gh-krishagel-geoffrey/skills/research/RESEARCH_ENHANCEMENT.md

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

## 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