research-lookup

name	research-lookup
description	Look up current research information using the Parallel Chat API (primary) 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.
allowed-tools	Read Write Edit Bash
license	MIT license
compatibility	PARALLEL_API_KEY and OPENROUTER_API_KEY required
tags	["scientific-skills","research-lookup","api","search","academic-writing"]
metadata	{"skill-author":"K-Dense Inc."}

Core Capabilities

1. General Research Queries (Parallel Chat API)

Default backend. Provides comprehensive, multi-source research with citations via the Chat API (core model).

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"

Response includes:

• Comprehensive research report in markdown
• Inline citations from authoritative web sources
• Structured sections with key findings
• Multiple perspectives and data points
• Source URLs for verification

2. Academic Paper Search (Perplexity sonar-pro-search)

Used for academic-specific queries. Prioritizes scholarly databases and peer-reviewed sources.

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. Technical and Methodological Information

Query Examples:
- "Western blot protocol for protein detection"
- "Statistical power analysis for clinical trials"
- "Machine learning model evaluation metrics comparison"

4. Statistical and Market Data

Query Examples:
- "Prevalence of diabetes in US population 2025"
- "Global AI market size and growth projections"
- "COVID-19 vaccination rates by country"

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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

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Technical Integration

Environment Variables

# Primary backend (Parallel Chat API) - REQUIRED
export PARALLEL_API_KEY="your_parallel_api_key"

# Academic search backend (Perplexity) - REQUIRED for academic queries
export OPENROUTER_API_KEY="your_openrouter_api_key"

API Specifications

Parallel Chat API:

• 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:

• 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

# Auto-routed research (recommended) — ALWAYS save to sources/
python research_lookup.py "your query" -o sources/research_YYYYMMDD_HHMMSS_<topic>.md

# Force specific backend — ALWAYS save to sources/
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

# JSON output — ALWAYS save to sources/
python research_lookup.py "your query" --json -o sources/research_<topic>.json

# Batch queries — ALWAYS save to sources/
python research_lookup.py --batch "query 1" "query 2" "query 3" -o sources/batch_research_<topic>.md

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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 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 call to research_lookup.py MUST include the -o flag pointing to the sources/ folder.

CRITICAL: Saved files MUST preserve all citations, source URLs, and DOIs. The default text output automatically includes a Sources section (with title, date, URL for each source) and an Additional References section (with DOIs and academic URLs extracted from the response text). For maximum citation metadata, use --json.

# General research — save to sources/ (includes Sources + Additional References sections)
python research_lookup.py "Recent advances in CRISPR gene editing 2025" \
  -o sources/research_20250217_143000_crispr_advances.md

# Academic paper search — save to sources/ (includes paper citations with DOIs)
python research_lookup.py "Find papers on transformer attention mechanisms in NeurIPS 2024" \
  -o sources/papers_20250217_143500_transformer_attention.md

# JSON format for maximum citation metadata (full citation objects with URLs, DOIs, snippets)
python research_lookup.py "CRISPR clinical trials" --json \
  -o sources/research_20250217_143000_crispr_trials.json

# Forced backend — save to sources/
python research_lookup.py "AI regulation landscape" --force-backend parallel \
  -o sources/research_20250217_144000_ai_regulation.md

# Batch queries — save to sources/
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
Text (default)	Sources (N): section with [title] (date) + URL + Additional References (N): with DOIs and academic URLs	Standard use — human-readable with all citations
JSON (--json)	Full citation objects: url, title, date, snippet, doi, type	When you need maximum citation metadata

For Parallel 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

1. Reproducibility: Every citation and claim can be traced back to its raw research source
2. Context Window Recovery: If context is compacted, saved results can be re-read without re-querying
3. Audit Trail: The sources/ folder documents exactly how all research information was gathered
4. Reuse Across Sections: Multiple sections can reference the same saved research without duplicate queries
5. Cost Efficiency: Check sources/ for existing results before making new API calls
6. 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/  # Check existing saved results

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)

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Integration with Scientific Writing

This skill enhances scientific writing by providing:

1. Literature Review Support: Gather current research for introduction and discussion — save to sources/
2. Methods Validation: Verify protocols against current standards — save to sources/
3. Results Contextualization: Compare findings with recent similar studies — save to sources/
4. Discussion Enhancement: Support arguments with latest evidence — save to sources/
5. Citation Management: Provide properly formatted citations — save to sources/

Complementary Tools

Task	Tool
General web search	parallel-web skill (parallel_web.py search)
Citation verification	parallel-web skill (parallel_web.py extract)
Deep research (any topic)	research-lookup or parallel-web skill
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-web skill (parallel_web.py search or extract)

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Error Handling and Limitations

Known Limitations:

• Parallel Chat API (core model): Complex queries may take up to 5 minutes
• Perplexity: Information cutoff, may not access full text behind paywalls
• Both: Cannot access proprietary or restricted databases

Fallback Behavior:

• If the selected backend's API key is missing, tries the other backend
• If both backends fail, returns structured error response
• Rephrase queries for better results if initial response is insufficient

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Usage Examples

Example 1: General Research (Routes to Parallel)

Query: "Recent advances in transformer attention mechanisms 2025"

Backend: Parallel Chat API (core model)

Response: Comprehensive markdown report with citations from authoritative 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)

Query: "Compare and contrast mRNA vaccines vs traditional vaccines for cancer treatment"

Backend: Parallel Chat API (core model)

Response: Detailed comparative report with data from multiple sources, structured analysis, and cited evidence.

Example 4: Market Data (Routes to Parallel)

Query: "Global AI adoption in healthcare statistics 2025"

Backend: Parallel Chat API (core model)

Response: Current market data, adoption rates, growth projections, and regional analysis with source citations.

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Summary

This skill serves as the primary research interface with intelligent dual-backend routing:

• Parallel Chat API (default, core model): Comprehensive, multi-source research for any topic
• Perplexity sonar-pro-search: Academic-specific paper searches only
• Automatic routing: Detects academic queries and routes appropriately
• Manual override: Force any backend when needed
• Complementary: Works alongside parallel-web skill for web search and URL extraction