Results for “pr”

Filters and prioritizes compound libraries in drug discovery using drug-likeness rules, structural alerts, complexity metrics, and a query language.

Provides reference documentation and code patterns for using Biopython to handle biological sequences, file formats, database access, alignments, structures, and phylogenetics.

Recalibrates conservative Kiro cache-estimation coefficients from recent successful usage samples, producing a recommendation for manual admin Kmodel updates.

Teaches standard SQL for creating, managing, and querying relational databases, covering DDL, DML, DQL, DCL, joins, subqueries, and best practices.

Performs deterministic image operations with Python Pillow: resizing, cropping, compositing, format conversion, watermarks, brightness/contrast adjustments, and web optimization.

Process PDFs with Python and command-line tools: extract text and tables, merge, split, rotate, create, watermark, OCR, and handle passwords.

Provides guidance for using the Astropy Python library in astronomical research, covering coordinates, units, FITS files, cosmology, tables, time, and WCS transformations.

Dask parallel computing reference for Python. Covers Dask DataFrame (parallel Pandas), Dask Array (parallel NumPy), Dask Delayed for custom parallelism, Dask Bag, distributed clusters, dashboard monitoring, and scaling best practices.

Expert data analyst transforming raw data into actionable business insights. Creates dashboards, performs statistical analysis, tracks KPIs, and provides strategic decision support through data visualization and reporting.

Use essa skill para processar e analisar grandes conjuntos de dados tabulares (bilhões de linhas) que excedem a RAM disponível. Vaex excels em operações DataFrame out-of-core, avaliação lazy, agregações rápidas, visualização eficiente de big data e machine learning em datasets grandes. Aplique quando usuários precisarem trabalhar com arquivos CSV/HDF5/Arrow/Parquet grandes, realizar estatísticas rápidas em datasets massivos, criar visualizações de big data ou construir pipelines de ML que não cabem em memória.

Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Design schemas, write TypeScript functions, set up real-time subscriptions, authentication, file storage, scheduling, and deployment for Convex reactive backend projects.

Plan, design, and implement A/B tests and experiments with statistical rigor, or build a growth experimentation program.

Transcribe audio to text using ElevenLabs Scribe v2, supporting 90+ languages, speaker diarization, and word-level timestamps.

Get best practices for Entity Framework Core, covering data context design, entity design, performance, migrations, querying, change tracking, security, and testing.

Diagnoses Qdrant search quality issues by isolating causes like HNSW approximation, quantization, embedding model, or search pipeline problems.

Automates PDF.co operations through Composio's toolkit via Rube MCP, including tool discovery, connection management, and execution.

Guides implementing authentication, authorization, input validation, and OWASP Top 10 prevention with secure code patterns and validation checkpoints.

Create clear, effective data visualizations with chart selection, color theory, and annotation best practices using the inference.sh CLI.

Queries 20+ bioinformatics databases from the command line or Python for gene info, sequences, BLAST/BLAT, protein structures, viral data, and expression metrics.

Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.

Provides guidance on using the Astropy library for astronomy and astrophysics workflows, including units, coordinates, FITS I/O, tables, time, WCS, and cosmology.

Sets up Git-based version control for data science projects, handling notebooks, datasets, and pipelines with DVC and nbstripout.

Scans local repositories and a Supabase database to inventory all pathway, portal, and theme content, producing a categorized report organized by portal.

Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.

Provides data-driven endurance cycling coaching using Intervals.icu data, including fitness assessment, workout planning, and power curve analysis.

Process PDFs with Python libraries and command-line tools: extract text and tables, create, merge, split, rotate, watermark, encrypt, and OCR documents.

Process tabular data with Polars' expression API, lazy evaluation, and parallel execution for fast in-memory analysis and pandas migration.

Runs NGS read QC, alignment, and BAM processing, wrapping FastQC, BWA/Bowtie2/Minimap2, SAMtools, and MultiQC for automated read-to-BAM workflows.

Provides ClickHouse-specific patterns for schema design, query optimization, data ingestion, and analytics, including materialized views and performance monitoring.

Ingests EPUB and ebook files into a local vector index and provides a command-line interface for asking questions about the books.

Provides Python bindings to interact with the DNAnexus platform, enabling file uploads, job management, and API calls.

Extracts text from PDFs and scanned documents, choosing the cheapest method that works, from direct file reads to full OCR.

Optimize SQL queries, design database schemas, and tune performance across cloud-native and hybrid OLTP/OLAP environments.

Computes the SQuAD metric using torchmetrics, given predictions and ground truth. Use when evaluating question-answering outputs with exact match and F1 scores.

DataFrame operations with pandas and polars — groupby, joins, reshaping, performance. Use when manipulating tabular data, choosing between pandas and polars, optimizing DataFrame code, or translating between the two libraries.