Skill UI

This comprehensive reference architecture details a multi-stage pipeline for web scraping and content ingestion using Firecrawl. It seamlessly integrates single-page scraping, site-wide crawling, URL mapping, and structured data extraction. The subsequent processing layer handles cleaning, deduplication, and chunking, making this blueprint ideal for designing robust knowledge bases, RAG systems, and complex AI data pipelines.

Enables efficient querying of the bioRxiv preprint server with keywords, authors, dates, and categories, returning structured JSON metadata and optional PDF downloads for literature reviews or tracking research trends.

Datamol wraps RDKit with Pythonic APIs tailored for drug discovery, offering SMILES/SELFIES/InChI parsing, standardization, descriptor and fingerprint calculation, clustering, 3D conformer workflows, and parallel/cloud-aware batch I/O while returning native rdkit.Chem.Mol objects.

deepTools is a Python command-line suite for NGS workflows, converting BAM files into normalized coverage, running QC (fingerprint, correlation, PCA), comparing samples, and rendering heatmaps or profiles for ChIP-seq, RNA-seq, and ATAC-seq analyses.

RDKit delivers a Python-based cheminformatics toolkit for parsing SMILES/SDF, computing descriptors, generating 2D/3D coordinates, fingerprinting, searching substructures, managing reactions, and visualizing molecules, ideal for drug discovery and computational chemistry workflows.

A specialized tool for finding peer-reviewed academic papers from major venues like IEEE, ACM, and Springer. Unlike preprint services (e.g., arXiv), this skill provides published literature with citation counts, detailed venue metadata, and a TLDR summary, ensuring access to verified research findings.

Datamol is a high-level, Pythonic abstraction layer built over RDKit, designed for computational chemistry and drug discovery. It simplifies complex molecular operations, offering sensible defaults for tasks like SMILES standardization, descriptor calculation, fingerprint generation (ECFP4), 3D conformer analysis, and molecular clustering. It supports efficient parallel processing and cloud storage I/O, making it ideal for handling large-scale chemical datasets.

DeepChem is a comprehensive Python library designed to apply machine learning to chemistry, materials science, and biology. It enables sophisticated molecular property prediction (ADMET, solubility, toxicity), drug discovery, and materials design. The library supports diverse featurization methods (fingerprints, graph representations) and advanced model architectures, including GNNs and transfer learning, making it ideal for analyzing complex chemical datasets like MoleculeNet.

Utilize Bright Data's Scraping Browser with Playwright or Puppeteer to reliably scrape structured data from JavaScript-rendered pages, Single Page Applications (SPAs), and anti-bot-protected sites. This tool manages proxy routing, CAPTCHA solving, and fingerprint management, ensuring robust data collection from complex web environments.

This comprehensive guide demonstrates how to scrape live webpages using Bright Data's Web Unlocker proxy. It details how to bypass advanced anti-scraping mechanisms, such as CAPTCHAs and fingerprinting, automatically. Users are provided with full code examples in both Node.js and Python, showing methods for scraping via the proxy endpoint and through the dedicated REST API. Ideal for starting new scraping projects or integrating Bright Data into existing workflows.

This reference architecture provides a comprehensive blueprint for building a robust revenue intelligence data platform integrated with Clari. It covers the entire data lifecycle, including API data extraction (ETL pipeline), data warehousing design (Snowflake/BQ), change detection logic, and advanced analytics/alerting layers. Use this guide when designing a scalable forecast tracking system, planning complex data integrations, or establishing best practices for enterprise revenue analytics.

This skill integrates 10 major academic literature APIs (including PubMed, arXiv, OpenAlex, Crossref, etc.). It guides users on converting complex research intents into reproducible, verifiable lookups for papers, preprints, citations, and open-access full texts. It ensures results include verifiable provenance and follows a rigorous workflow for maximum accuracy in scholarly research.