This skill enables real-time research information lookup using Perplexity's Sonar models through OpenRouter. It intelligently selects between Sonar Pro Search (fast, efficient lookup) and Sonar Reasoning Pro (deep analytical reasoning) based on query complexity. The skill provides access to current academic literature, recent studies, technical documentation, and general research information with proper citations and source attribution.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionresearch-lookupExecute the skills CLI command in your project's root directory to begin installation:
Fetches research-lookup from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate research-lookup. Access via /research-lookup in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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This skill enables real-time research information lookup using Perplexity's Sonar models through OpenRouter. It intelligently selects between Sonar Pro Search (fast, efficient lookup) and Sonar Reasoning Pro (deep analytical reasoning) based on query complexity. The skill provides access to current academic literature, recent studies, technical documentation, and general research information with proper citations and source attribution.
Use this skill when you need:
When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.
If your document does not already contain schematics or diagrams:
For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
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:
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:
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:
Citation Finding: Locate relevant papers and studies for citation in manuscripts:
Query Examples:
- "Foundational papers on transformer architecture"
- "Seminal works in quantum computing"
- "Key studies on climate change mitigation"
- "Landmark trials in cancer immunotherapy"
Expected Response Format:
This skill features intelligent model selection based on query complexity:
1. Sonar Pro Search (perplexity/sonar-pro-search)
2. Sonar Reasoning Pro (perplexity/sonar-reasoning-pro)
The skill automatically detects query complexity using these indicators:
Reasoning Keywords (triggers Sonar Reasoning Pro):
compare, contrast, analyze, analysis, evaluate, critiqueversus, vs, vs., compared to, differences between, similaritiesmeta-analysis, systematic review, synthesis, integratemechanism, why, how does, how do, explain, relationship, causal relationship, underlying mechanismtheoretical framework, implications, interpret, reasoningcontroversy, conflicting, paradox, debate, reconcilepros and cons, advantages and disadvantages, trade-off, tradeoff, trade offsmultifaceted, complex interaction, critical analysisComplexity Scoring:
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):
✅ Sonar Reasoning Pro (complex analysis):
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"
This skill integrates with OpenRouter (openrouter.ai) to access Perplexity's Sonar models:
Model Specifications:
perplexity/sonar-pro-search (fast lookup)perplexity/sonar-reasoning-pro-online (deep analysis)high search context for deeper, more comprehensive research resultsAPI Requirements:
OPENROUTER_API_KEY environment variable)Academic Mode Configuration:
Source Verification: The skill prioritizes:
Citation Standards: All responses include:
For Simple Lookups (Sonar Pro Search):
For Complex Analysis (Sonar Reasoning Pro):
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.
Good Queries (will trigger appropriate model):
Poor Queries:
Recommended Structure:
[Topic] + [Specific Aspect] + [Time Frame] + [Type of Information]
Examples:
Effective Follow-ups:
This skill enhances scientific writing by providing:
Known Limitations:
Error Conditions:
Fallback Strategies:
Query: "Recent advances in transformer attention mechanisms 2024"
Model Selected: Sonar Pro Search (straightforward lookup)
Response Includes:
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:
Query: "Standard protocols for flow cytometry analysis"
Model Selected: Sonar Pro Search (protocol lookup)
Response Includes:
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:
Query: "Global AI adoption in healthcare statistics 2024"
Model Selected: Sonar Pro Search (data lookup)
Response Includes:
Sonar Pro Search:
Sonar Reasoning Pro:
Automatic Selection Benefits:
Manual Override Use Cases:
Best Practices:
Responsible Use:
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend research-lookup for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: research-lookup is focused, and the summary matches what you get after install.
research-lookup has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend research-lookup for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in research-lookup — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for research-lookup matched our evaluation — installs cleanly and behaves as described in the markdown.
research-lookup reduced setup friction for our internal harness; good balance of opinion and flexibility.
research-lookup has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: research-lookup is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: research-lookup is focused, and the summary matches what you get after install.
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