Alterlab Research Lookup

Research Information Lookup

Overview

This skill provides real-time research information lookup with intelligent backend routing:

• Parallel Chat API (core model): Default backend for all general research queries. Provides comprehensive, multi-source research reports with inline citations via the OpenAI-compatible Chat API at https://api.parallel.ai.
• 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

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)
    |
    +-- Everything else (general research, market data, technical info, analysis)
            --> Parallel Chat API (core model)

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

Everything Else (Routes to Parallel)

All other queries go to the Parallel Chat API (core model), including:

• General research questions
• Market and industry analysis
• Technical information and documentation
• Current events and recent developments
• Comparative analysis
• Statistical data retrieval
• Complex analytical queries

Manual Override

You can force a specific backend:

# Force Parallel Deep Research
python scripts/research_lookup.py "your query" --force-backend parallel

# Force Perplexity academic search
python scripts/research_lookup.py "your query" --force-backend perplexity

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"

Paper Quality and Popularity Prioritization

CRITICAL: When searching for papers, ALWAYS prioritize high-quality, influential papers.

Citation-Based Ranking

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

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 source URLs and excerpts
• Rate limits: subject to your Parallel account tier (check the dashboard)
• 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 scripts/research_lookup.py "your query" -o sources/research_YYYYMMDD_HHMMSS_<topic>.md

# Force specific backend — ALWAYS save to sources/
python scripts/research_lookup.py "your query" --force-backend parallel -o sources/research_<topic>.md
python scripts/research_lookup.py "your query" --force-backend perplexity -o sources/papers_<topic>.md

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

# Batch queries — ALWAYS save to sources/
python scripts/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

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 scripts/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 scripts/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 scripts/research_lookup.py "CRISPR clinical trials" --json \
  -o sources/research_20250217_143000_crispr_trials.json

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

# Batch queries — save to sources/
python scripts/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:

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)

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

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

Backend-key setup and specific error messages: see references/troubleshooting.md.

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.

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