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harrylabsj

Decision Fatigue Reliever

by haidong · GitHub ↗ · v1.0.5 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install decision-fatigue-reliever
Description
Helps you make quick decisions on daily trivial matters (food, clothing, activities, shopping), reducing choice anxiety and freeing up cognitive resources. U...
README (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

Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install decision-fatigue-reliever
  3. After installation, invoke the skill by name or use /decision-fatigue-reliever
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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
Metadata
Slug decision-fatigue-reliever
Version 1.0.5
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 6
Frequently Asked Questions

What is Decision Fatigue Reliever?

Helps you make quick decisions on daily trivial matters (food, clothing, activities, shopping), reducing choice anxiety and freeing up cognitive resources. U... It is an AI Agent Skill for Claude Code / OpenClaw, with 293 downloads so far.

How do I install Decision Fatigue Reliever?

Run "/install decision-fatigue-reliever" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Decision Fatigue Reliever free?

Yes, Decision Fatigue Reliever is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Decision Fatigue Reliever support?

Decision Fatigue Reliever is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Decision Fatigue Reliever?

It is built and maintained by haidong (@harrylabsj); the current version is v1.0.5.

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