Research Information Lookup

Overview

This skill provides multi-provider research lookup with intelligent routing between:

• Gemini Deep Research: 60-minute comprehensive research with extensive citations (requires GEMINI_API_KEY + Google AI Pro subscription)
• Perplexity Sonar: Fast web-grounded research in 30 seconds (requires OPENROUTER_API_KEY)

The skill automatically selects the best provider and model based on:

• Research mode configuration (balanced, perplexity, deep_research, auto)
• Query complexity (keywords, length, structure)
• Context (planning phase, task type)

Research Modes

Balanced (Recommended)

• Use Deep Research for Phase 1 market analysis and competitive landscape
• Use Perplexity for quick fact lookups and technology verification
• Best quality/time tradeoff: Comprehensive where it matters, fast elsewhere
• Total plan time: ~90 minutes

Quick (Perplexity Only)

• Fast 30-second lookups for all research queries
• Total plan time: ~30 minutes
• Good for well-known tech stacks and familiar domains

Comprehensive (Deep Research for All)

• 60-minute deep research for every major decision
• Total plan time: ~4 hours
• Best for novel technologies, uncertain markets, or high-stakes projects

Auto (Context-Aware)

• Automatically selects based on query keywords and planning phase
• Uses Deep Research for: competitive analysis, market landscape, architecture decisions
• Uses Perplexity for: quick facts, pricing, version numbers, simple comparisons

⚠️ Deep Research Budget Constraints

CRITICAL: You have a strict budget of 2 Deep Research queries per /full-plan session.

Budget Allocation Strategy

Deep Research is expensive (30-60 min per query, high API cost). Use it ONLY for:

1. Phase 1: Competitive Landscape/Analysis (Highest Priority)

   • Comprehensive market analysis with multiple competitors
   • Industry trends and adoption patterns
   • Regulatory landscape with complex timelines

2. Phase 2: Novel Architecture Decisions (Use Sparingly)

   • ONLY if technology stack is highly uncertain or cutting-edge
   • ONLY if multiple architectural approaches need deep comparison
   • DEFAULT to Gemini Pro or Perplexity for standard tech stack research

DO NOT Use Deep Research For:

❌ Version checks or feature comparisons (use Perplexity) ❌ Pricing lookups or cost estimates (use Perplexity) ❌ Quick technical documentation (use Perplexity) ❌ Simple "what is X" queries (use Gemini Flash/Perplexity) ❌ Phases 3-6 research (use Perplexity - better temporal accuracy)

Recommended Budget Allocation

Conservative (Recommended):

• 1 Deep Research: Phase 1 competitive landscape
• 1 Deep Research: Phase 1 regulatory timeline OR Phase 2 architecture (if novel)
• All others: Perplexity or Gemini Pro

Aggressive (High-Stakes Projects):

• 2 Deep Research: Phase 1 market analysis queries
• All others: Perplexity or Gemini Pro

Budget Tracking

The system automatically tracks your Deep Research usage:

• planning_outputs/<project>/DEEP_RESEARCH_BUDGET.json contains the budget state
• Progress warnings appear when approaching limit: ⚠️ 1/2 Deep Research queries used
• System falls back to Gemini Pro if budget exhausted

Before using Deep Research, ask yourself:

1. Is this query critical to project viability/direction?
2. Does it require 30-60 min comprehensive multi-source analysis?
3. Can Perplexity or Gemini Pro provide sufficient depth?

Remember: Perplexity has better temporal accuracy for 2026 data, so prefer it for time-sensitive queries even in Phase 1.

When to Use This Skill

Use this skill when you need:

• Current Research Information: Latest studies, papers, and findings in a specific field
• Literature Verification: Check facts, statistics, or claims against current research
• Background Research: Gather context and supporting evidence for project planning
• Citation Sources: Find relevant papers and studies to cite in manuscripts
• Technical Documentation: Look up specifications, protocols, or methodologies
• Recent Developments: Stay current with emerging trends and breakthroughs
• Statistical Data: Find recent statistics, survey results, or research findings
• Expert Opinions: Access insights from recent interviews, reviews, or commentary

Visual Enhancement with Project Diagrams

When creating documents with this skill, always consider adding diagrams to enhance visual communication.

If your document does not already contain diagrams:

• Use the project-diagrams skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Nano Banana Pro will automatically generate, review, and refine the diagram

For project planning documents: Diagrams should be generated by default to visually represent system architectures, workflows, data flows, or relationships described in the text.

How to generate schematics:

python .claude/skills/project-diagrams/scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

When to add diagrams:

• System architecture diagrams
• Data flow illustrations
• Integration workflow diagrams
• Process pipeline visualizations
• Component relationship frameworks
• Any complex concept that benefits from visualization

For detailed guidance on creating diagrams, refer to the project-diagrams skill documentation.

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Usage

Command-Line Interface

# Basic usage with auto mode (context-aware selection)
python research_lookup.py "Your research query here"

# Specify research mode explicitly
python research_lookup.py "Competitive landscape for SaaS market" \
  --research-mode deep_research

# Provide context for smart routing
python research_lookup.py "Latest PostgreSQL features" \
  --research-mode balanced \
  --phase 2 \
  --task-type architecture-research

# Force specific Perplexity model
python research_lookup.py "Quick fact check" \
  --research-mode perplexity \
  --force-model pro

Research Mode Options

Mode	Provider Selection	Best For
balanced	Deep Research for Phase 1 analysis, Perplexity for others	Most projects (recommended)
perplexity	Always use Perplexity	Quick planning, well-known tech
deep_research	Always use Gemini Deep Research	Novel domains, high-stakes
auto	Automatic based on keywords/context	Let the system decide

Context Parameters

Phase-based routing:

• --phase 1 with --task-type competitive-analysis → triggers Deep Research in balanced/auto modes
• --phase 2 with keywords like "architecture decision" → may trigger Deep Research
• Other phases → generally use Perplexity unless query is complex

Example in planning workflow:

# Phase 1: Competitive analysis (use Deep Research)
python research_lookup.py "Comprehensive competitive analysis for task management SaaS" \
  --research-mode balanced \
  --phase 1 \
  --task-type competitive-analysis

# Phase 2: Quick tech lookup (use Perplexity)
python research_lookup.py "Latest React best practices 2026" \
  --research-mode balanced \
  --phase 2 \
  --task-type research-lookup

API Requirements

For Perplexity (required for perplexity and balanced modes):

export OPENROUTER_API_KEY='your_openrouter_key'

For Gemini Deep Research (required for deep_research and balanced modes):

export GEMINI_API_KEY='your_gemini_key'
# Requires Google AI Pro subscription ($19.99/month)

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Progress Tracking & Monitoring (v1.4.0+)

For long-running Deep Research operations (60+ minutes), the plugin provides comprehensive progress tracking and checkpoint capabilities.

Real-Time Progress Monitoring

When research operations take longer than 30 seconds, progress tracking is automatically enabled:

Tier 1: Streaming Progress (Perplexity ~30s)

• Real-time event callbacks for instant feedback
• No external monitoring needed

Tier 2: Progress Files (Deep Research ~60 min)

• JSON progress tracking with checkpoint history
• External monitoring from separate terminal
• Resume capability if interrupted

Monitor Active Research

Monitor long-running research from a separate terminal:

# List all active research operations
python scripts/monitor-research-progress.py <project_folder> --list

# Monitor specific operation with live updates
python scripts/monitor-research-progress.py <project_folder> <task_id> --follow

# Example output:
# [14:23:45] 🔄 [████████████░░░░░] 30% | analyzing: Cross-referencing...
# [14:38:12] 🔄 [████████████████░░] 50% | synthesizing: Results...
# [14:52:30] ✅ [████████████████████] 100% | Complete!

Resume Interrupted Research

If Deep Research is interrupted (network issues, timeout), resume from checkpoints:

# List resumable tasks with time estimates
python scripts/resume-research.py <project_folder> 1 --list

# Resume from checkpoint (saves up to 50 minutes)
python scripts/resume-research.py <project_folder> 1 --task <task_name>

Checkpoint Strategy:

• 15% checkpoint: ~9 minutes saved if interrupted
• 30% checkpoint: ~18 minutes saved if interrupted
• 50% checkpoint: ~30 minutes saved if interrupted

Enhanced Research Integration

For Python API usage with full progress tracking:

import asyncio
from pathlib import Path
from enhanced_research_integration import EnhancedResearchLookup

async def main():
    # Initialize with progress tracking
    research = EnhancedResearchLookup(
        project_folder=Path("planning_outputs/20260115_my-project"),
        phase_num=1,
        research_mode="balanced"  # or "quick", "deep_research", "auto"
    )

    # Execute with automatic progress tracking and checkpoints
    result = await research.research_with_progress(
        task_name="competitive-analysis",
        query="Comprehensive competitive landscape analysis",
        estimated_duration_sec=3600  # Auto-detected if not provided
    )

    # Access results and statistics
    print(f"Success: {result['success']}")
    print(f"Provider: {result['provider']}")
    print(f"Sources: {len(result.get('sources', []))}")

    # View execution statistics
    stats = research.get_stats()
    print(f"Tasks completed: {stats['tasks_completed']}")
    print(f"Time saved: {stats['total_time_saved_min']} minutes")

asyncio.run(main())

Key Features:

• Automatic checkpoint creation at 15%, 30%, 50% milestones
• Graceful degradation (Deep Research → Perplexity fallback)
• Error recovery with exponential backoff
• External monitoring support via progress files
• Statistics tracking across all research operations

See Also:

• docs/WORKFLOWS.md - Complete workflow examples with dual-terminal monitoring
• scripts/enhanced_research_integration.py - Integration layer implementation
• scripts/resumable_research.py - Core resumable research executor

---

Core Capabilities

1. Academic Research Queries

Search Academic Literature: Query for recent papers, studies, and reviews in specific domains:

Query Examples:
- "Recent advances in CRISPR gene editing 2024"
- "Latest clinical trials for Alzheimer's disease treatment"
- "Machine learning applications in drug discovery systematic review"
- "Climate change impacts on biodiversity meta-analysis"

Expected Response Format:

• Summary of key findings from recent literature
• Citation of 3-5 most relevant papers with authors, titles, journals, and years
• Key statistics or findings highlighted
• Identification of research gaps or controversies
• Links to full papers when available

2. Technical and Methodological Information

Protocol and Method Lookups: Find detailed procedures, specifications, and methodologies:

Query Examples:
- "Western blot protocol for protein detection"
- "RNA sequencing library preparation methods"
- "Statistical power analysis for clinical trials"
- "Machine learning model evaluation metrics"

Expected Response Format:

• Step-by-step procedures or protocols
• Required materials and equipment
• Critical parameters and considerations
• Troubleshooting common issues
• References to standard protocols or seminal papers

3. Statistical and Data Information

Research Statistics: Look up current statistics, survey results, and research data:

Query Examples:
- "Prevalence of diabetes in US population 2024"
- "Global renewable energy adoption statistics"
- "COVID-19 vaccination rates by country"
- "AI adoption in healthcare industry survey"

Expected Response Format:

• Current statistics with dates and sources
• Methodology of data collection
• Confidence intervals or margins of error when available
• Comparison with previous years or benchmarks
• Citations to original surveys or studies

4. Citation and Reference Assistance

Citation Finding: Locate the most influential, highly-cited papers from reputable authors and prestigious venues:

Query Examples:
- "Foundational papers on transformer architecture" (expect: Vaswani et al. 2017 in NeurIPS, 90,000+ citations)
- "Seminal works in quantum computing" (expect: papers from Nature, Science by leading researchers)
- "Key studies on climate change mitigation" (expect: IPCC-cited papers, Nature Climate Change)
- "Landmark trials in cancer immunotherapy" (expect: NEJM, Lancet trials with 1000+ citations)

Expected Response Format:

• 5-10 most influential papers, ranked by impact and relevance
• Complete citation information (authors, title, journal, year, DOI)
• Citation count for each paper (approximate if exact unavailable)
• Venue tier indication (Nature, Science, Cell = Tier 1, etc.)
• Brief description of each paper's contribution
• Author credentials when notable (e.g., "from the Hinton lab", "Nobel laureate")
• Journal impact factors when relevant

Quality Criteria for Citation Selection:

• Prefer papers with 100+ citations (for papers 3+ years old)
• Prioritize Tier-1 journals (Nature, Science, Cell, NEJM, Lancet)
• Include work from recognized leaders in the field
• Balance foundational papers (high citations, older) with recent advances (emerging, high-impact venues)

Automatic Model Selection

This skill features intelligent model selection based on query complexity:

Model Types

1. Sonar Pro (perplexity/sonar-pro)

• Use Case: Straightforward information lookup
• Context: 200K tokens
• Pricing: $3/1M prompt + $15/1M completion + $5/1K searches
• Best For:
  • Simple fact-finding queries
  • Recent publication searches
  • Basic protocol lookups
  • Statistical data retrieval
• Speed: Fast responses

2. Sonar Reasoning Pro (perplexity/sonar-reasoning-pro)

• Use Case: Complex analytical queries requiring deep reasoning
• Model: Powered by DeepSeek R1 with Chain of Thought
• Context: 128K tokens
• Pricing: $2/1M prompt + $8/1M completion + $5/1K searches
• Best For:
  • Comparative analysis ("compare X vs Y")
  • Synthesis of multiple studies
  • Evaluating trade-offs or controversies
  • Explaining mechanisms or relationships
  • Critical analysis and interpretation
• Speed: Slower but more thorough

Complexity Assessment

The skill automatically detects query complexity using these indicators:

Reasoning Keywords (triggers Sonar Reasoning Pro):

• Analytical: compare, contrast, analyze, analysis, evaluate, critique
• Comparative: versus, vs, vs., compared to, differences between, similarities
• Synthesis: meta-analysis, systematic review, synthesis, integrate
• Causal: mechanism, why, how does, how do, explain, relationship, causal relationship, underlying mechanism
• Theoretical: theoretical framework, implications, interpret, reasoning
• Debate: controversy, conflicting, paradox, debate, reconcile
• Trade-offs: pros and cons, advantages and disadvantages, trade-off, tradeoff, trade offs
• Complexity: multifaceted, complex interaction, critical analysis

Complexity Scoring:

• Reasoning keywords: 3 points each (heavily weighted)
• Multiple questions: 2 points per question mark
• Complex sentence structures: 1.5 points per clause indicator (and, or, but, however, whereas, although)
• Very long queries: 1 point if >150 characters
• Threshold: Queries scoring ≥3 points trigger Sonar Reasoning Pro

Practical Result: Even a single strong reasoning keyword (compare, explain, analyze, etc.) will trigger the more powerful Sonar Reasoning Pro model, ensuring you get deep analysis when needed.

Example Query Classification:

✅ Sonar Pro Search (straightforward lookup):

• "Recent advances in CRISPR gene editing 2024"
• "Prevalence of diabetes in US population"
• "Western blot protocol for protein detection"

✅ Sonar Reasoning Pro (complex analysis):

• "Compare and contrast mRNA vaccines vs traditional vaccines for cancer treatment"
• "Explain the mechanism underlying the relationship between gut microbiome and depression"
• "Analyze the controversy surrounding AI in medical diagnosis and evaluate trade-offs"

Manual Override

You can force a specific model using the force_model parameter:

# Force Sonar Pro Search for fast lookup
research = ResearchLookup(force_model='pro')

# Force Sonar Reasoning Pro for deep analysis
research = ResearchLookup(force_model='reasoning')

# Automatic selection (default)
research = ResearchLookup()

Command-line usage:

# Force Sonar Pro Search
python research_lookup.py "your query" --force-model pro

# Force Sonar Reasoning Pro
python research_lookup.py "your query" --force-model reasoning

# Automatic (no flag)
python research_lookup.py "your query"

# Save output to a file
python research_lookup.py "your query" -o results.txt

# Output as JSON (useful for programmatic access)
python research_lookup.py "your query" --json

# Combine: JSON output saved to file
python research_lookup.py "your query" --json -o results.json

Technical Integration

OpenRouter API Configuration

This skill integrates with OpenRouter (openrouter.ai) to access Perplexity's Sonar models:

Model Specifications:

• Models:
  • perplexity/sonar-pro (fast lookup, 200K context)
  • perplexity/sonar-reasoning-pro (deep analysis with DeepSeek R1, 128K context)
• Search Mode: Academic/scholarly mode (prioritizes peer-reviewed sources)
• Search Context: Always uses high search context for deeper, more comprehensive research results
• Context Window: 128-200K tokens depending on model
• Capabilities: Academic paper search, citation generation, scholarly analysis
• Output: Rich responses with citations and source links from academic databases
• Pricing: $2-3/1M input + $8-15/1M output + $5/1K searches

API Requirements:

• OpenRouter API key (set as OPENROUTER_API_KEY environment variable)
• Account with sufficient credits for research queries
• Proper attribution and citation of sources

Python Dependencies (for CLI usage): If using the research_lookup.py script directly, install dependencies:

pip install requests
# Or install all plugin dependencies:
pip install -r requirements.txt

Academic Mode Configuration:

• System message configured to prioritize scholarly sources
• Search focused on peer-reviewed journals and academic publications
• Enhanced citation extraction for academic references
• Preference for recent academic literature (2020-2024)
• Direct access to academic databases and repositories

Response Quality and Reliability

Source Verification: The skill prioritizes:

• Peer-reviewed academic papers and journals
• Reputable institutional sources (universities, government agencies, NGOs)
• Recent publications (within last 2-3 years preferred)
• High-impact journals and conferences
• Primary research over secondary sources

Citation Standards: All responses include:

• Complete bibliographic information
• DOI or stable URLs when available
• Access dates for web sources
• Clear attribution of direct quotes or data

Paper Quality and Popularity Prioritization

CRITICAL: When searching for papers, ALWAYS prioritize high-quality, influential papers over obscure or low-impact publications. Quality matters more than quantity.

Citation-Based Ranking

Prioritize papers based on citation count relative to their age:

Paper Age	Citation Threshold	Classification
0-3 years	20+ citations	Noteworthy
0-3 years	100+ citations	Highly Influential
3-7 years	100+ citations	Significant
3-7 years	500+ citations	Landmark Paper
7+ years	500+ citations	Seminal Work
7+ years	1000+ citations	Foundational

When reporting citations: Always indicate approximate citation count when known (e.g., "cited 500+ times" or "highly cited").

Venue Quality Tiers

Prioritize papers from higher-tier venues:

Tier 1 - Premier Venues (Always prefer):

• General Science: Nature, Science, Cell, PNAS
• Medicine: NEJM, Lancet, JAMA, BMJ
• Field-Specific Flagships: Nature Medicine, Nature Biotechnology, Nature Methods, Nature Genetics, Cell Stem Cell, Immunity
• Top CS/AI: NeurIPS, ICML, ICLR, ACL, CVPR (for ML/AI topics)

Tier 2 - High-Impact Specialized (Strong preference):

• Journals with Impact Factor > 10
• Top conferences in subfields (e.g., EMNLP, NAACL, ECCV, MICCAI)
• Society flagship journals (e.g., Blood, Circulation, Gastroenterology)

Tier 3 - Respected Specialized (Include when relevant):

• Journals with Impact Factor 5-10
• Established conferences in the field
• Well-indexed specialized journals

Tier 4 - Other Peer-Reviewed (Use sparingly):

• Lower-impact journals, only if directly relevant and no better source exists

Author Reputation Indicators

Prefer papers from established, reputable researchers:

• Senior authors with high h-index (>40 in established fields)
• Multiple publications in Tier-1 venues
• Leadership positions at recognized research institutions
• Recognized expertise: Awards, editorial positions, society fellows
• First/last author on landmark papers in the field

Direct Relevance Scoring

Always prioritize papers that directly address the research question:

1. Primary Priority: Papers directly addressing the exact research question
2. Secondary Priority: Papers with applicable methods, data, or conceptual frameworks
3. Tertiary Priority: Tangentially related papers (include ONLY if from Tier-1 venues or highly cited)

Practical Application

When conducting research lookups:

1. Start with the most influential papers - Look for highly-cited, foundational work first
2. Prioritize Tier-1 venues - Nature, Science, Cell family journals, NEJM, Lancet for medical topics
3. Check author credentials - Prefer work from established research groups
4. Balance recency with impact - Recent highly-cited papers > older obscure papers > recent uncited papers
5. Report quality indicators - Include citation counts, journal names, and author affiliations in responses

Example Quality-Focused Query Response:

Key findings from high-impact literature:

1. Smith et al. (2023), Nature Medicine (IF: 82.9, cited 450+ times)
   - Senior author: Prof. John Smith, Harvard Medical School
   - Key finding: [finding]

2. Johnson & Lee (2024), Cell (IF: 64.5, cited 120+ times)
   - From the renowned Lee Lab at Stanford
   - Key finding: [finding]

3. Chen et al. (2022), NEJM (IF: 158.5, cited 890+ times)
   - Landmark clinical trial (N=5,000)
   - Key finding: [finding]

Query Best Practices

1. Model Selection Strategy

For Simple Lookups (Sonar Pro Search):

• Recent papers on a specific topic
• Statistical data or prevalence rates
• Standard protocols or methodologies
• Citation finding for specific papers
• Factual information retrieval

For Complex Analysis (Sonar Reasoning Pro):

• Comparative studies and synthesis
• Mechanism explanations
• Controversy evaluation
• Trade-off analysis
• Theoretical frameworks
• Multi-faceted relationships

Pro Tip: The automatic selection is optimized for most use cases. Only use force_model if you have specific requirements or know the query needs deeper reasoning than detected.

2. Specific and Focused Queries

Good Queries (will trigger appropriate model):

• "Randomized controlled trials of mRNA vaccines for cancer treatment 2023-2024" → Sonar Pro Search
• "Compare the efficacy and safety of mRNA vaccines vs traditional vaccines for cancer treatment" → Sonar Reasoning Pro
• "Explain the mechanism by which CRISPR off-target effects occur and strategies to minimize them" → Sonar Reasoning Pro

Poor Queries:

• "Tell me about AI" (too broad)
• "Cancer research" (lacks specificity)
• "Latest news" (too vague)

3. Structured Query Format

Recommended Structure:

[Topic] + [Specific Aspect] + [Time Frame] + [Type of Information]

Examples:

• "CRISPR gene editing + off-target effects + 2024 + clinical trials"
• "Quantum computing + error correction + recent advances + review papers"
• "Renewable energy + solar efficiency + 2023-2024 + statistical data"

4. Follow-up Queries

Effective Follow-ups:

• "Show me the full citation for the Smith et al. 2024 paper"
• "What are the limitations of this methodology?"
• "Find similar studies using different approaches"
• "What controversies exist in this research area?"

Integration with Project Planning

This skill enhances project planning by providing:

1. Technology Research: Gather current information on frameworks, tools, and best practices
2. Architecture Validation: Verify patterns and approaches against current standards
3. Competitive Analysis: Compare solutions with recent similar projects
4. Decision Support: Inform architectural decisions with latest evidence
5. Cost Research: Research pricing and service comparisons

Error Handling and Limitations

Known Limitations:

• Information cutoff: Responses limited to training data (typically 2023-2024)
• Paywall content: May not access full text behind paywalls
• Emerging research: May miss very recent papers not yet indexed
• Specialized databases: Cannot access proprietary or restricted databases

Error Conditions:

• API rate limits or quota exceeded
• Network connectivity issues
• Malformed or ambiguous queries
• Model unavailability or maintenance

Fallback Strategies:

• Rephrase queries for better clarity
• Break complex queries into simpler components
• Use broader time frames if recent data unavailable
• Cross-reference with multiple query variations

Usage Examples

Example 1: Simple Literature Search (Sonar Pro Search)

Query: "Recent advances in transformer attention mechanisms 2024"

Model Selected: Sonar Pro Search (straightforward lookup)

Response Includes:

• Summary of 5 key papers from 2024
• Complete citations with DOIs
• Key innovations and improvements
• Performance benchmarks
• Future research directions

Example 2: Comparative Analysis (Sonar Reasoning Pro)

Query: "Compare and contrast the advantages and limitations of transformer-based models versus traditional RNNs for sequence modeling"

Model Selected: Sonar Reasoning Pro (complex analysis required)

Response Includes:

• Detailed comparison across multiple dimensions
• Analysis of architectural differences
• Trade-offs in computational efficiency vs performance
• Use case recommendations
• Synthesis of evidence from multiple studies
• Discussion of ongoing debates in the field

Example 3: Method Verification (Sonar Pro Search)

Query: "Standard protocols for flow cytometry analysis"

Model Selected: Sonar Pro Search (protocol lookup)

Response Includes:

• Step-by-step protocol from recent review
• Required controls and calibrations
• Common pitfalls and troubleshooting
• Reference to definitive methodology paper
• Alternative approaches with pros/cons

Example 4: Mechanism Explanation (Sonar Reasoning Pro)

Query: "Explain the underlying mechanism of how mRNA vaccines trigger immune responses and why they differ from traditional vaccines"

Model Selected: Sonar Reasoning Pro (requires causal reasoning)

Response Includes:

• Detailed mechanistic explanation
• Step-by-step biological processes
• Comparative analysis with traditional vaccines
• Molecular-level interactions
• Integration of immunology and pharmacology concepts
• Evidence from recent research

Example 5: Statistical Data (Sonar Pro Search)

Query: "Global AI adoption in healthcare statistics 2024"

Model Selected: Sonar Pro Search (data lookup)

Response Includes:

• Current adoption rates by region
• Market size and growth projections
• Survey methodology and sample size
• Comparison with previous years
• Citations to market research reports

Performance and Cost Considerations

Response Times

Sonar Pro Search:

• Typical response time: 5-15 seconds
• Best for rapid information gathering
• Suitable for batch queries

Sonar Reasoning Pro:

• Typical response time: 15-45 seconds
• Worth the wait for complex analytical queries
• Provides more thorough reasoning and synthesis

Cost Optimization

Automatic Selection Benefits:

• Saves costs by using Sonar Pro Search for straightforward queries
• Reserves Sonar Reasoning Pro for queries that truly benefit from deeper analysis
• Optimizes the balance between cost and quality

Manual Override Use Cases:

• Force Sonar Pro Search when budget is constrained and speed is priority
• Force Sonar Reasoning Pro when working on critical research requiring maximum depth
• Use for specific sections of papers (e.g., Pro Search for methods, Reasoning for discussion)

Best Practices:

1. Trust the automatic selection for most use cases
2. Review query results - if Sonar Pro Search doesn't provide sufficient depth, rephrase with reasoning keywords
3. Use batch queries strategically - combine simple lookups to minimize total query count
4. For literature reviews, start with Sonar Pro Search for breadth, then use Sonar Reasoning Pro for synthesis

Security and Ethical Considerations

Responsible Use:

• Verify all information against primary sources when possible
• Clearly attribute all data and quotes to original sources
• Avoid presenting AI-generated summaries as original research
• Respect copyright and licensing restrictions
• Use for research assistance, not to bypass paywalls or subscriptions

Academic Integrity:

• Always cite original sources, not the AI tool
• Use as a starting point for literature searches
• Follow institutional guidelines for AI tool usage
• Maintain transparency about research methods

Complementary Tools

In addition to research-lookup, the project planner has access to WebSearch for:

• Quick metadata verification: Look up DOIs, publication years, journal names, volume/page numbers
• Non-academic sources: News, blogs, technical documentation, current events
• General information: Company info, product details, current statistics
• Cross-referencing: Verify citation details found through research-lookup

When to use which tool:

Task	Tool
Find academic papers	research-lookup
Literature search	research-lookup
Deep analysis/comparison	research-lookup (Sonar Reasoning Pro)
Look up DOI/metadata	WebSearch
Verify publication year	WebSearch
Find journal volume/pages	WebSearch
Current events/news	WebSearch
Non-scholarly sources	WebSearch

Summary

This skill serves as a powerful research assistant with intelligent dual-model selection:

• Automatic Intelligence: Analyzes query complexity and selects the optimal model (Sonar Pro Search or Sonar Reasoning Pro)
• Cost-Effective: Uses faster, cheaper Sonar Pro Search for straightforward lookups
• Deep Analysis: Automatically engages Sonar Reasoning Pro for complex comparative, analytical, and theoretical queries
• Flexible Control: Manual override available when you know exactly what level of analysis you need
• Academic Focus: Both models configured to prioritize peer-reviewed sources and scholarly literature
• Complementary WebSearch: Use alongside WebSearch for metadata verification and non-academic sources

Whether you need quick fact-finding or deep analytical synthesis, this skill automatically adapts to deliver the right level of research support for your project planning needs.