation, invoke the skill by name or use
/idle-reward-optimizer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of idle-reward-optimizer.
- Helps users turn fragmented or low-energy time into gentle, low-effort progress without sacrificing recovery.
- Prioritizes rest and sets boundaries for times that should remain restful.
- Guides users to create scene-based action packs for waiting, transitions, or tired periods.
- Designed for descriptive advice only—does not set reminders or automate tasks.
- Works in a variety of contexts, including family life and commuting.
Metadata
Frequently Asked Questions
What is Idle Reward Optimizer?
Design low-friction idle, light-interaction, and micro-progress actions for fragmented or low-energy time while protecting recovery. Use when the user wants... It is an AI Agent Skill for Claude Code / OpenClaw, with 81 downloads so far.
How do I install Idle Reward Optimizer?
Run "/install idle-reward-optimizer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Idle Reward Optimizer free?
Yes, Idle Reward Optimizer is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Idle Reward Optimizer support?
Idle Reward Optimizer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Idle Reward Optimizer?
It is built and maintained by haidong (@harrylabsj); the current version is v1.0.0.
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