Claude Skills by majiayu000

Implement client-side data storage with localStorage, IndexedDB, or SQLite WASM. Use when storing user preferences, caching data, or building offline-first applications.

Transforms raw data into compelling visual narratives using Python or R, focusing on clarity, insight, and aesthetic presentation.

This skill should be used when reading any tabular data file (Excel, CSV, Parquet, ODS). It automatically detects and fixes common data issues including multi-level headers, encoding problems, empty rows/columns, and data type mismatches. Returns a clean DataFrame ready for analysis with zero user intervention.

Recommend basic data structures for a task. Use when a junior developer needs help choosing lists, maps, or sets.

Python data structure conventions for this codebase. Apply when choosing between Pydantic models, dataclasses, and other data containers.

For data

Analyze fundamental data primitives, type systems, and state management patterns in a codebase. Use when (1) evaluating typing strategies (Pydantic vs TypedDict vs loose dicts), (2) assessing immutability and mutation patterns, (3) understanding serialization approaches, (4) documenting state shape and lifecycle, or (5) comparing data modeling approaches across frameworks.

Synthesize findings from multiple research sources into coherent insights. Use when combining data from different sub-agents or research threads.

Use when designing databases for data-heavy applications, making schema decisions for performance, choosing between normalization and denormalization, selecting storage/indexing strategies, planning for scale, or evaluating OLTP vs OLAP trade-offs. Also use when encountering N+1 queries, ORM issues, or concurrency problems.

Create Next.js data table pages with SSR initial load, SWR caching, and server-response-based UI updates. Use when asked to create a new data table page, entity management page, CRUD table, or admin list view. Generates page.tsx (SSR), table components, columns, context, actions, and API routes following a proven architecture with centralized reusable data-table component.

Helps with data stuff

Data-to-UI pipeline patterns. Use when transforming JSON data into React components, creating TypeScript types from schemas, building derived types, or creating data utilities.

Manage AI training data, monitor content freshness, detect repetition, and update training samples for continuous learning. Use when managing training data, checking content quality, updating AI models, or preventing repetitive content.

Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

Convert between data formats (JSON, CSV, XML, YAML, TOML). Handles nested structures, arrays, and preserves data types where possible.

This skill should be used when the user asks about "Effect Option", "Effect Either", "Option.some", "Option.none", "Either.left", "Either.right", "Cause", "Exit", "Chunk", "Data", "Data.TaggedEnum", "Data.Class", "Duration", "DateTime", "HashMap", "HashSet", "Redacted", or needs to understand Effect's built-in data types and functional data structures.

Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.

Implementing comprehensive validation rules across database, application, and pipeline layers to ensure data integrity.

Comprehensive data validation framework for testing schema compliance, data quality, and referential integrity. Validates databases, APIs, data pipelines, and file formats. Generates data quality scorecards with anomaly detection across completeness, accuracy, and consistency.

Validate data against schemas, business rules, and data quality standards.

Creating effective data visualizations using charts, graphs, and visual representations to communicate insights clearly and accurately following Tufte and Few principles.

Provides expert design guidance for creating truthful, clear, beautiful data visualizations. Focuses on **DESIGN DECISIONS ONLY**—chart selection, color strategy, visual encoding, and validation. Assumes data is accurate and prepared. Auto-activates when user mentions: data viz, dashboard, chart type, visualization, infographic

专业数据可视化专家，精通现代图表库、仪表板设计和交互式数据展示。能够将复杂数据转化为直观、美观且富有洞察力的可视化作品。

Generate data visualizations, plots, and charts. Analyzes data structure to select optimal visualization types. supports bar charts, line graphs, and scatter plots for clarity.

Build mathematically correct, visually prominent data visualizations for time-series charts. Use this skill when creating charts with mathematical overlays (trendlines, patterns, indicators), fixing visual artifacts (wavy lines, domain mismatches), or validating chart correctness. Focuses on technical correctness and progressive validation, not aesthetic design.

Automated data visualization for EDA, model performance, and business reporting. Activates for "visualize data", "create plots", "EDA", "exploratory analysis", "confusion matrix", "ROC curve", "feature distribution", "correlation heatmap", "plot results", "dashboard". Generates publication-quality visualizations integrated with SpecWeave increments.

State-of-the-art data visualization for React/Next.js/TypeScript with Tailwind CSS. Creates compelling, tested, and accessible visualizations following Tufte principles and NYT Graphics standards. Activate on "data viz", "chart", "graph", "visualization", "dashboard", "plot", "Recharts", "Nivo", "D3". NOT for static images, print graphics, or basic HTML tables.

Interactive data exploration and visualization skill. Use when users ask to visualize data, analyze datasets, create charts, or explore data files (CSV, Excel, Parquet, JSON). This skill guides through data exploration, proposes visualization strategies based on data characteristics, creates interactive Plotly charts in marimo notebooks, and generates analytical conclusions.

Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

Design dimensional models and fact tables.

Data warehouse (大数据/数仓) ops skill for designing and operating analytical data platforms. Use for tasks like defining source-of-truth, building ETL/ELT pipelines, dimensional modeling (star schema), data quality checks, partitioning strategies, cost/performance tuning, governance, lineage, and SLA monitoring.

Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture

Data processing expert - ETL, transformation, visualization

Transform and export data using DuckDB SQL. Read CSV/Parquet/JSON/Excel/databases, apply SQL transformations (joins, aggregations, PIVOT/UNPIVOT, sampling), and optionally write results to files. Use when the user wants to: (1) Clean, filter, or transform data, (2) Join multiple data sources, (3) Convert between formats (CSV→Parquet, etc.), (4) Create partitioned datasets, (5) Sample large datasets, (6) Export query results. Prefer this over in-context reasoning for datasets with thousands of...

AgentSkill for https://idx.md. Use the index to locate AI agent library topics and fetch HEAD/BODY markdown.

Expert for developing Streamlit data apps for Keboola deployment. Activates when building, modifying, or debugging Keboola data apps, Streamlit dashboards, adding filters, creating pages, or fixing data app issues. Validates data structures using Keboola MCP before writing code, tests implementations with Playwright browser automation, and follows SQL-first architecture patterns.

データベースアダプター（PostgreSQL/MySQL両対応）の開発・修正を行う際に使用。SqlExecutorパターン、マイグレーション、DB固有SQL実装時に役立つ。

Automate backups, replication, and performance tuning.

Copilot agent that assists with database operations, performance tuning, backup/recovery, monitoring, and high availability configuration Trigger terms: database administration, DBA, database tuning, performance tuning, backup recovery, high availability, database monitoring, query optimization, index optimization Use when: User requests involve database administrator tasks.

Use SQL (PostgreSQL) when:

Analyze and optimize database schemas, identify performance issues, and suggest improvements. Use when working with database structure, indexes, or query performance.

Role assignment for Claude Agent #1 - Database schema architect for Lead Hunter Prime. Build ONLY database schema (11 tables, RLS policies, seed data). Do NOT build APIs, dashboards, or N8N workflows.

Database architecture and design specialist. Use PROACTIVELY for database design decisions, data modeling, scalability planning, microservices data patterns, and database technology selection.

MANDATORY when designing schemas, writing migrations, creating indexes, or making architectural database decisions - enforces PostgreSQL 18 best practices including AIO, UUIDv7, temporal constraints, and modern indexing strategies

MANDATORY when designing schemas, writing migrations, creating indexes, or making architectural database decisions - enforces PostgreSQL 18 best practices including AIO, UUIDv7, temporal constraints, and modern indexing strategies

Activates when user needs help with database design, SQL queries, migrations, or ORM usage. Triggers on "database schema", "SQL query", "migration", "optimize query", "foreign key", "index", "normalize", "ORM", "Prisma", "TypeORM", "SQLAlchemy", or database-related questions.

Auditoria e análise de bancos de dados para identificar anomalias, inconsistências, registros órfãos, duplicatas, índices faltantes, e problemas de integridade referencial. Usar para diagnosticar problemas de dados, preparar migrações, gerar relatórios de qualidade de dados, identificar foreign keys quebradas, e otimizar estrutura de tabelas.

Backup database before tests, migrations, or other database operations

Implement backup and restore strategies for disaster recovery. Use when creating backup plans, testing restore procedures, or setting up automated backups.

Prisma ORM best practices for Shopify apps including multi-tenant data isolation, query optimization, transaction patterns, and migration strategies. Auto-invoked when working with database operations.