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Added parallel-web skill
Refactor research lookup skill to enhance backend routing and update documentation. The skill now intelligently selects between the Parallel Chat API and Perplexity sonar-pro-search based on query type. Added compatibility notes, license information, and improved descriptions for clarity. Removed outdated example scripts to streamline the codebase.
This commit is contained in:
Vinayak Agarwal
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scientific-skills/parallel-web/references/workflow_recipes.md
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scientific-skills/parallel-web/references/workflow_recipes.md
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# Workflow Recipes
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Common multi-step patterns combining Parallel's Search, Extract, and Deep Research APIs for scientific writing tasks.
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---
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## Recipe Index
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| Recipe | APIs Used | Time | Use Case |
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|--------|-----------|------|----------|
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| [Section Research Pipeline](#recipe-1-section-research-pipeline) | Research + Search | 2-5 min | Writing a paper section |
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| [Citation Verification](#recipe-2-citation-verification) | Search + Extract | 1-2 min | Verifying paper metadata |
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| [Literature Survey](#recipe-3-literature-survey) | Research + Search + Extract | 5-15 min | Comprehensive lit review |
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| [Market Intelligence Report](#recipe-4-market-intelligence-report) | Research (multi-stage) | 10-30 min | Market/industry analysis |
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| [Competitive Analysis](#recipe-5-competitive-analysis) | Search + Extract + Research | 5-10 min | Comparing companies/products |
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| [Fact-Check Pipeline](#recipe-6-fact-check-pipeline) | Search + Extract | 1-3 min | Verifying claims |
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| [Current Events Briefing](#recipe-7-current-events-briefing) | Search + Research | 3-5 min | News synthesis |
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| [Technical Documentation Gathering](#recipe-8-technical-documentation-gathering) | Search + Extract | 2-5 min | API/framework docs |
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| [Grant Background Research](#recipe-9-grant-background-research) | Research + Search | 5-10 min | Grant proposal background |
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---
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## Recipe 1: Section Research Pipeline
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**Goal:** Gather research and citations for writing a single section of a scientific paper.
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**APIs:** Deep Research (pro-fast) + Search
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```bash
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# Step 1: Deep research for comprehensive background
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python scripts/parallel_web.py research \
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"Recent advances in federated learning for healthcare AI, focusing on privacy-preserving training methods, real-world deployments, and regulatory considerations (2023-2025)" \
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--processor pro-fast -o sources/section_background.md
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# Step 2: Targeted search for specific citations
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python scripts/parallel_web.py search \
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"Find peer-reviewed papers on federated learning in hospitals" \
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--queries "federated learning clinical deployment" "privacy preserving ML healthcare" \
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--max-results 10 -o sources/section_citations.txt
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```
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**Python version:**
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```python
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from parallel_web import ParallelDeepResearch, ParallelSearch
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researcher = ParallelDeepResearch()
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searcher = ParallelSearch()
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# Step 1: Deep background research
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background = researcher.research(
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query="Recent advances in federated learning for healthcare AI (2023-2025): "
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"privacy-preserving methods, real-world deployments, regulatory landscape",
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processor="pro-fast",
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description="Structure as: (1) Key approaches, (2) Clinical deployments, "
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"(3) Regulatory considerations, (4) Open challenges. Include statistics."
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)
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# Step 2: Find specific papers to cite
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papers = searcher.search(
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objective="Find recent peer-reviewed papers on federated learning deployed in hospital settings",
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search_queries=[
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"federated learning hospital clinical study 2024",
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"privacy preserving machine learning healthcare deployment"
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],
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source_policy={"allow_domains": ["nature.com", "thelancet.com", "arxiv.org", "pubmed.ncbi.nlm.nih.gov"]},
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)
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# Combine: use background for writing, papers for citations
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```
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**When to use:** Before writing each major section of a research paper, literature review, or grant proposal.
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---
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## Recipe 2: Citation Verification
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**Goal:** Verify that a citation is real and get complete metadata (DOI, volume, pages, year).
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**APIs:** Search + Extract
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```bash
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# Option A: Search for the paper
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python scripts/parallel_web.py search \
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"Vaswani et al 2017 Attention is All You Need paper NeurIPS" \
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--queries "Attention is All You Need DOI" --max-results 5
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# Option B: Extract metadata from a DOI
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python scripts/parallel_web.py extract \
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"https://doi.org/10.48550/arXiv.1706.03762" \
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--objective "Complete citation: authors, title, venue, year, pages, DOI"
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```
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**Python version:**
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```python
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from parallel_web import ParallelSearch, ParallelExtract
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searcher = ParallelSearch()
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extractor = ParallelExtract()
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# Step 1: Find the paper
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result = searcher.search(
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objective="Find the exact citation details for the Attention Is All You Need paper by Vaswani et al.",
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search_queries=["Attention is All You Need Vaswani 2017 NeurIPS DOI"],
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max_results=5,
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)
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# Step 2: Extract full metadata from the paper's page
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paper_url = result["results"][0]["url"]
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metadata = extractor.extract(
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urls=[paper_url],
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objective="Complete BibTeX citation: all authors, title, conference/journal, year, pages, DOI, volume",
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)
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```
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**When to use:** After writing a section, verify every citation in references.bib has correct and complete metadata.
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---
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## Recipe 3: Literature Survey
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**Goal:** Comprehensive survey of a research field, identifying key papers, themes, and gaps.
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**APIs:** Deep Research + Search + Extract
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```python
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from parallel_web import ParallelDeepResearch, ParallelSearch, ParallelExtract
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researcher = ParallelDeepResearch()
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searcher = ParallelSearch()
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extractor = ParallelExtract()
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topic = "CRISPR-based diagnostics for infectious diseases"
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# Stage 1: Broad research overview
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overview = researcher.research(
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query=f"Comprehensive review of {topic}: key developments, clinical applications, "
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f"regulatory status, commercial products, and future directions (2020-2025)",
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processor="ultra-fast",
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description="Structure as a literature review: (1) Historical development, "
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"(2) Current technologies, (3) Clinical applications, "
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"(4) Regulatory landscape, (5) Commercial products, "
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"(6) Limitations and future directions. Include key statistics and milestones."
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)
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# Stage 2: Find specific landmark papers
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key_papers = searcher.search(
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objective=f"Find the most cited and influential papers on {topic} from Nature, Science, Cell, NEJM",
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search_queries=[
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"CRISPR diagnostics SHERLOCK DETECTR Nature",
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"CRISPR point-of-care testing clinical study",
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"nucleic acid detection CRISPR review"
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],
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source_policy={
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"allow_domains": ["nature.com", "science.org", "cell.com", "nejm.org", "thelancet.com"],
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},
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max_results=15,
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)
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# Stage 3: Extract detailed content from top 5 papers
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top_urls = [r["url"] for r in key_papers["results"][:5]]
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detailed = extractor.extract(
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urls=top_urls,
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objective="Study design, key results, sensitivity/specificity data, and clinical implications",
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)
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```
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**When to use:** Starting a literature review, systematic review, or comprehensive background section.
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---
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## Recipe 4: Market Intelligence Report
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**Goal:** Generate a comprehensive market research report on an industry or product category.
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**APIs:** Deep Research (multi-stage)
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```python
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researcher = ParallelDeepResearch()
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industry = "AI-powered drug discovery"
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# Stage 1: Market overview (ultra-fast for maximum depth)
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market_overview = researcher.research(
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query=f"Comprehensive market analysis of {industry}: market size, growth rate, "
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f"key segments, geographic distribution, and forecast through 2030",
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processor="ultra-fast",
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description="Include specific dollar figures, CAGR percentages, and data sources. "
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"Break down by segment and geography."
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)
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# Stage 2: Competitive landscape
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competitors = researcher.research_structured(
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query=f"Top 10 companies in {industry}: revenue, funding, key products, partnerships, and market position",
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processor="pro-fast",
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)
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# Stage 3: Technology and innovation trends
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tech_trends = researcher.research(
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query=f"Technology trends and innovation landscape in {industry}: "
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f"emerging approaches, breakthrough technologies, patent landscape, and R&D investment",
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processor="pro-fast",
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description="Focus on specific technologies, quantify R&D spending, and identify emerging leaders."
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)
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# Stage 4: Regulatory and risk analysis
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regulatory = researcher.research(
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query=f"Regulatory landscape and risk factors for {industry}: "
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f"FDA guidance, EMA requirements, compliance challenges, and market risks",
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processor="pro-fast",
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)
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```
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**When to use:** Creating market research reports, investor presentations, or strategic analysis documents.
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---
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## Recipe 5: Competitive Analysis
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**Goal:** Compare multiple companies, products, or technologies side-by-side.
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**APIs:** Search + Extract + Research
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```python
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searcher = ParallelSearch()
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extractor = ParallelExtract()
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researcher = ParallelDeepResearch()
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companies = ["OpenAI", "Anthropic", "Google DeepMind"]
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# Step 1: Search for recent data on each company
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for company in companies:
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result = searcher.search(
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objective=f"Latest product launches, funding, team size, and strategy for {company} in 2025",
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search_queries=[f"{company} product launch 2025", f"{company} funding valuation"],
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source_policy={"after_date": "2024-06-01"},
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)
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# Step 2: Extract from company pages
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company_pages = [
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"https://openai.com/about",
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"https://anthropic.com/company",
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"https://deepmind.google/about/",
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]
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company_data = extractor.extract(
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urls=company_pages,
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objective="Mission, key products, team size, founding date, and recent milestones",
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)
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# Step 3: Deep research for synthesis
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comparison = researcher.research(
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query=f"Detailed comparison of {', '.join(companies)}: "
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f"products, pricing, technology approach, market position, strengths, weaknesses",
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processor="pro-fast",
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description="Create a structured comparison covering: "
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"(1) Product portfolio, (2) Technology approach, (3) Pricing, "
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"(4) Market position, (5) Strengths/weaknesses, (6) Future outlook. "
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"Include a summary comparison table."
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)
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```
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---
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## Recipe 6: Fact-Check Pipeline
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**Goal:** Verify specific claims or statistics before including in a document.
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**APIs:** Search + Extract
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```python
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searcher = ParallelSearch()
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extractor = ParallelExtract()
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claim = "The global AI market is expected to reach $1.8 trillion by 2030"
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# Step 1: Search for corroborating sources
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result = searcher.search(
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objective=f"Verify this claim: '{claim}'. Find authoritative sources that confirm or contradict this figure.",
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search_queries=["global AI market size 2030 forecast", "artificial intelligence market projection trillion"],
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max_results=8,
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)
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# Step 2: Extract specific figures from top sources
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source_urls = [r["url"] for r in result["results"][:3]]
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details = extractor.extract(
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urls=source_urls,
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objective="Specific market size figures, forecast years, CAGR, and methodology of the projection",
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)
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# Analyze: Do multiple authoritative sources agree?
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```
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**When to use:** Before including any specific statistic, market figure, or factual claim in a paper or report.
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---
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## Recipe 7: Current Events Briefing
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**Goal:** Get up-to-date synthesis of recent developments on a topic.
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**APIs:** Search + Research
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```python
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searcher = ParallelSearch()
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researcher = ParallelDeepResearch()
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topic = "EU AI Act implementation"
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# Step 1: Find the latest news
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latest = searcher.search(
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objective=f"Latest news and developments on {topic} from the past month",
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search_queries=[f"{topic} 2025", f"{topic} latest updates"],
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source_policy={"after_date": "2025-01-15"},
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max_results=15,
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)
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# Step 2: Synthesize into a briefing
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briefing = researcher.research(
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query=f"Summarize the latest developments in {topic} as of February 2025: "
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f"key milestones, compliance deadlines, industry reactions, and implications",
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processor="pro-fast",
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description="Write a concise 500-word executive briefing with timeline of key events."
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)
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```
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---
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## Recipe 8: Technical Documentation Gathering
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**Goal:** Collect and synthesize technical documentation for a framework or API.
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**APIs:** Search + Extract
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```python
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searcher = ParallelSearch()
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extractor = ParallelExtract()
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# Step 1: Find documentation pages
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docs = searcher.search(
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objective="Find official PyTorch documentation for implementing custom attention mechanisms",
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search_queries=["PyTorch attention mechanism tutorial", "PyTorch MultiheadAttention documentation"],
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source_policy={"allow_domains": ["pytorch.org", "github.com/pytorch"]},
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)
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# Step 2: Extract full content from documentation pages
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doc_urls = [r["url"] for r in docs["results"][:3]]
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full_docs = extractor.extract(
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urls=doc_urls,
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objective="Complete API reference, parameters, usage examples, and code snippets",
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full_content=True,
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)
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```
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---
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## Recipe 9: Grant Background Research
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**Goal:** Build a comprehensive background section for a grant proposal with verified statistics.
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**APIs:** Deep Research + Search
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```python
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researcher = ParallelDeepResearch()
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searcher = ParallelSearch()
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research_area = "AI-guided antibiotic discovery to combat antimicrobial resistance"
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# Step 1: Significance and burden of disease
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significance = researcher.research(
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query=f"Burden of antimicrobial resistance: mortality statistics, economic impact, "
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f"WHO priority pathogens, and projections. Include specific numbers.",
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processor="pro-fast",
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description="Focus on statistics suitable for NIH Significance section: "
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"deaths per year, economic cost, resistance trends, and urgency."
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)
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# Step 2: Innovation landscape
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innovation = researcher.research(
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query=f"Current approaches to {research_area}: successes (halicin, etc.), "
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f"limitations of current methods, and what makes our approach novel",
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processor="pro-fast",
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description="Focus on Innovation section: what has been tried, what gaps remain, "
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"and what new approaches are emerging."
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)
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# Step 3: Find specific papers for preliminary data context
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papers = searcher.search(
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objective="Find landmark papers on AI-discovered antibiotics and ML approaches to drug discovery",
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search_queries=[
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"halicin AI antibiotic discovery Nature",
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"machine learning antibiotic resistance prediction",
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"deep learning drug discovery antibiotics"
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],
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source_policy={"allow_domains": ["nature.com", "science.org", "cell.com", "pnas.org"]},
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)
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```
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**When to use:** Writing Significance, Innovation, or Background sections for NIH, NSF, or other grant proposals.
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---
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## Combining with Other Skills
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### With `research-lookup` (Academic Papers)
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```python
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# Use parallel-web for general research
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researcher.research("Current state of quantum computing applications")
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# Use research-lookup for academic paper search (auto-routes to Perplexity)
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# python research_lookup.py "find papers on quantum error correction in Nature and Science"
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```
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### With `citation-management` (BibTeX)
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```python
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# Step 1: Find paper with parallel search
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result = searcher.search(objective="Vaswani et al Attention Is All You Need paper")
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# Step 2: Get DOI from results
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doi = "10.48550/arXiv.1706.03762"
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# Step 3: Convert to BibTeX with citation-management skill
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# python scripts/doi_to_bibtex.py 10.48550/arXiv.1706.03762
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```
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### With `scientific-schematics` (Diagrams)
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```python
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# Step 1: Research a process
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result = researcher.research("How does the CRISPR-Cas9 gene editing mechanism work step by step")
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# Step 2: Use the research to inform a schematic
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# python scripts/generate_schematic.py "CRISPR-Cas9 gene editing workflow: guide RNA design -> Cas9 binding -> DNA cleavage -> repair pathway" -o figures/crispr_mechanism.png
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```
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---
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## Performance Cheat Sheet
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| Task | Processor | Expected Time | Approximate Cost |
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|------|-----------|---------------|------------------|
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| Quick fact lookup | `base-fast` | 15-50s | $0.01 |
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| Section background | `pro-fast` | 30s-5min | $0.10 |
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| Comprehensive report | `ultra-fast` | 1-10min | $0.30 |
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| Web search (10 results) | Search API | 1-3s | $0.005 |
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| URL extraction (1 URL) | Extract API | 1-20s | $0.001 |
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| URL extraction (5 URLs) | Extract API | 5-30s | $0.005 |
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---
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## See Also
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- [API Reference](api_reference.md) - Complete API parameter reference
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- [Search Best Practices](search_best_practices.md) - Effective search queries
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- [Deep Research Guide](deep_research_guide.md) - Processor selection and output formats
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- [Extraction Patterns](extraction_patterns.md) - URL content extraction
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Reference in New Issue
Block a user