skills/parallel-web/SKILL.md
# Parallel Web Systems API Skill Overview ## Core Purpose This skill enables web search, content extraction, and comprehensive research using the Parallel Chat API and Extract API. It serves as the primary tool for all internet-based information gathering in scientific writing workflows. ## Key Capabilities **Search Function**: Delivers synthesized summaries with citations through the Parallel Chat API's base model, ideal for quick lookups and factual queries. **Deep Research**: Produces det
npx skillsauth add lamm-mit/scienceclaw skills/parallel-webInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill enables web search, content extraction, and comprehensive research using the Parallel Chat API and Extract API. It serves as the primary tool for all internet-based information gathering in scientific writing workflows.
Search Function: Delivers synthesized summaries with citations through the Parallel Chat API's base model, ideal for quick lookups and factual queries.
Deep Research: Produces detailed intelligence reports using the core model, best suited for market analysis, competitive intelligence, and multi-source synthesis.
URL Extraction: Limited to citation verification and special cases where specific URL content confirmation is needed.
The documentation emphasizes that "every web search and deep research result MUST be saved to the project's sources/ folder." This mandatory practice ensures reproducibility and maintains an audit trail of all research activities.
Two research models are available:
Users must configure the PARALLEL_API_KEY environment variable and install required Python packages (openai and parallel-web). The API platform is accessible at https://platform.parallel.ai.
The skill differentiates from academic-specific tools—purely scholarly queries should route to research-lookup instead, while general web searches remain within this skill's scope.
tools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.