| name | research-lookup |
| description | Look up current research information using parallel-cli search (primary, fast web search), the Parallel Chat API (deep research), or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information. Note: query text is transmitted to api.parallel.ai (PARALLEL_API_KEY) and, for academic searches, to openrouter.ai (OPENROUTER_API_KEY). |
| allowed-tools | Read Write Edit Bash |
| license | MIT license |
| compatibility | parallel-cli required (primary); PARALLEL_API_KEY and OPENROUTER_API_KEY optional for deep/academic backends |
| metadata | {"skill-author":"K-Dense Inc."} |
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 search with --include-domains for scholarly sources.
- Parallel Chat API (
core model): 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:
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
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-DD for time-sensitive queries--include-domains domain1.com,domain2.com to 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:
parallel-cli search "your query" -q "keyword" --json --max-results 10 -o sources/research_<topic>.json
python research_lookup.py "your query" --force-backend parallel
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"
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 blot" -q "protocol" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
-o sources/research_western_blot.json
5. Statistical and Market Data
Use parallel-cli search (default) for current data:
parallel-cli search "Global AI market size and growth projections 2025" \
-q "AI market" -q "statistics" -q "growth" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
--after-date 2024-01-01 \
-o sources/research_ai_market.json
Paper Quality and Popularity Prioritization
CRITICAL: When searching for papers, ALWAYS prioritize high-quality, influential papers.
Citation-Based Ranking
| 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 |
Venue Quality Tiers
Tier 1 - Premier Venues (Always prefer):
- General Science: Nature, Science, Cell, PNAS
- Medicine: NEJM, Lancet, JAMA, BMJ
- Field-Specific: Nature Medicine, Nature Biotechnology, Nature Methods
- Top CS/AI: NeurIPS, ICML, ICLR, ACL, CVPR
Tier 2 - High-Impact Specialized (Strong preference):
- Journals with Impact Factor > 10
- Top conferences in subfields (EMNLP, NAACL, ECCV, MICCAI)
Tier 3 - Respected Specialized (Include when relevant):
- Journals with Impact Factor 5-10
Technical Integration
Prerequisites
curl -fsSL https://parallel.ai/install.sh | bash
parallel-cli auth
Environment Variables
export PARALLEL_API_KEY="your_parallel_api_key"
export OPENROUTER_API_KEY="your_openrouter_api_key"
API Specifications
parallel-cli search (PRIMARY):
- Command:
parallel-cli search with --json output
- Latency: 2-10 seconds (fast)
- Output: JSON with title, URL, publish_date, excerpts
- Academic domains: Use
--include-domains for scholarly sources
- Saves results:
-o filename.json for follow-up and reproducibility
Parallel Chat API (deep research only):
- Endpoint:
https://api.parallel.ai (OpenAI SDK compatible)
- Model:
core (60s-5min latency, complex multi-source synthesis)
- Output: Markdown text with inline citations
- Citations: Research basis with URLs, reasoning, and confidence levels
- Rate limits: 300 req/min
- Python package:
openai
Perplexity sonar-pro-search (academic only):
- Model:
perplexity/sonar-pro-search (via OpenRouter)
- Search mode: Academic (prioritizes peer-reviewed sources)
- Search context: High (comprehensive research)
- Response time: 5-15 seconds
Command-Line Usage
parallel-cli search "your query" -q "keyword1" -q "keyword2" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
-o sources/research_<topic>.json
parallel-cli search "your query" -q "keyword1" \
--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,nature.com,science.org,cell.com,pnas.org,nih.gov" \
-o sources/research_<topic>-academic.json
parallel-cli search "your query" -q "keyword" \
--json --max-results 10 --after-date 2024-01-01 \
-o sources/research_<topic>.json
parallel-cli extract "https://example.com/paper" --json
python research_lookup.py "your query" --force-backend parallel -o sources/research_<topic>.md
python research_lookup.py "your query" --force-backend perplexity -o sources/papers_<topic>.md
python research_lookup.py "your query" -o sources/research_YYYYMMDD_HHMMSS_<topic>.md
python research_lookup.py --batch "query 1" "query 2" "query 3" -o sources/batch_research_<topic>.md
MANDATORY: Save All Results to Sources Folder
Every research-lookup result MUST be saved to the project's sources/ folder.
This is non-negotiable. Research results are expensive to obtain and critical for reproducibility.
Saving Rules
| Backend | -o Flag Target | Filename Pattern |
|---|
| parallel-cli search (default) | sources/research_<topic>.json | research_<brief_topic>.json or research_<brief_topic>-academic.json |
| Parallel Deep Research | sources/research_<topic>.md | research_YYYYMMDD_HHMMSS_<brief_topic>.md |
| Perplexity (academic) | sources/papers_<topic>.md | papers_YYYYMMDD_HHMMSS_<brief_topic>.md |
| Batch queries | sources/batch_<topic>.md | batch_research_YYYYMMDD_HHMMSS_<brief_topic>.md |
How to Save
CRITICAL: Every search MUST save results to the sources/ folder using the -o flag.
CRITICAL: Saved files MUST preserve all citations, source URLs, and DOIs.
parallel-cli search "Recent advances in CRISPR gene editing 2025" \
-q "CRISPR" -q "gene editing" \
--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
python research_lookup.py "Find papers on transformer attention mechanisms in NeurIPS 2024" \
-o sources/papers_20250217_143500_transformer_attention.md
python research_lookup.py "AI regulation landscape" --force-backend parallel \
-o sources/research_20250217_144000_ai_regulation.md
python research_lookup.py --batch "mRNA vaccines efficacy" "mRNA vaccines safety" \
-o sources/batch_research_20250217_144500_mrna_vaccines.md
Citation Preservation in Saved Files
Each output format preserves citations differently:
| Format | Citations Included | When to Use |
|---|
| parallel-cli JSON (default) | Full result objects: title, url, publish_date, excerpts | Standard use — structured, parseable, fast |
| Text (research_lookup.py) | Sources (N): section with [title] (date) + URL + Additional References (N): with DOIs and academic URLs | Deep research / Perplexity — human-readable |
JSON (--json via research_lookup.py) | Full citation objects: url, title, date, snippet, doi, type | When you need maximum citation metadata from deep research |
For parallel-cli search, saved JSON files include: full search results with title, URL, publish date, and content excerpts for each result.
For Parallel Chat API backend, saved files include: research report + Sources list (title, URL) + Additional References (DOIs, academic URLs).
For Perplexity backend, saved files include: academic summary + Sources list (title, date, URL, snippet) + Additional References (DOIs, academic URLs).
Use --json when you need to:
- Parse citation metadata programmatically
- Preserve full DOI and URL data for BibTeX generation
- Maintain the structured citation objects for cross-referencing
Why Save Everything
- Reproducibility: Every citation and claim can be traced back to its raw research source
- Context Window Recovery: If context is compacted, saved results can be re-read without re-querying
- Audit Trail: The
sources/ folder documents exactly how all research information was gathered
- Reuse Across Sections: Multiple sections can reference the same saved research without duplicate queries
- Cost Efficiency: Check
sources/ for existing results before making new API calls
- Peer Review Support: Reviewers can verify the research backing every citation
Before Making a New Query, Check Sources First
Before calling research_lookup.py, check if a relevant result already exists:
ls sources/
If a prior lookup covers the same topic, re-read the saved file instead of making a new API call.
Logging
When saving research results, always log:
[HH:MM:SS] SAVED: Research lookup to sources/research_20250217_143000_crispr_advances.md (3,800 words, 8 citations)
[HH:MM:SS] SAVED: Paper search to sources/papers_20250217_143500_transformer_attention.md (6 papers found)
Integration with Scientific Writing
This skill enhances scientific writing by providing:
- Literature Review Support: Gather current research for introduction and discussion — save to
sources/
- Methods Validation: Verify protocols against current standards — save to
sources/
- Results Contextualization: Compare findings with recent similar studies — save to
sources/
- Discussion Enhancement: Support arguments with latest evidence — save to
sources/
- Citation Management: Provide properly formatted citations — save to
sources/
Complementary Tools
| Task | Tool |
|---|
| General web search (fast) | parallel-cli search (built into this skill) |
| Academic-focused web search | parallel-cli search --include-domains (built into this skill) |
| URL content extraction | parallel-cli extract (parallel-web skill) |
| Deep research (exhaustive) | research-lookup via Parallel Chat API or parallel-web deep research |
| Academic paper search | research-lookup (auto-routes to Perplexity) |
| Google Scholar search | citation-management skill |
| PubMed search | citation-management skill |
| DOI to BibTeX | citation-management skill |
| Metadata verification | parallel-cli extract (parallel-web skill) |
Error Handling and Limitations
Known Limitations:
- parallel-cli search: Requires
parallel-cli to be installed and authenticated
- Parallel Chat API (core model): Complex queries may take up to 5 minutes
- Perplexity: Information cutoff, may not access full text behind paywalls
- All backends: Cannot access proprietary or restricted databases
Fallback Behavior:
- If
parallel-cli is not found, install with curl -fsSL https://parallel.ai/install.sh | bash or uv tool install "parallel-web-tools[cli]"
- If parallel-cli search returns insufficient results, fall back to Perplexity or Parallel Chat API
- If the selected backend's API key is missing, tries the other backend
- If all backends fail, returns structured error response
- Rephrase queries for better results if initial response is insufficient
Usage Examples
Example 1: General Research (Routes to parallel-cli search)
Query: "Recent advances in transformer attention mechanisms 2025"
Backend: parallel-cli search (default, fast)
Commands:
parallel-cli search "Recent advances in transformer attention mechanisms 2025" \
-q "transformer" -q "attention" -q "2025" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
--include-domains "arxiv.org,semanticscholar.org,nature.com,science.org,ieee.org,acm.org" \
-o sources/research_transformer_attention-academic.json
parallel-cli search "Recent advances in transformer attention mechanisms 2025" \
-q "transformer" -q "attention" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
-o sources/research_transformer_attention-general.json
Response: Synthesized findings with inline citations from academic and general sources, covering recent papers, key innovations, and performance benchmarks.
Example 2: Academic Paper Search (Routes to Perplexity)
Query: "Find papers on CRISPR off-target effects in clinical trials"
Backend: Perplexity sonar-pro-search (academic mode)
Response: Curated list of 5-8 high-impact papers with full citations, DOIs, citation counts, and venue tier indicators.
Example 3: Comparative Analysis (Routes to parallel-cli search)
Query: "Compare and contrast mRNA vaccines vs traditional vaccines for cancer treatment"
Backend: parallel-cli search (default, fast)
Response: Synthesized comparison from multiple web sources with inline citations, structured analysis, and evidence quality notes.
Example 4: Market Data (Routes to parallel-cli search)
Query: "Global AI adoption in healthcare statistics 2025"
Backend: parallel-cli search (default, fast)
parallel-cli search "Global AI adoption in healthcare statistics 2025" \
-q "AI healthcare" -q "adoption statistics" \
--json --max-results 10 --excerpt-max-chars-total 27000 \
--after-date 2024-01-01 \
-o sources/research_ai_healthcare_adoption.json
Response: Current market data, adoption rates, growth projections, and regional analysis with source citations.
Summary
This skill serves as the primary research interface with intelligent tri-backend routing:
- parallel-cli search (default): Fast, cost-effective web search with academic source prioritization via the parallel-web skill
- Parallel Chat API (
core model): Deep, exhaustive multi-source synthesis (on explicit request only)
- Perplexity sonar-pro-search: Academic-specific paper searches only
- Automatic routing: Detects query type and routes to the optimal backend
- Manual override: Force any backend when needed
- Academic prioritization: Two-search pattern ensures scholarly sources surface for scientific queries
