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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| 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 |
| metadata | {"skill-author":"K-Dense Inc."} |
This skill provides real-time research information lookup with intelligent backend routing:
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.The skill automatically detects query type and routes to the optimal backend.
Use this skill when you need:
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:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The skill automatically routes queries to the best backend based on content:
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)
Queries containing these terms are routed to Perplexity for academic-focused search:
find papers, find articles, research papers on, published studiescite, citation, doi, pubmed, pmidpeer-reviewed, journal article, scholarly, arxiv, preprintsystematic review, meta-analysis, literature searchfoundational papers, seminal papers, landmark papers, highly citedAll other queries go to the Parallel Chat API (core model), including:
You can force a specific backend:
# Force Parallel Deep Research
python research_lookup.py "your query" --force-backend parallel
# Force Perplexity academic search
python research_lookup.py "your query" --force-backend perplexity
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:
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:
Query Examples:
- "Western blot protocol for protein detection"
- "Statistical power analysis for clinical trials"
- "Machine learning model evaluation metrics comparison"
Query Examples:
- "Prevalence of diabetes in US population 2025"
- "Global AI market size and growth projections"
- "COVID-19 vaccination rates by country"
CRITICAL: When searching for papers, ALWAYS prioritize high-quality, influential papers.
| 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 |
Tier 1 - Premier Venues (Always prefer):
Tier 2 - High-Impact Specialized (Strong preference):
Tier 3 - Respected Specialized (Include when relevant):
# 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"
Parallel Chat API:
https://api.parallel.ai (OpenAI SDK compatible)core (60s-5min latency, complex multi-source synthesis)openaiPerplexity sonar-pro-search:
perplexity/sonar-pro-search (via OpenRouter)# 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
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.
| 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 |
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
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:
sources/ folder documents exactly how all research information was gatheredsources/ for existing results before making new API callsBefore 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.
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)
This skill enhances scientific writing by providing:
sources/sources/sources/sources/sources/| 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) |
Known Limitations:
Fallback Behavior:
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.
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.
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.
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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