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 1. **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 ``` 2. **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 3. **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 | 4. **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 |