research-lookup — Автоматизированный поиск информации для исследователей

--- 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. --- # 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 ## 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 ```bash 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 | +-- 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: ```bash # 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 ``` --- ## 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 | 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 ### Environment Variables ```bash # 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 URLs, reasoning, and confidence levels - Rate limits: 300 req/min - 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 ```bash # 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 ``` --- ## 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 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 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`. ```bash # 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 ``` ### Citation Preservation in Saved Files 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:** - 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: ```bash 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 | `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`) | --- ## 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 --- ## 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