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Design PostgreSQL tables by choosing data types, validation rules, and indexes that match actual queries. The skill also guides JSONB usage, partitioning large tables, and planning schema changes with attention to read and write workloads.
Manage BigQuery datasets, tables, views, and jobs, and run SQL queries for basic data analysis. Provides setup examples and references for data ingestion, command-line operations, client libraries, access permissions, and infrastructure configuration.
Search and update Airtable data through the airtable-mcp command-line tool. Find bases, inspect tables, filter records, and manage fields, with guidance on authentication, available operations, and structured data input.
Runs SQL queries on CloudWatch Logs data exported as Apache Iceberg tables in S3 Tables. Covers VPC Flow Logs, WAF logs, CloudFront access logs, Route 53 resolver logs, Network Firewall logs, EKS audit logs, Verified Access logs, SES logs, VPC Lattice logs, Step Functions logs, NLB access logs, and 20+ other AWS vended data sources. Applies when analyzing network traffic, investigating security incidents, querying exported logs with SQL, enabling S3 Tables integration, configuring log export, correlating logs with other data, or running Athena queries on the aws-cloudwatch table bucket. Trigger phrases: query logs with SQL, analyze logs in Athena, SQL on VPC flow logs, investigate network traffic, run SQL on exported logs, enable S3 Tables for CloudWatch, correlate logs, historical log analysis, set up log querying.
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Operates Amazon MSK Provisioned clusters (Standard and Express brokers). Required for ANY MSK Provisioned task — training data conflates Standard and Express. Covers performance, consumer lag, storage, traffic shaping; sizing Standard vs Express; Kafka client tuning; CloudWatch alarms; cluster configs; maintenance, patching, rolling restarts; topic management; MSK Replicator setup for DR and active-active/active-passive replication (same-/cross-account), including throughput and partition sizing; Streaming Tables for S3 Tables and Data Delivery for General Purpose S3 — setup, IAM, monitoring. Prefer Flink for Kafka Iceberg sink questions. Migrations: use migrate-to-msk. Triggers: MSK Provisioned, Kafka, `kafka.*`/`express.*` types, AWS/Kafka namespace, cross-cluster replication, Kafka to Iceberg on S3 Tables, Kafka to S3, lakehouse, data lake from Kafka, Kafka Connect S3 Sink or Firehose alternative. DO NOT USE for MSK Connect — search docs instead. Serverless: only S3 Tables/data delivery eligibility.
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Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Explore an existing PostgreSQL database to understand its objects and relationships - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates - Plan or execute safe schema migrations with zero downtime **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration
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Use when building or cleaning the tables and figures for an Academy of Management Journal (AMJ) manuscript — correlation tables, regression/SEM/HLM result tables, the theoretical-model figure, and interaction plots in AOM house style. Finalizes exhibits; it does not run the analysis (amj-data-analysis) or frame the contribution (amj-contribution-framing).
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Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).
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Comprehensive guide for migrating PostgreSQL tables to TimescaleDB hypertables with optimal configuration and performance validation
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N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.
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Load this skill whenever the project contains HTML data tables (<table> elements). Under no circumstances use tables for layout purposes. Absolutely always include <th> elements with appropriate scope attributes, a <caption> or aria-labelledby, and ensure complex tables have headers associated with data cells. Apply these rules to every data table without exception.
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Recipe for an agent building a small custom web app (an Applet) for one person or business on THEIR OWN store tables, live at aimatrx.com/applets/<slug>. Use when asked to build an app, portal, calendar, tracker, dashboard screen or client view on someone's tables/data in AI Matrx. NOT for platform features (use build-sub-feature) or tables the platform keeps for itself (defineTypedTable).
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Process PDF documents by extracting text, tables, and images, merging or splitting files, rotating pages, creating documents, and filling forms. It also covers password protection and OCR, which recognizes text in scanned pages to make them searchable.
Review large tables, feeds, and lists for rendering overhead. Introduce virtualization, which renders only visible items and a small buffer, while preserving keyboard access, selection, and expected scrolling behavior.
Keep comparison answers readable in a terminal about 100 columns wide. Limits Markdown tables to four columns with short phrases, and switches to lists or per-item headings when the content needs longer explanations.
Read, create, and organize PDF files, including merging documents, splitting pages, extracting tables, and filling forms. It also covers password protection and OCR, which recognizes text in scanned pages for extraction or search.
Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.
Find biological pathways and functions enriched in gene lists or ranked gene data. Choose an appropriate statistical method, align gene identifiers, define comparison backgrounds, and turn results into interpretable tables and plots with multiple-testing corrections.
Create Chinese-English reading materials that connect a scientific paper’s text with its figures, tables, and equations. It also handles selected excerpts and source-linked questions, keeping translations and explanations tied to the available original material.
Helps implement document processing in TypeScript with Azure, extracting text, tables, invoice fields, and receipt details. Covers ready-made models, custom model training, document classification, authentication, and waiting for analysis results.
Extract text, tables, and images from PDFs; merge, split, rotate, or create documents; and fill forms. The skill also covers password protection and optical character recognition, which reads text from scanned pages to make documents searchable.
Extract text and tables from PDFs, fill form fields with supplied data, and merge documents. Includes a pdfplumber text extraction example and scripts for inspecting form fields and extracting text.
Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.
Design PostgreSQL tables around actual queries and data volumes. Choose data types, validation rules, and indexes, then plan large table partitioning, access rules for individual rows, and schema changes with migration and rollback considerations.