Skill UI

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.

TileDB-VCF lets you ingest, store, query, and export large genomic VCF/BCF datasets using TileDB’s sparse arrays, supporting incremental sample growth, parallel regions queries, and cloud-local interoperability for population genomics workflows.

Zarr is a Python toolkit for chunked N-D array storage with compression, sharding, and native integrations with NumPy/Dask/Xarray, ideal for parallel I/O and scalable cloud pipelines.

Infers candidate gene regulatory networks from bulk or single-cell expression data using GRNBoost2 and GENIE3 algorithms. Ranks transcription factor-target associations with Dask-compatible parallel execution, sparse expression support, and network stability validation for biological research applications.

Dask is a Python library for parallel and distributed computing that scales pandas, NumPy, and custom workflows beyond memory limits. It supports DataFrames, Arrays, and Bags for processing large datasets across single machines or clusters. Ideal for scientific computing, ETL tasks, and parallelizing operations when data exceeds available RAM or requires distributed execution.

Pythonic wrapper around RDKit for drug discovery workflows: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel batch processing. Returns native RDKit Mol objects.

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

Compile verified scholarly evidence and references for scientific manuscripts or research briefs. Runs multi-pass Parallel Search with Extract source verification, ranked deduplication, and optional Research/Chat backends to deliver a manuscript-ready research packet targeting 60 unique references.

Store and process large N-dimensional arrays with chunking, compression, and parallel I/O. Supports cloud stores like S3/GCS via fsspec and integrates with NumPy, Dask, and Xarray for scientific pipelines.

Store and query genomic variant data with TileDB sparse arrays. Ingest VCF/BCF files, add samples incrementally, run parallel region queries, and export subsets for population genomics workflows.

A unified CLI tool that queries multiple web and academic search engines (Crossref, Brave, Tavily, DuckDuckGo) in parallel. It aggregates results, extracting structured metadata like DOI, publication year, and authors. Ideal for deep research, cross-checking claims, or comparing scholarly coverage against general web results.

This guide provides a comprehensive, advanced strategy for academic researchers writing microeconometric papers for top-tier journals (e.g., AEJ: Applied). It teaches authors how to anticipate sophisticated, technical objections from expert referees (such as issues with causal inference design, selection bias, or parallel trends). The focus is on pre-empting these objections by integrating solutions directly into the manuscript text, rather than waiting for a formal revision letter.