Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control.
WHAT YOU BECOME
Perfect for these scenarios
Document microservices, serverless, or event-driven patterns with reusable templates
Create standardized CI/CD, monitoring, or deployment guides for teams
Build consistent onboarding guides for common coding practices
Generate reusable UI component documentation with live examples
MEASURED GAIN
Proven benefits and measurable impact
Reduce guide creation time from 2 hours to 25 minutes with auto-research
Improve pattern adoption across teams with consistent, minimal guides
Accelerate new project implementation with reusable pattern documentation
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
Recommended based on tags and category
Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Expert on Anthropic Claude API, models, prompt engineering, function calling, vision, and best practices. Triggers on anthropic, claude, api, prompt, function calling, vision, messages api, embeddings
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.