Skill UI

Pythonic RDKit wrapper for molecular cheminformatics: parse and standardize SMILES, compute descriptors and fingerprints, cluster compounds, generate 3D conformers, and run parallel drug-discovery workflows.

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.

This skill generates actionable implementation tasks from product specifications, requirements, and design documents. It ensures tasks are properly sized, mapped to requirements, and include clear completion criteria. It supports parallel or sequential execution modes and includes a sanity review gate to verify task executability before finalizing the task plan.

Interactive technical design quality review tool that validates design documents against requirements, identifies critical issues, and provides GO/NO-GO decisions with actionable feedback for implementation readiness. Supports multi-agent parallel research and follows structured review process.

A comprehensive guide for developers to optimize performance when integrating Adobe APIs (Firefly, PDF, Photoshop). Techniques covered include implementing token caching, parallelizing asynchronous job submissions, leveraging response caching (LRU), and optimizing connection handling to minimize latency and maximize throughput.

This comprehensive guide details advanced strategies for optimizing performance when interacting with blockchain data using the Alchemy SDK. Techniques covered include implementing TTL-based caching for data freshness, achieving parallel multi-chain fetches, utilizing batch API calls (e.g., NFT metadata) to reduce Compute Unit (CU) costs, and adopting WebSockets for real-time event subscriptions. Essential for scaling robust, low-latency dApps.

This guide outlines advanced strategies to optimize latency and throughput when integrating Anthropic's Claude API. Learn critical techniques such as streaming responses, prompt caching, model selection (Haiku, Sonnet, Opus), and request parallelization, ensuring your applications provide a low-latency, high-performance user experience.

This guide provides advanced methods to significantly boost Clari API performance and data pipeline efficiency. It covers parallelizing multi-period data exports using Python's concurrency features, implementing a robust local caching mechanism with Time-To-Live (TTL), and optimizing data warehouse loading using SQL MERGE statements. Use this when facing slow exports, high API call volume, or requiring fast data synchronization in multi-forecast environments.

This guide provides techniques to significantly boost performance when integrating with the Hex API. It covers essential optimization strategies such as implementing LRU caching for project lists, running multiple independent jobs in parallel, and adopting adaptive polling intervals to manage API latency and resource usage effectively. Use this when facing slow data retrieval or requiring high-throughput data processing.

This guide provides advanced techniques to optimize AssemblyAI's transcription performance for large-scale applications. Learn how to maximize throughput and minimize latency by implementing caching strategies (Redis/LRU), utilizing webhooks instead of polling, selecting the optimal speech model based on needs, and parallelizing batch processing workloads.