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Decision Fatigue Reliever

作者 haidong · GitHub ↗ · v1.0.5 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install decision-fatigue-reliever
功能描述
Helps you make quick decisions on daily trivial matters (food, clothing, activities, shopping), reducing choice anxiety and freeing up cognitive resources. U...
使用说明 (SKILL.md)

Decision Fatigue Reliever

Overview

Helps you make quick decisions on daily trivial matters, reducing choice anxiety and freeing up cognitive resources.

"Let AI handle the trivial decisions, save your energy for what truly matters"


Triggers

Natural Language Triggers

Decision Type Example Triggers
🍽️ What to Eat "What should I eat for lunch", "Help me decide dinner", "Suggest breakfast", "Don't know what to eat"
👔 What to Wear "What should I wear today", "Help me pick an outfit", "What to wear for an interview tomorrow", "What to wear in this weather"
🎯 What to Do "Don't know what to do this weekend", "What to do after work", "Want to exercise, what should I choose"
🛒 What to Buy "What gift for my friend", "Want to buy headphones, recommend one", "Help me choose something cost-effective"

Quick Commands

Command Function
/decide food Quick food decision
/decide outfit Quick outfit decision
/decide activity Quick activity decision
/decide buy [item] Quick purchase decision
/decide random Completely random decision

Core Features

🍽️ Food Decisions

Applicable Scenarios: Breakfast, lunch, dinner, snacks, group dining

Optional Inputs:

  • Budget range (Economy / Standard / Unlimited)
  • Taste preference (Light / Heavy / Spicy / Sweet, etc.)
  • Time constraint (Rushed / Normal / Relaxed)
  • Dietary restrictions (Vegetarian / Halal / Allergies, etc.)

Output:

  • Recommended dish/restaurant
  • Reasoning (taste, nutrition, convenience)
  • Estimated price and time
  • Alternative options

Example:

User: What to eat for lunch? Budget 50, short on time

AI: 🍽️ Lunch Decision: Business Set Meal

📍 Recommendation: [Yoshinoya] Beef Bowl Set
📋 Reasoning: Fast service (3-5 min), within budget (¥45), filling
⏱️ Time: 20-25 minutes

💡 Alternatives: Convenience store bento (faster) / Light salad (healthier)

👔 Outfit Decisions

Applicable Scenarios: Daily wear, occasion dressing, weather adaptation

Optional Inputs:

  • Occasion (Work / Date / Exercise / Formal event)
  • Weather/temperature
  • Style preference (Minimalist / Fashionable / Vintage, etc.)

Output:

  • Complete outfit plan (Top + Bottom + Shoes + Accessories)
  • Styling reasoning (color, occasion fit, comfort)
  • Weather adaptation notes
  • Alternative outfits

Example:

User: First date tomorrow, 18°C, what to wear?

AI: 👔 Date Outfit: Clean Gentleman Style

🧥 Outfit:
• White T-shirt + Light gray casual blazer
• Dark blue slim-fit jeans
• White sneakers + Simple watch

📋 Reasoning: Can remove blazer at 18°C, suitable for restaurant + movie, clean color palette
💡 Alternatives: More formal (shirt + leather shoes) / More casual (remove blazer)

🎯 Activity Decisions

Applicable Scenarios: Weekend activities, after-work plans, exercise choices, work-life balance

Optional Inputs:

  • Available time (duration)
  • Energy level (High / Medium / Low)
  • Budget (Free / Low / Medium / High)
  • Group size (Solo / Friends / Family)

Output:

  • Recommended activity plan
  • Activity reasoning (fit analysis)
  • Required preparation
  • Estimated duration and cost
  • Alternative activities

Example:

User: Weekend alone, don't want to spend much, want to go out

AI: 🎯 Weekend Activity: Park Picnic + Reading

📍 Recommendation: City park picnic
📋 Reasoning: Solo-friendly, low cost (under ¥50), outdoor + digital detox
🎒 Preparation: Picnic mat, sandwich, fruit, book to read
⏱️ Duration: 3-4 hours
💰 Cost: ¥30-50

💡 Upgrade: Bring camera for photography practice / Bird and plant watching

🛒 Purchase Decisions

Applicable Scenarios: Gift shopping, daily necessities, electronics comparison, value shopping

Optional Inputs:

  • Product category
  • Purchase purpose/usage scenario
  • Budget limit
  • Recipient info (age/gender/preferences)
  • Priority (price/quality/brand/features)

Output:

  • Recommended product/brand (1-3 options)
  • Reasoning (fit analysis)
  • Price comparison
  • Purchase channel suggestions

Example:

User: Birthday gift for mom, budget 300-500

AI: 🎁 Gift Decision: Silk Scarf + Hand Cream Set

🎯 Recommendation: Wensli silk scarf + L'Occitane hand cream
📋 Reasoning: Matches 50+ elegant taste, practical, mom values skincare
💰 Budget: ¥300-500
🛒 Channel: Tmall flagship store

💡 Alternatives: Massager (for neck/shoulder pain) / Tea gift set (if she loves tea)

Decision Templates

Standard Output Format

🎯 Decision: [Specific recommendation]

📋 Reasoning:
• [Reason 1 - Preference match]
• [Reason 2 - Scenario fit]
• [Reason 3 - Other value]

💰 Estimated Cost: [Price range]
⏱️ Estimated Time: [Time needed]

💡 Alternatives:
1. [Alternative 1]
2. [Alternative 2]

❓ Satisfaction: [👍 Satisfied / 👎 Not satisfied / 🔄 Try another]

Quick Decision Mode

When user says "hurry", "whatever", "quick", use simplified output:

🚀 Quick Decision: [Result]

Reason: [One sentence]

Say "another one" if not satisfied

Usage Examples

Example 1: Workday Lunch

User: What to eat for lunch? Budget 50, near office, short on time

AI: 🍽️ Lunch Decision: Business Set Meal

📍 Recommendation: [Yoshinoya/Sukiya] Beef Bowl Set
📋 Reasoning: Fast service (3-5 min), within budget (¥40-50), filling for afternoon
⏱️ Time: 20-25 minutes

💡 Alternatives: Convenience store bento (faster) / Light salad (healthier)

Example 2: Date Outfit

User: First date tomorrow night, 18°C, dinner and movie

AI: 👔 Date Outfit: Clean Gentleman Style

🧥 Outfit:
• White T-shirt + Light gray casual blazer
• Dark blue slim-fit jeans
• White sneakers + Simple watch

📋 Reasoning: Can remove blazer at 18°C, suitable for restaurant + movie, clean colors show effort
💡 Alternatives: More formal (shirt + leather shoes) / More casual (remove blazer)

Example 3: Weekend Activity

User: Weekend alone, don't want to spend much, want to go out

AI: 🎯 Weekend Activity: Park Picnic + Reading

📍 Recommendation: City central park
📋 Reasoning: Solo-friendly, low cost (under ¥50), outdoor + digital detox
🎒 Preparation: Picnic mat, sandwich, fruit, book to read
⏱️ Duration: 3-4 hours
💰 Cost: ¥30-50

Example 4: Gift Shopping

User: Birthday gift for mom, budget 300-500

AI: 🎁 Gift Decision: Silk Scarf + Hand Cream Set

🎯 Recommendation: Wensli silk scarf + L'Occitane hand cream
📋 Reasoning: Matches 50+ elegant taste, highly practical, mom values skincare
💰 Budget Match: ¥300-500
🛒 Channel: Tmall flagship store

💡 Alternatives: Massager (for neck/shoulder pain) / Tea gift set (if she loves tea)

Decision Principles

1. Decision First

Give a clear decision result first, then explain the reasoning

2. Transparent Reasoning

Help users understand the logic behind the decision

3. Provide Alternatives

Always provide 1-2 alternative options

4. Feedback Loop

Every output includes a feedback entry point

5. Personalization

Adjust recommendations based on user context and preferences


Safety Escalation (High-Risk Response)

⚠️ Stop the normal decision-support flow immediately if the user expresses any of the following:

  • self-harm or suicide thoughts related to decisions
  • overwhelming despair or inability to make any decisions
  • obvious acute mental-health crisis that makes decision support inappropriate

Use a direct response like:

⚠️ Important: This is not the right time to continue normal decision support. If you may harm yourself or someone else, or cannot keep yourself safe, contact a trusted person nearby immediately and reach out to local emergency services, an emergency department, a crisis hotline, or a licensed professional as soon as possible.

Then keep the tone calm, direct, and serious. Do not continue normal decision guidance until safety is addressed.


Boundaries & Limitations

Supported

✅ Daily trivial matters: food, clothing, activities, shopping

Not Supported

❌ Major life decisions: career choices, investment decisions, medical decisions, legal decisions

Disclaimer

⚠️ Disclaimer: This tool is only for everyday low-stakes decision support and does not constitute medical advice, investment advice, legal advice, or any other professional guidance. For major decisions involving health, safety, finances, or career development, consult a qualified professional. Users remain responsible for their own decisions and outcomes.

This skill can:

  • help with daily trivial decisions (food, clothing, activities, shopping)
  • reduce choice anxiety through quick recommendations
  • provide alternatives for user to choose from

This skill must not:

  • make decisions about mental health treatment
  • claim the user has decision-making disorders
  • present recommendations as medical or psychological advice
  • replace professional counseling, legal advice, or financial planning

Technical Information

Attribute Value
Skill ID decision-fatigue-reliever
Version 1.0.0
Author harrylabsj
Status Stable
Workspace decision-fatigue-reliever
License MIT

Last updated: 2026-03-23

安全使用建议
This skill appears safe and coherent: it only uses the included JSON data and text templates to make recommendations. Before installing or using it, consider: (1) it may ask for personal context (location, recipient details, budgets) to tailor suggestions — don't provide sensitive credentials or private documents; (2) recommendations include third‑party channels (Tmall, JD, shipping services) — verify prices and sellers yourself; and (3) results are regionally biased (many China-specific examples/brands), so expect localization limits. If you later connect this skill to external connectors (maps, stores) or grant it permissions, review what external access/credentials it will require.
功能分析
Type: OpenClaw Skill Name: decision-fatigue-reliever Version: 1.0.5 The skill bundle is a well-structured decision-support tool designed to help users with trivial daily choices like food, clothing, and activities. It consists entirely of markdown instructions (SKILL.md) and reference data (JSON files in the references/ directory) without any executable code, network calls, or sensitive data access. The instructions include appropriate safety escalations for mental health crises and clear boundaries against providing professional medical, legal, or financial advice.
能力评估
Purpose & Capability
Name/description (daily trivial decisions) match the provided SKILL.md and the included local reference JSON files (food, outfits, activities, purchase guides). There are no unrelated env vars, binaries, or config paths requested.
Instruction Scope
Runtime instructions are limited to producing recommendations using templates and the included data files. The SKILL.md does not instruct the agent to read system files, access credentials, or transmit data to external endpoints. Occasional prompts (e.g., 'find specific locations near you') are user-facing suggestions, not baked-in network calls.
Install Mechanism
No install spec or downloadable code; this is an instruction-only skill with bundled JSON data. Nothing will be written to disk or fetched at install time.
Credentials
The skill declares no required environment variables, credentials, or config paths. The content expects optional user inputs (budget, weather, recipient info) but does not request system secrets or unrelated tokens.
Persistence & Privilege
always is false and model invocation is allowed (default). The skill does not request special persistent privileges or modifications to other skills/configs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install decision-fatigue-reliever
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /decision-fatigue-reliever 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.5
Translate the remaining Chinese safety and disclaimer text in SKILL.md to English.
v1.0.4
Publish the English SKILL.md metadata so the ClawHub listing summary appears in English.
v1.0.3
Fix identity.json and workspace consistency, add proper metadata
v1.0.2
Translated examples.md to English
v1.0.1
Translated documentation to English
v1.0.0
Initial release
元数据
Slug decision-fatigue-reliever
版本 1.0.5
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 6
常见问题

Decision Fatigue Reliever 是什么?

Helps you make quick decisions on daily trivial matters (food, clothing, activities, shopping), reducing choice anxiety and freeing up cognitive resources. U... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 293 次。

如何安装 Decision Fatigue Reliever?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install decision-fatigue-reliever」即可一键安装,无需额外配置。

Decision Fatigue Reliever 是免费的吗?

是的,Decision Fatigue Reliever 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Decision Fatigue Reliever 支持哪些平台?

Decision Fatigue Reliever 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Decision Fatigue Reliever?

由 haidong(@harrylabsj)开发并维护,当前版本 v1.0.5。

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