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SKILL.md
Perplexity Search
Overview
Perform AI-powered web searches using Perplexity models through LiteLLM and OpenRouter. Perplexity provides real-time, web-grounded answers with source citations, making it ideal for finding current information, recent scientific literature, and facts beyond the model's training data cutoff.
This skill provides access to all Perplexity models through OpenRouter, requiring only a single API key (no separate Perplexity account needed).
When to Use This Skill
Use this skill when:
Searching for current information or recent developments (2024 and beyond)
Finding latest scientific publications and research
Getting real-time answers grounded in web sources
Verifying facts with source citations
Conducting literature searches across multiple domains
Accessing information beyond the model's knowledge cutoff
Performing domain-specific research (biomedical, technical, clinical)
Comparing current approaches or technologies
Do not use for:
Simple calculations or logic problems (use directly)
Tasks requiring code execution (use standard tools)
Questions well within the model's training data (unless verification needed)
python scripts/perplexity_search.py "Compare mRNA and viral vector vaccines" --model sonar-pro-search
Verbose output:
python scripts/perplexity_search.py "Quantum computing for drug discovery" --verbose
Available Models
Access models via --model parameter:
sonar-pro (default): General-purpose search, best balance of cost and quality
sonar-pro-search: Most advanced agentic search with multi-step reasoning
sonar: Basic model, most cost-effective for simple queries
sonar-reasoning-pro: Advanced reasoning with step-by-step analysis
sonar-reasoning: Basic reasoning capabilities
Model selection guide:
Default queries → sonar-pro
Complex multi-step analysis → sonar-pro-search
Explicit reasoning needed → sonar-reasoning-pro
Simple fact lookups → sonar
Cost-sensitive bulk queries → sonar
See references/model_comparison.md for detailed comparison, use cases, pricing, and performance characteristics.
Crafting Effective Queries
Be Specific and Detailed
Good examples:
"What are the latest clinical trial results for CAR-T cell therapy in treating B-cell lymphoma published in 2024?"
"Compare the efficacy and safety profiles of mRNA vaccines versus viral vector vaccines for COVID-19"
"Explain AlphaFold3 improvements over AlphaFold2 with specific accuracy metrics from 2023-2024 research"
Bad examples:
"Tell me about cancer treatment" (too broad)
"CRISPR" (too vague)
"vaccines" (lacks specificity)
Include Time Constraints
Perplexity searches real-time web data:
"What papers were published in Nature Medicine in 2024 about long COVID?"
"What are the latest developments (past 6 months) in large language model efficiency?"
"What was announced at NeurIPS 2023 regarding AI safety?"
Specify Domain and Sources
For high-quality results, mention source preferences:
"According to peer-reviewed publications in high-impact journals..."
"Based on FDA-approved treatments..."
"From clinical trial registries like clinicaltrials.gov..."
Structure Complex Queries
Break complex questions into clear components:
Topic: Main subject
Scope: Specific aspect of interest
Context: Time frame, domain, constraints
Output: Desired format or type of answer
Example:
"What improvements does AlphaFold3 offer over AlphaFold2 for protein structure prediction, according to research published between 2023 and 2024? Include specific accuracy metrics and benchmarks."
See references/search_strategies.md for comprehensive guidance on query design, domain-specific patterns, and advanced techniques.
Common Use Cases
Scientific Literature Search
python scripts/perplexity_search.py \
"What does recent research (2023-2024) say about the role of gut microbiome in Parkinson's disease? Focus on peer-reviewed studies and include specific bacterial species identified." \
--model sonar-pro
Technical Documentation
python scripts/perplexity_search.py \
"How to implement real-time data streaming from Kafka to PostgreSQL using Python? Include considerations for handling backpressure and ensuring exactly-once semantics." \
--model sonar-reasoning-pro
Comparative Analysis
python scripts/perplexity_search.py \
"Compare PyTorch versus TensorFlow for implementing transformer models in terms of ease of use, performance, and ecosystem support. Include benchmarks from recent studies." \
--model sonar-pro-search
Clinical Research
python scripts/perplexity_search.py \
"What is the evidence for intermittent fasting in managing type 2 diabetes in adults? Focus on randomized controlled trials and report HbA1c changes and weight loss outcomes." \
--model sonar-pro
Trend Analysis
python scripts/perplexity_search.py \
"What are the key trends in single-cell RNA sequencing technology over the past 5 years? Highlight improvements in throughput, cost, and resolution, with specific examples." \
--model sonar-pro
Working with Results
Programmatic Access
Use perplexity_search.py as a module:
from scripts.perplexity_search import search_with_perplexity
result = search_with_perplexity(
query="What are the latest CRISPR developments?",
model="openrouter/perplexity/sonar-pro",
max_tokens=4000,
temperature=0.2,
verbose=False
)
if result["success"]:
print(result["answer"])
print(f"Tokens used: {result['usage']['total_tokens']}")
else:
print(f"Error: {result['error']}")
Save and Process Results
# Save to JSON
python scripts/perplexity_search.py "query" --output results.json
# Process with jq
cat results.json | jq '.answer'
cat results.json | jq '.usage'
