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skills/research/RESEARCH_ENHANCEMENT.md

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+# Research Skill Enhancement - Requirements
+
+## Core Principles
+
+1. **Discovery-driven, not list-driven** - Find best sources wherever they are
+2. **Context-aware** - Grounded in user's domain preferences
+3. **Finds unknowns** - Value is in discovering what user doesn't know
+4. **No time limits** - Can run 30 min to 3 hours
+5. **Exhaustive** - Don't stop until topic is exhausted
+
+## Domain Contexts
+
+### What Context Provides
+- **Relevance filtering** - what matters to this user
+- **Constraint awareness** - their specific resources/limitations
+- **Optimization targets** - what to maximize for them
+
+### Domain: Travel
+- Loyalty: Alaska MVP Gold 75K, Marriott Titanium Elite
+- Points: Current balances (fetch via browser-control)
+- Credit cards: Alaska card, Marriott Amex, Chase Sapphire Preferred
+- Memberships: Epic Pass
+- Preferences: "Luxury at rock-bottom prices", never book only research
+
+### Domain: Shopping
+- Quality standards
+- Budget ranges
+- Brand preferences
+
+### Domain: Work/Education
+- PSD context (CIO role)
+- UDL expertise
+- Edtech landscape
+- District priorities
+
+### Domain: AI/Coding
+- Tech stack preferences
+- Languages/frameworks
+- Architecture patterns
+
+### Domain: Consulting
+- Service offerings
+- Client contexts
+
+## Research Engine Architecture
+
+### Phase 1: Query Understanding
+- Detect domain from query
+- Load relevant context
+- Decompose into sub-questions (5-10 angles)
+- Identify what types of sources needed (academic, forum, video, etc.)
+
+### Phase 2: Parallel Multi-Source Discovery
+- Launch multiple search strategies simultaneously:
+  - Multiple search engines (Google, Bing, DuckDuckGo)
+  - Multiple query formulations
+  - Different source types (articles, forums, videos, podcasts)
+- Use multiple LLMs for different perspectives:
+  - Perplexity: Current web info with citations
+  - Gemini: Multi-perspective synthesis
+  - OpenAI: Structured analysis
+  - Claude: Deep reasoning
+
+### Phase 3: Source Evaluation & Expansion
+- Evaluate each source for:
+  - Credibility (author expertise, publication reputation)
+  - Recency (when published/updated)
+  - Depth (surface vs comprehensive)
+  - Citations (does it cite others? is it cited?)
+- Follow promising leads:
+  - Sources referenced by good sources
+  - Authors who appear multiple times
+  - Cross-referenced claims
+
+### Phase 4: Deep Scraping
+- Don't stop at first page of results
+- Go 5-10 pages deep on good queries
+- Use browser-control for:
+  - JS-rendered content (Reddit, forums)
+  - Authenticated pages (user accounts)
+  - Sites that block scrapers
+- Follow internal links on valuable sources
+
+### Phase 5: Multimedia Discovery
+- YouTube videos
+- TikTok content
+- Podcasts (Spotify, Apple)
+- Not just text articles
+
+### Phase 6: Synthesis
+- Organize by theme/question
+- Every claim cited
+- Note consensus vs disagreement
+- Include multimedia resources
+- Provide actionable recommendations
+- Flag what's still uncertain
+
+## Output Format
+
+```markdown
+## Research: [Topic]
+
+### Context Applied
+- [Domain context that was loaded]
+- [Dynamic data fetched - e.g., current point balances]
+
+### Executive Summary
+[2-3 paragraph overview of key findings]
+
+### Detailed Findings
+
+#### [Sub-topic 1]
+[Deep analysis with inline citations]
+
+#### [Sub-topic 2]
+[Deep analysis with inline citations]
+
+### Multimedia Resources
+- [Video: Title](url) - description
+- [Podcast: Title](url) - description
+- [TikTok: @user](url) - description
+
+### Recommendations
+1. [Actionable recommendation based on user's context]
+2. [Another recommendation]
+
+### What I Discovered You Might Not Know
+- [Surprising finding 1]
+- [Surprising finding 2]
+
+### Confidence Assessment
+- High confidence: [topics]
+- Needs verification: [topics]
+- Conflicting information: [topics]
+
+### All Sources
+[Complete list of every URL referenced]
+```
+
+## Technical Implementation
+
+### Files to Create/Modify
+- [ ] Update preferences.json with domain contexts
+- [ ] Create domain context loader
+- [ ] Create multi-LLM research orchestrator
+- [ ] Create source evaluator
+- [ ] Create deep scraper (uses browser-control)
+- [ ] Create multimedia searcher
+- [ ] Create synthesis engine
+- [ ] Update SKILL.md with new architecture
+
+### API Keys Needed
+- PERPLEXITY_API_KEY
+- GEMINI_API_KEY
+- OPENAI_API_KEY
+- (Claude runs natively)
+
+### Browser-Control Integration
+- Fetch current balances (Marriott, Alaska, Chase)
+- Scrape full forum threads
+- Access authenticated content
+- Handle JS-rendered sites
+
+## Success Criteria
+
+1. Can run a travel research query and get:
+   - Current point balances fetched
+   - Transfer bonus opportunities identified
+   - 5-6 hotel options with points AND cash prices
+   - Flight options with miles needed
+   - Deep user reviews from forums
+   - Video content discovered
+   - Optimization recommendations
+
+2. Report takes 30-60 minutes to generate (not 5 minutes)
+
+3. Sources include things user didn't know existed
+
+4. Every claim is cited
+
+5. Recommendations are specific to user's context