research-lookup

name	research-lookup
description	Look up current research information using saved source searches. Prefer direct parallel-cli search commands for fast web search when available; use research_lookup.py for API-routed Parallel Chat deep research or Perplexity academic searches. Use for finding papers, gathering research data, and verifying scientific information. Note: API-routed queries are transmitted to api.parallel.ai (PARALLEL_API_KEY) or openrouter.ai (OPENROUTER_API_KEY).
allowed-tools	Read Write Edit Bash
license	MIT license
compatibility	parallel-cli recommended for fast saved web searches; PARALLEL_API_KEY and OPENROUTER_API_KEY optional for research_lookup.py API backends
metadata	{"skill-author":"K-Dense Inc."}

Research Information Lookup

Overview

This skill provides real-time research information lookup with two execution paths:

• Direct CLI path: parallel-cli search is the recommended fast path for standard web searches when the CLI is installed and authenticated. Run it explicitly and save JSON to sources/.
• Python API helper: research_lookup.py routes only between the Parallel Chat API and Perplexity/OpenRouter. It does not call parallel-cli internally.

Use the CLI path first for ordinary literature/background searches. Use research_lookup.py when you explicitly need deep synthesis, academic-focused Perplexity search, batch API lookup, or a CLI fallback is unavailable.

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

---

Backend Selection

Select the path explicitly:

Routing Logic

Query arrives
    |
    +-- Is parallel-cli installed and this is a standard search?
    |       YES --> Run parallel-cli search directly and save JSON to sources/
    |
    +-- Need deep synthesis or API-routed batch output?
    |       YES --> Run research_lookup.py --force-backend parallel, or auto-route
    |
    +-- Need academic Perplexity search for papers/DOIs?
    |       YES --> Run research_lookup.py --force-backend perplexity, or auto-route
    |
    +-- No backend/API key available?
            --> Use available web search tools and save sources manually

Recommended fast path: parallel-cli search

Primary command path for all standard research queries. Fast, cost-effective, and supports academic source prioritization when parallel-cli is installed and authenticated.

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

research_lookup.py academic routing

When using research_lookup.py without --force-backend, queries containing these terms route to Perplexity when OPENROUTER_API_KEY is available:

• 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

research_lookup.py deep research routing

When using research_lookup.py, deep, exhaustive, or comprehensive queries route to the Parallel Chat API when PARALLEL_API_KEY is available. This path is much slower 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 Deep Research (slow, exhaustive)
python research_lookup.py "your query" --force-backend parallel

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

---

Core Capabilities

1. General Research Queries (parallel-cli search — DEFAULT)

Recommended fast path. Use direct parallel-cli search commands for fast saved web search with academic source prioritization.

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 (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

# Recommended fast path (parallel-cli)
# 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

# API key for parallel-cli search or Parallel Chat API when available
export PARALLEL_API_KEY="your_parallel_api_key"

# Deep research backend (Parallel Chat API) - optional, for deep research only
# Uses the same PARALLEL_API_KEY

# Academic search backend (Perplexity) - optional, for academic paper queries
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

# 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

# Force Perplexity academic search — via research_lookup.py
python research_lookup.py "your query" --force-backend perplexity -o sources/papers_<topic>.md

# API-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

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 (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

# Academic paper search via Perplexity — save to sources/
python research_lookup.py "Find papers on transformer attention mechanisms in NeurIPS 2024" \
  -o sources/papers_20250217_143500_transformer_attention.md

# Deep research via Parallel Chat API — 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
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

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

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 when available
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	Use available citation tools if installed; otherwise use Semantic Scholar/CrossRef/arXiv and save sources
PubMed search	Use PubMed/NCBI web or API tools if available; save sources
DOI to BibTeX	Use CrossRef DOI-to-BibTeX or mark [CITATION NEEDED] if unresolved
Metadata verification	parallel-cli extract when available

---

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 a clear command/API split:

• parallel-cli search: Recommended fast path for standard saved searches when installed
• Parallel Chat API (core model): Deep, exhaustive multi-source synthesis via research_lookup.py
• Perplexity sonar-pro-search: Academic-specific paper searches via research_lookup.py
• Automatic API routing: research_lookup.py chooses between Parallel and Perplexity, not parallel-cli
• Manual override: Force API backends when needed
• Academic prioritization: Two-search pattern ensures scholarly sources surface for scientific queries