Results for “lightning-datatable”

Render long lists (1k+ rows) efficiently in Salesforce LWC using lightning-datatable infinite loading or an IntersectionObserver sentinel, with keyset pagination and accessibility guidance.

Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), and distributed training (DDP, FSDP, DeepSpeed) for scalable neural network training.

Builds a custom LWC lookup component with typeahead search via Apex SOSL/SOQL, debounce, keyboard navigation, and pill UI for cases where standard pickers fall short.

Guides using the lightning-record-picker base component for single-record selection in LWC, covering display-info, matching-info, graph-QL filters, and accessibility.

Enforce quality, security, and accessibility standards for Salesforce Lightning Web Components, Aura components, and Visualforce pages.

Documents MariaDB-specific ALTER TABLE syntax and behavior, including online DDL algorithms, lock modes, lock timeouts, idempotent subclauses, system versioning, and partition operations, for writing and reviewing schema migrations.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Build accessible modal dialogs in LWC using the LightningModal base class, covering open(), close(), sizes, and accessibility.

Creates a New York Times-style data chart frame with animated reveal, supporting line, bar, and range-band charts from CSV/JSON or a textual conclusion.

Manages biological datasets and models with LaminDB, covering setup, artifact registration, querying, lineage tracking, validation, ontology annotation, collections, branches, storage, and workflow integrations.

Provides MariaDB-specific syntax, atomicity, privilege requirements, and restrictions for RENAME TABLE statements, including multi-table swaps, temporary tables, and cross-database moves.

Write and run analytical SQL queries against Redpanda SQL (Oxla), a distributed PostgreSQL-wire-compatible columnar database, including data loading, external sources, and type handling.

Manage, annotate, and trace biological data with LaminDB, an open-source FAIR data framework that makes datasets queryable, versioned, and reproducible. Use when registering or querying biological datasets (scRNA-seq, spatial, flow cytometry), validating and curating data against ontologies (genes, cell types, diseases, tissues), tracking data lineage and computational workflows, building data lakehouses, or wiring integrations with Nextflow, Snakemake, W&B, or MLflow. Part of the AlterLab Academic Skills suite.

Builds memory-safe, performant Lightning Web Components with correct lifecycle hook usage, covering build, review, and troubleshooting workflows.

Creates, updates, and deletes Salesforce records from Lightning Web Components using Lightning Data Service, covering record input shapes, error handling, and cache refresh strategies.

Reads related records across multiple sObjects in one GraphQL request, paginates related lists with cursors, and replaces overlapping @wire(getRecord) calls with a single cache-shared query.

Manage biological datasets and models with LaminDB, an open-source lineage-native lakehouse. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation, collections, branches, storage, and workflow integrations.

Covers MariaDB-specific CREATE TABLE syntax and behavior, including atomic CREATE OR REPLACE, invisible and compressed columns, temporal PERIOD clauses, ignored indexes, encryption, page compression, and the utf8/utf8mb4 distinction.

Implements sibling communication between Lightning Web Components using Lightning Message Service (LMS) or the legacy pubsub utility, covering message channel definition, publish/subscribe patterns, and scope selection.

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

Explains MariaDB-specific TRUNCATE TABLE semantics: DROP privilege, implicit commit, AUTO_INCREMENT reset, trigger and FK behavior, and restrictions on system-versioned tables and sequences.

飞书多维表格（Base）：基于需求搭建系统和应用，或把已有数据、零散信息结构化，搭建成可持续记录、收集、整理、关联、协作、统计、提醒和流转的数据工具与管理系统。适用于个人、组织、企业的日常记录、清单登记、资料库、报名问卷、进度跟踪及项目/客户/订单/库存等系统和应用的生成需求；用户想把信息记下来、管起来、统计查看、持续更新或自动处理时使用，或提及 Base/多维表格/bitable、提供 Base 链接时使用；已有 Base 的查询、编辑和分析同样使用。

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Automate Snowflake data warehouse operations: list databases, schemas, and tables, execute SQL statements, and manage data workflows via the Composio MCP integration.

Compute, object storage, block storage, a managed relational database, a message

Access the ZINC database of 230M+ commercially available (purchasable) compounds, searching by ZINC ID or SMILES, running similarity searches, and downloading 3D-ready structures. Use when assembling a compound library for virtual screening, finding purchasable analogs, or obtaining docking-ready 3D structures for drug discovery. Part of the AlterLab Academic Skills suite.

Documents MariaDB-specific UPDATE syntax and behavior, including single-table vs multi-table forms, assignment evaluation order, RETURNING with OLD_VALUE(), temporal tables, and common pitfalls. Use when writing, generating, or reviewing UPDATE statements targeting MariaDB.

Databricks Job & Notebook Lineage (Lite)

Organizes PyTorch code with a Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. Scales from laptop to supercomputer with the same code.

Configure the Data Table screen component in Salesforce Screen Flows for row selection, including single/multi-select, column setup, and downstream consumption of selected rows.

Generates and reviews MariaDB CREATE SEQUENCE statements with version-specific syntax, defaults, and gotchas.

Develops and debugs Apex code, builds Lightning Web Components, optimizes SOQL queries, and manages Salesforce DX deployments with CI/CD pipelines.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Documents MariaDB-specific transaction control syntax and behavior, including START TRANSACTION, COMMIT, ROLLBACK, SAVEPOINT, implicit commit rules, autocommit defaults, and idle transaction timeouts.

Provision Bigtable instances, design performant schemas, and query data using gcloud, cbt, or client libraries.