Decision Mode

Activate when the user asks a question that requires judgment, choice, or decision-making. This skill helps provide structured decision support by analyzing from both AI perspective and user's perspective, with confidence levels and confidence ratings to help users assess the certainty of conclusions.

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Install

openclaw skills install decision-mode

Decision Mode 🎯

A structured framework for providing decision support with confidence assessment.

When to Activate

Activate this skill when:

User asks "我应该...吗？" / "Should I...?"
User asks for advice on choices or options
User presents a dilemma or trade-off
User asks for predictions or forecasts
User asks "哪个更好？" / "Which is better?"
Any question requiring judgment or subjective assessment

⚠️ CRITICAL: Before activating, determine if information gathering is needed:

Does this involve current market conditions? → Search first
Does this involve recent events or trends? → Search first
Does this involve time-sensitive data? → Search first
Is this a general principle question? → Can proceed without search

Decision Framework

Step 0: Information Gathering (CRITICAL)

⚠️ BEFORE providing any analysis, you MUST gather current information.

When to Search

Activate information gathering when the decision involves:

Market conditions (stocks, crypto, real estate, job market)
Current events (policy changes, industry trends, company news)
Time-sensitive factors (economic data, seasonal patterns, deadlines)
Rapidly changing domains (technology, regulations, competitive landscape)
Location-specific information (local laws, market conditions, opportunities)

Information Gathering Process

Identify Key Information Needs

For decision "X", I need to know:
- Current market/industry status
- Recent trends or changes
- Relevant data or statistics
- Expert opinions or consensus

Execute Search Strategy
- Use web_search for broad trends and recent news
- Use web_fetch for specific articles or data sources
- Use browser if real-time data needed (prices, job listings, etc.)
- Check multiple sources for conflicting information

Assess Information Quality

Source Type	Reliability	Use For
Official data (gov, exchanges)	High	Facts, statistics
Major news outlets	High-Medium	Current events
Industry reports	Medium	Trends, forecasts
Social media/forums	Low-Medium	Sentiment, anecdotes
Personal blogs	Low	Alternative views

Document Information Gaps
- Note what you couldn't find
- Acknowledge conflicting sources
- Adjust confidence downward when information is incomplete

Search Result Integration

After gathering information, structure your analysis:

### 📊 Information Landscape

**Key Findings:**
- [Finding 1 from search with source]
- [Finding 2 from search with source]
- [Finding 3 from search with source]

**Information Gaps:**
- [What you couldn't find]
- [Conflicting information between sources]

**Source Reliability:**
- High: [Official/expert sources]
- Medium: [News/industry sources]
- Low: [Opinion/social sources]

Step 1: Identify Decision Type

Type	Description	Example
Binary	Yes/No decision	"Should I quit my job?"
Multi-choice	Select from options	"Which laptop should I buy?"
Trade-off	Balance competing factors	"Work-life balance vs career growth"
Prediction	Forecast future outcome	"Will the stock market crash?"
Risk assessment	Evaluate potential downsides	"Is this investment safe?"

Step 2: Dual Perspective Analysis

For every decision, provide TWO perspectives:

🤖 AI Perspective (Objective Analysis)

Based on gathered information + training data patterns
Considers typical outcomes and probabilities
References similar cases or established best practices
Explicitly cites sources for key claims
Acknowledges limitations of training data AND information gaps

⚠️ CRITICAL: If you did NOT search for current information, state clearly:

Note: This analysis is based on general patterns from training data. For time-sensitive decisions, current market/condition data should be verified.

👤 User Perspective (Subjective Analysis)

Consider user's specific context from conversation history
Factor in user's stated preferences, values, constraints
Account for user's risk tolerance (if known)
Respect user's unique circumstances

Step 2.5: Information Quality Assessment

Before assigning confidence, evaluate:

Factor	Impact on Confidence
Information freshness	Older data = lower confidence
Source diversity	Single source = lower confidence
Source authority	Official > News > Opinion
Conflicting signals	Conflicts = lower confidence
Information completeness	Gaps = lower confidence
Personal knowledge cutoff	Post-cutoff events = lower confidence

Confidence Adjustment Rules:

No search performed on time-sensitive topic: Max confidence C (50-69%)
Single source: Reduce by 1 grade
Conflicting sources without resolution: Reduce by 1-2 grades
Information >6 months old: Reduce by 1 grade

Step 3: Confidence Assessment

Confidence Score (0-100%)

Score	Interpretation
90-100%	Very High - Strong evidence, clear consensus
70-89%	High - Good evidence, minor uncertainties
50-69%	Moderate - Mixed evidence, reasonable assumptions
30-49%	Low - Limited evidence, significant uncertainty
0-29%	Very Low - Highly speculative, major unknowns

Confidence Level (A-F Rating)

Rating	Criteria	Action for User
A (90-100%)	Multiple reliable sources, clear patterns, strong consensus	Can rely on this conclusion
B (70-89%)	Good sources, minor gaps, generally reliable	Reliable but verify key facts
C (50-69%)	Some evidence, reasonable assumptions, mixed signals	Consider as one factor among many
D (30-49%)	Limited evidence, significant assumptions	Treat as tentative, seek more info
F (0-29%)	Mostly speculation, major unknowns	Do not rely on this conclusion

Step 4: Structured Output Format

## 🎯 Decision Analysis: [Brief Title]

### 📋 Decision Type: [Binary/Multi-choice/Trade-off/Prediction/Risk]

---

### 🤖 AI Perspective (Objective)

**Analysis:**
[2-3 sentences of objective analysis based on data/patterns]

**Conclusion:**
[Clear statement of what the data suggests]

**Confidence:** XX% (Grade X)
- **Basis:** [Why this confidence level - what evidence supports it]
- **Limitations:** [What could change this conclusion]

---

### 👤 User Perspective (Subjective)

**Context Considerations:**
- [Factor 1 from user's situation]
- [Factor 2 from user's situation]
- [Factor 3 from user's situation]

**Personalized Conclusion:**
[How the general advice applies specifically to this user]

**Confidence:** XX% (Grade X)
- **Basis:** [Why this confidence level given user's context]
- **Unknowns:** [What user information would improve confidence]

---

### ⚖️ Synthesis

| Factor | AI View | User View | Alignment |
|--------|---------|-----------|-----------|
| [Key factor 1] | [AI assessment] | [User-specific] | ✅/⚠️/❌ |
| [Key factor 2] | [AI assessment] | [User-specific] | ✅/⚠️/❌ |

**Overall Recommendation:**
[Clear, actionable recommendation]

**Confidence Summary:**
- AI Confidence: XX% (Grade X)
- User Confidence: XX% (Grade X)
- **Combined: XX% (Grade X)** ← Most important number

---

### ⚠️ Caveats & Next Steps

**What could change this:**
- [Factor that would increase confidence]
- [Factor that would change recommendation]

**Recommended next steps:**
1. [Specific action to gather more info]
2. [Specific action to reduce risk]
3. [Specific action to validate assumption]

**Disclaimer:**
This analysis is for informational purposes only. Final decisions should consider your complete personal circumstances and, where appropriate, professional advice.

Special Cases

When User Context is Unknown

If you don't have enough information about the user's specific situation:

User Perspective: Limited information available about your specific circumstances. The following assumes typical preferences - please share more details for a personalized analysis.

Confidence for User Perspective should be D or F when context is unknown.

When Evidence is Contradictory

Present both sides clearly:

Conflicting Evidence:

Pro: [Evidence supporting conclusion X]

Con: [Evidence supporting conclusion Y]

Resolution: [How you weighed the evidence]

When Decision Involves Ethics/Values

Be explicit about value judgments:

Value Assumption: This recommendation assumes [value X] is more important than [value Y]. If you prioritize differently, the conclusion may change.

Examples

Example 1: Career Decision

User: "Should I accept a job offer with 30% higher pay but longer hours?"

Output:

## 🎯 Decision Analysis: Job Offer Evaluation

### 📋 Decision Type: Trade-off (Compensation vs Work-Life Balance)

---

### 🤖 AI Perspective (Objective)

**Analysis:**
Research indicates that beyond a certain income threshold (varies by location, roughly $75-100K in US), additional income shows diminishing returns on life satisfaction. However, career trajectory impact of early high-paying roles can compound significantly over 10-20 years.

**Conclusion:**
If the user is early in career (< 35 years old), the long-term career capital likely outweighs short-term lifestyle costs. If mid-to-late career, work-life balance becomes relatively more important.

**Confidence:** 65% (Grade C)
- **Basis:** General research on income/happiness correlation and career trajectory patterns
- **Limitations:** Individual variation is enormous; industry-specific factors not considered

---

### 👤 User Perspective (Subjective)

**Context Considerations:**
- User mentioned being 28 years old (early career)
- User previously expressed interest in "building wealth quickly"
- User has no dependents mentioned
- Current role described as "comfortable but stagnant"

**Personalized Conclusion:**
Given your age, stated financial goals, and current career stagnation, the new role aligns well with your expressed priorities. The lifestyle trade-off is likely acceptable given your current life stage.

**Confidence:** 72% (Grade B)
- **Basis:** Good alignment between offer and stated goals
- **Unknowns:** Actual work hours undefined; health impact unclear; partner/family preferences unknown

---

### ⚖️ Synthesis

| Factor | AI View | User View | Alignment |
|--------|---------|-----------|-----------|
| Career timing | Early career = take risks | Early career, wants growth | ✅ Strong |
| Financial priority | Moderate importance | High importance | ✅ Strong |
| Work-life balance | Important long-term | Not stated as priority | ⚠️ Unclear |
| Risk tolerance | Varies individually | Seems moderate-high | ✅ Likely |

**Overall Recommendation:**
**ACCEPT the offer**, with conditions: negotiate start date for transition time; set 6-month review checkpoint; maintain strict boundaries on hours where possible.

**Confidence Summary:**
- AI Confidence: 65% (Grade C)
- User Confidence: 72% (Grade B)
- **Combined: 68% (Grade C+)**

---

### ⚠️ Caveats & Next Steps

**What could change this:**
- If actual hours exceed 60/week consistently
- If health/relationship impacts emerge
- If better offer materializes within 3 months

**Recommended next steps:**
1. Ask current employer for counter-offer (market test)
2. Clarify exact expectations and hours with new employer
3. Set date 6 months out to reassess

Example 2: Simple Factual Question

User: "Is Python better than JavaScript for data science?"

Output:

## 🎯 Decision Analysis: Language Choice for Data Science

### 📋 Decision Type: Multi-choice (Tool Selection)

---

### 🤖 AI Perspective (Objective)

**Analysis:**
Python dominates data science ecosystems with libraries (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow) that have no JavaScript equivalents in maturity or performance. JavaScript's data science ecosystem (TensorFlow.js, Danfo.js) is nascent and primarily targets web deployment, not analysis.

**Conclusion:**
Python is objectively superior for data science work in virtually all dimensions: library ecosystem, performance, community support, job market.

**Confidence:** 95% (Grade A)
- **Basis:** Market data, library maturity metrics, job posting analysis, performance benchmarks
- **Limitations:** Specific use cases (web-embedded ML) may favor JavaScript

---

### 👤 User Perspective (Subjective)

**Context Considerations:**
- No specific user context provided
- Assuming general data science goals

**Personalized Conclusion:**
Without knowing your specific constraints (team requirements, deployment targets, existing skills), the general recommendation is Python.

**Confidence:** 85% (Grade B) → reduced due to unknown context
- **Basis:** Strong general case, but individual circumstances vary
- **Unknowns:** Your current skills, team standards, deployment requirements

---

### ⚖️ Synthesis

| Factor | AI View | User View | Alignment |
|--------|---------|-----------|-----------|
| Library ecosystem | Python dominant | N/A | ✅ |
| Performance | Python better | N/A | ✅ |
| Job market | Python preferred | N/A | ✅ |

**Overall Recommendation:**
Use **Python** for data science. Only consider JavaScript if: (1) your team mandates it, (2) you're deploying to web browsers, or (3) you're building a web app with light ML features.

**Confidence Summary:**
- AI Confidence: 95% (Grade A)
- User Confidence: 85% (Grade B)
- **Combined: 90% (Grade A)**

Confidence Calibration Guide

Overconfidence Traps to Avoid

❌ Don't say: "You should definitely do X" ✅ Do say: "Based on [evidence], X appears to be the better option with 75% confidence"

❌ Don't say: "The answer is obviously Y" ✅ Do say: "Y is supported by [factors], though Z is also reasonable if you prioritize [different factor]"

❌ Don't say: "I'm certain that..." ✅ Do say: "The evidence strongly suggests... (Grade A, 92% confidence)"

Underconfidence to Avoid

Don't be so cautious that the analysis becomes useless:

❌ Weak: "Both options have pros and cons, it depends on your preferences" ✅ Stronger: "Option A is better for [specific scenario], Option B for [specific scenario]. Given [user's stated priority], A is recommended with 70% confidence"

Final Checklist

Before providing decision analysis, verify:

Information Gathering

Determined if search is needed (time-sensitive? market-dependent?)
Performed search if needed (web_search, web_fetch, browser)
Assessed source quality (official > news > opinion)
Noted information gaps and conflicting sources
Documented findings in Information Landscape section

Analysis Quality

Red Flags to Avoid

Did NOT make time-sensitive claims without current data
Did NOT present training data as current market reality
Did NOT hide information gaps
Did NOT overstate confidence when sources are weak