Scientific Research Copilot

The problem

General-purpose agents often hallucinate scientific API endpoints, fail to handle complex bioinformatics file formats, or ignore reproducibility standards. This leads to broken pipelines and untrustworthy results in data-heavy research workflows.

What it does

• Provides curated entry points for genomics, cheminformatics, and clinical data APIs.
• Automates structured processing for FASTA, VCF, BAM, and molecular structure files.
• Generates publication-ready figures with proper units, legends, and colorblind-safe palettes.
• Enforces reproducibility by logging library versions, seeds, and intermediate data states.

Frameworks & tools

Python 3.11+, RDKit, Biopython, Scanpy, Pandas, Matplotlib, Scikit-learn, and Geopandas.

Why this beats prompting it yourself

Manually prompting for scientific workflows often results in agents guessing outdated database parameters or missing critical QC steps like doublet removal. This skill provides a domain-specific map that forces the agent to use verified library patterns and multi-step validation protocols.

Use cases

• Identify and rank potential inhibitors for protein targets using chemical databases.
• Perform quality control and UMAP clustering on 10X single-cell expression data.
• Annotate pathogenicity and protein impact for variants found in VCF files.
• Build and compare time-series forecasting models for sensor data with residual analysis.

Known limitations

Requires a local Python environment with specific scientific libraries installed. Large-scale genomic or GPU-intensive tasks depend on your local hardware capacity.