research-lookup（研究查找）

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. 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
    |
    +-- Needs deep multi-source synthesis? (user says "deep research", "exhaustive")
    |       YES --> Parallel Chat API (core model, 60s-5min)
    |
    +-- Everything else (general research, academic queries, market data, technical info)
            --> 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-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 parallel-cli with academic domains)

Queries containing these terms trigger the two-search pattern with academic domain prioritization:

• 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 Chat API (deep research, slow)
python research_lookup.py "your query" --force-backend parallel-chat

# Force parallel-cli (explicit)
python research_lookup.py "your query" --force-backend parallel-cli

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 (parallel-cli with academic domains)

Uses the two-search pattern when academic keywords are detected. Prioritizes scholarly databases via --include-domains.

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:

• Results from academic domains (arxiv, pubmed, nature, etc.) and general web
• Title, URL, publish date, and content excerpts for each source
• Sources from peer-reviewed journals, preprints, and institutional sites

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

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

# Primary backend (parallel-cli) - REQUIRED
# Install parallel-cli if not already available:
curl -fsSL https://parallel.ai/install.sh | bash
# Or: uv tool install "parallel-web-tools[cli]"

# Authenticate:
parallel-cli auth
# Or: export PARALLEL_API_KEY="your_parallel_api_key"

Environment Variables

# Authenticate parallel-cli (primary backend)
parallel-cli auth
# Or set API key directly:
export PARALLEL_API_KEY="your_parallel_api_key"

# Deep research backend (Parallel Chat API) - optional, same key
# Uses PARALLEL_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

Command-Line Usage

# Fast web search via parallel-cli (DEFAULT — recommended) — ALWAYS save to sources/
parallel-cli search "your query" -q "keyword1" -q "keyword2" \
  --json --max-results 10 --excerpt-max-chars-total 27000 \
  -o sources/research_<topic>.json

# Academic-focused search via parallel-cli — ALWAYS save to sources/
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

# Time-sensitive search via parallel-cli
parallel-cli search "your query" -q "keyword" \
  --json --max-results 10 --after-date 2024-01-01 \
  -o sources/research_<topic>.json

# Extract full content from a specific URL (use parallel-web extract)
parallel-cli extract "https://example.com/paper" --json

# Force Parallel Deep Research (slow, exhaustive) — via research_lookup.py
python research_lookup.py "your query" --force-backend parallel -o sources/research_<topic>.md

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

# Batch queries via research_lookup.py — ALWAYS save to sources/
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

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 (DEFAULT) — save JSON to sources/
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

# Deep research via Parallel Chat API — save to sources/
python research_lookup.py "AI regulation landscape" --force-backend parallel-chat \
  -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:

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).

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-cli search: Requires parallel-cli to be installed and authenticated
• Parallel Chat API (core model): Complex queries may take up to 5 minutes
• All backends: Cannot access proprietary or restricted databases
• parallel-cli: Requires installation and authentication
• Parallel Chat API: Complex queries may take up to 5 minutes

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 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 parallel-cli with academic domains)

Query: "Find papers on CRISPR off-target effects in clinical trials"

Backend: parallel-cli search with --include-domains targeting scholarly sources

Response: Results from arxiv, pubmed, nature, science, and other academic sources with titles, URLs, dates, and content excerpts.

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)
• 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