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 (Beta, https://api.parallel.ai/v1beta/chat/completions).
• 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

Does NOT Trigger

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

When searching for papers, favor high-quality, influential work, but treat citation counts and venue as rough proxies rather than measures of quality: they lag for recent papers, vary widely by field, and under-represent null results, regional journals, and non-English work. Judge each paper on its design and evidence (see alterlab-scientific-thinking), and include a lower-profile paper when it is the most direct evidence for the claim. The thresholds below are working heuristics, not established cut-offs.

Citation-Based Ranking

Venue Quality Tiers

Tier 1 - Premier Venues (prefer when relevant):

• 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)
export PARALLEL_API_KEY="your_parallel_api_key"

# Academic search backend (Perplexity via OpenRouter)
export OPENROUTER_API_KEY="your_openrouter_api_key"

At least one key is required; with only one, every query goes to that backend.

API Specifications

Parallel Chat API (Beta):

• Endpoint: POST https://api.parallel.ai/v1beta/chat/completions (OpenAI SDK with base_url="https://api.parallel.ai/v1beta")
• Model: core (60s-5min latency, complex multi-source synthesis)
• Output: Markdown text with inline citations
• Citations: Research basis with source URLs and excerpts
• Rate limit: 300 requests/min by default
• Python package: openai

Perplexity sonar-pro-search:

• Model: perplexity/sonar-pro-search (via OpenRouter; $3/$15 per million tokens plus $18 per 1,000 requests at 2026-09 list prices)
• Sends web_search_options.search_context_size: high and Perplexity's search_mode: academic; OpenRouter may not forward the latter, so academic-only sourcing is best-effort
• Sources come back as url_citation annotations and/or citations/search_results fields; the script merges and deduplicates them
• The script waits up to 90 s per request
• Python package: requests

Command-Line Usage

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

# Force specific backend
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
python scripts/research_lookup.py "your query" --json -o sources/research_<topic>.json

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

Save All Results to the Sources Folder

Save every research-lookup result to the project's sources/ folder. Results cost money to obtain, and the saved file is what lets you (and reviewers) trace each citation back to the raw search output.

Saving Rules

How to Save

Pass -o pointing into sources/ on every call, and keep the citations, source URLs, and DOIs in the saved file — they are the audit trail. The default text output already includes a Sources section (title, date, URL for each source) and an Additional References section (DOIs and academic URLs extracted from the response text); use --json for the full citation metadata.

# 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 supports scientific writing by providing (each result saved to sources/):

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

Treat returned citations as leads: open and check any paper before citing it, because search-model answers can misattribute findings or garble bibliographic details (alterlab-citation-verifier checks that references exist).

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 preferred backend's API key is missing (including a --force-backend choice), the query goes to the other backend
• A call that fails is not retried on the other backend; the result carries success: false and the error — rerun with --force-backend to try the other one
• Rephrase queries for better results if the 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 alterlab-parallel-web for web search and URL extraction

Part of the AlterLab Academic Skills suite.