Research Information Lookup
Overview
This skill provides real-time research information lookup with intelligent backend routing:
- parallel-cli search (parallel-web skill): Primary and default backend for all research queries. Fast, cost-effective web search with academic source prioritization. Uses
parallel-cli searchwith--include-domainsfor scholarly sources. - Parallel Chat API (
coremodel): Secondary backend for complex, multi-source deep research requiring extended synthesis (60s-5min latency). Use only when explicitly needed. - Perplexity sonar-pro-search (via OpenRouter): Used only for academic-specific paper searches where scholarly database access is critical.
The skill automatically detects query type and routes to the optimal backend.
When to Use This Skill
Use this skill when you need:
- Current Research Information: Latest studies, papers, and findings
- Literature Verification: Check facts, statistics, or claims against current research
- Background Research: Gather context and supporting evidence for scientific writing
- Citation Sources: Find relevant papers and studies to cite
- Technical Documentation: Look up specifications, protocols, or methodologies
- Market/Industry Data: Current statistics, trends, competitive intelligence
- Recent Developments: Emerging trends, breakthroughs, announcements
Visual Enhancement with Scientific Schematics
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:
- Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
Automatic Backend Selection
The skill automatically routes queries to the best backend based on content:
Routing Logic
Query arrives
|
+-- Contains academic keywords? (papers, DOI, journal, peer-reviewed, etc.)
| YES --> Perplexity sonar-pro-search (academic search mode)
|
+-- Needs deep multi-source synthesis? (user says "deep research", "exhaustive")
| YES --> Parallel Chat API (core model, 60s-5min)
|
+-- Everything else (general research, market data, technical info, analysis)
--> parallel-cli search (fast, default)
Default: parallel-cli search (parallel-web skill)
Primary backend for all standard research queries. Fast, cost-effective, and supports academic source prioritization.
For scientific/technical queries, run two searches to ensure academic coverage:
# 1. Academic-focused search
parallel-cli search "your research query" -q "keyword1" -q "keyword2" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
--include-domains "scholar.google.com,arxiv.org,pubmed.ncbi.nlm.nih.gov,semanticscholar.org,biorxiv.org,medrxiv.org,ncbi.nlm.nih.gov,nature.com,science.org,ieee.org,acm.org,springer.com,wiley.com,cell.com,pnas.org,nih.gov" \
-o sources/research_<topic>-academic.json
# 2. General search (catches non-academic sources)
parallel-cli search "your research query" -q "keyword1" -q "keyword2" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
-o sources/research_<topic>-general.json
Options:
--after-date YYYY-MM-DDfor time-sensitive queries--include-domains domain1.com,domain2.comto limit to specific sources
Merge results, leading with academic sources. For non-scientific queries, a single general search is sufficient.
All other queries route here by default, including:
- General research questions
- Market and industry analysis
- Technical information and documentation
- Current events and recent developments
- Comparative analysis
- Statistical data retrieval
- Fact-checking and verification
Academic Keywords (Routes to Perplexity)
Queries containing these terms are routed to Perplexity for academic-focused search:
- Paper finding:
find papers,find articles,research papers on,published studies - Citations:
cite,citation,doi,pubmed,pmid - Academic sources:
peer-reviewed,journal article,scholarly,arxiv,preprint - Review types:
systematic review,meta-analysis,literature search - Paper quality:
foundational papers,seminal papers,landmark papers,highly cited
Deep Research (Routes to Parallel Chat API)
Only used when the user explicitly requests deep, exhaustive, or comprehensive research. Much slower and more expensive than parallel-cli search.
Manual Override
You can force a specific backend:
# Force parallel-cli search (fast web search)
parallel-cli search "your query" -q "keyword" --json --max-results 10 -o sources/research_<topic>.json
# Force Parallel Deep Research (slow, exhaustive)
python research_lookup.py "your query" --force-backend parallel
# Force Perplexity academic search
python research_lookup.py "your query" --force-backend perplexity
Core Capabilities
1. General Research Queries (parallel-cli search — DEFAULT)
Primary backend. Fast, cost-effective web search with academic source prioritization via the parallel-web skill.
Query Examples:
- "Recent advances in CRISPR gene editing 2025"
- "Compare mRNA vaccines vs traditional vaccines for cancer treatment"
- "AI adoption in healthcare industry statistics"
- "Global renewable energy market trends and projections"
- "Explain the mechanism underlying gut microbiome and depression"
# Example: research on CRISPR advances
parallel-cli search "Recent advances in CRISPR gene editing 2025" \
-q "CRISPR" -q "gene editing" -q "2025" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
--include-domains "scholar.google.com,arxiv.org,pubmed.ncbi.nlm.nih.gov,nature.com,science.org,cell.com,pnas.org,nih.gov" \
-o sources/research_crispr_advances-academic.json
parallel-cli search "Recent advances in CRISPR gene editing 2025" \
-q "CRISPR" -q "gene editing" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
-o sources/research_crispr_advances-general.json
Response includes:
- Synthesized findings with inline citations from search results
- Academic sources prioritized (peer-reviewed, preprints)
- Specific facts, numbers, and dates
- Sources section listing all referenced URLs grouped by type
2. Academic Paper Search (Perplexity sonar-pro-search)
Used for academic-specific queries. Prioritizes scholarly databases and peer-reviewed sources. Use when queries specifically ask for papers, citations, or DOIs.
Query Examples:
- "Find papers on transformer attention mechanisms in NeurIPS 2024"
- "Foundational papers on quantum error correction"
- "Systematic review of immunotherapy in non-small cell lung cancer"
- "Cite the original BERT paper and its most influential follow-ups"
- "Published studies on CRISPR off-target effects in clinical trials"
Response includes:
- Summary of key findings from academic literature
- 5-8 high-quality citations with authors, titles, journals, years, DOIs
- Citation counts and venue tier indicators
- Key statistics and methodology highlights
- Research gaps and future directions
3. Deep Research (Parallel Chat API — on request only)
Used only when user explicitly requests deep/exhaustive research. Provides comprehensive, multi-source synthesis via the Chat API (core model). 60s-5min latency.
Query Examples:
- "Deep research on the current state of quantum computing error correction"
- "Exhaustive analysis of mRNA vaccine platforms for cancer immunotherapy"
4. Technical and Methodological Information
Use parallel-cli search (default) for quick lookups:
parallel-cli search "Western blot protocol for protein detection" \
-q "western bl