Data & Analytics

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SQL Server database design best practices, naming conventions, indexing strategies, and performance optimization for .NET applications using Microsoft.Data.SqlClient and EF Core.

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Use when you need programmatic JDBC in Micronaut — pooled DataSource, parameterized SQL, io.micronaut.transaction.annotation.Transactional, batching, and domain exception translation. This should trigger for requests such as Review JDBC or SQL data access in a Micronaut project; Improve transactions and parameter binding for Micronaut JDBC; Translate SQLException to domain exceptions or stream large result sets; Fix self-invocation bypassing @Transactional in Micronaut. Part of cursor-rules-java project

majiayu000 Bundle

Use when you need data access with Micronaut Data — @MappedEntity, CrudRepository/PageableRepository, @Query with parameters, @Transactional services, projections, @Version, and @MicronautTest with TestPropertyProvider and Testcontainers. For raw java.sql access without generated repositories, use @511-frameworks-micronaut-jdbc. This should trigger for requests such as Review or implement Micronaut Data repositories and entities; Add transactions, pagination, or projections in Micronaut persistence layer. Part of cursor-rules-java project

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Master metrics definition, KPI tracking, dashboarding, A/B testing, and data-driven decision making. Use data to guide product decisions.

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ClickHouse analytics and Grafana dashboard configuration for Vigil Guard v2.0.0 monitoring. Use when querying logs, analyzing 3-branch detection metrics, creating dashboards, investigating events, working with n8n_logs database, managing retention policies, or monitoring branch performance (branch_a_score, branch_b_score, branch_c_score).

majiayu000 Bundle

Apply SQL optimization patterns including index usage, query rewriting, JOIN optimization, and window functions. Use when improving query performance or analyzing slow queries. This skill provides comprehensive SQL optimization patterns and best practices: - N+1 query elimination - Index optimization strategies - JOIN vs subquery performance - Window functions for complex aggregations - Query execution plan analysis Triggers: "optimize SQL", "slow query", "improve performance", "SQL最適化", "クエリ改善", "パフォーマンス向上"

majiayu000 Bundle

Diagnose and resolve Microsoft Fabric OneLake performance issues including slow queries, cold cache latency, small file problems, Delta table fragmentation, V-Order optimization, Spark throttling, capacity SKU sizing, and cross-region data access. Use when remediate OneLake read/write performance, lakehouse query slowness, Direct Lake fallback, table maintenance failures, Spark concurrency limits, warehouse cold starts, or optimizing Delta parquet file layouts. Supports PowerShell, T-SQL, and Spark SQL diagnostic workflows.

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Diagnose and resolve Apache Spark performance issues in Microsoft Fabric notebooks and Spark Job Definitions. Use when PySpark jobs are slow, notebooks take too long, Spark stages are skewed, shuffles are excessive, out-of-memory errors occur, Delta Lake writes are slow, or Fabric capacity is throttled. Covers data skew, shuffle optimization, broadcast joins, partition tuning, VOrder, Optimized Write, resource profiles, autotune, native execution engine, small file compaction, and Spark UI interpretation. Keywords include slow notebook, OOM, spill, shuffle, skew, broadcast, repartition, coalesce, OPTIMIZE, VACUUM, Z-ORDER, checkpoint, cache, persist, executor memory, driver memory, spark.sql.shuffle.partitions, autoBroadcastJoinThreshold, maxPartitionBytes, Fabric capacity throttling, CU utilization.

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Transform raw data into actionable insights by shifting from tracking "what happened" to "why it happened." Use this skill when designing instrumentation specs for new features, auditing analytics dashboards that provide no clear direction, or diagnosing sudden drops in conversion.

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Data architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patterns

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Generates comprehensive role-based permission matrices in markdown or SQL format for pages, components, and data access patterns. This skill should be used when designing authorization systems, documenting permissions, creating RBAC tables, or planning access control. Use for RBAC, role permissions, access control, authorization matrix, permission mapping, or security policies.

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Modular design pattern for external service integrations (CAPTCHA, payment, SMS, file storage, analytics). Use when implementing third-party services, designing service abstractions, or reviewing service architecture. Triggers on /src/services/* files, integration questions, and provider implementations.

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Extracts specific training metrics from experiment logs at regular intervals and saves them to a CSV file for analysis. Processes time-series training data (like entropy_loss, clip_ratio, response_length_mean) at specified step intervals, formats data into a structured table, and creates a CSV file.

majiayu000 Bundle

Enhanced WasteWise Complete Analysis with comprehensive validation framework. Validates all requirements before output including contract tab generation, clause extraction, optimization criteria, formula accuracy, and data completeness. Produces detailed validation report showing which checks passed/failed. Use when rigorous quality control is needed for waste management analysis.

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Work with Reference Tables (static CSV lookup data) using OPAL to enrich datasets with descriptive information. Use when you need to map IDs to human-readable names, add static metadata from CSV uploads, or perform lookups without temporal considerations. Covers both explicit and implicit lookup patterns, column name matching, and when to choose Reference Tables vs Resources vs Correlation Tags.

majiayu000 Bundle

Convert per-case Abaqus FEA outputs into ML-ready (X, Y) wide-table CSVs. Pivots irregular FEA mesh node displacements onto a regular N×N grid via direct binning (structured mesh) or bilinear resampling, picks the final frame as the deformation target, and aggregates across many cases into X_amplitude.csv (design vectors) + Y_grid_uz.csv (flattened grid displacement). Use when the user has a folder of completed FEA cases and wants to train a Ridge / MLP / Gaussian Process surrogate on the (input → displacement field) mapping.

majiayu000 Bundle

Use when you need data access with Quarkus Hibernate ORM Panache — including PanacheEntity / PanacheEntityBase, PanacheRepository, named queries, JPQL, native SQL, DTO projections (project(Class)), pagination (Page.of()), N+1 avoidance (JOIN FETCH), optimistic locking (@Version / OptimisticLockException), @NamedQuery for validated reusable queries, transactions, @TestTransaction for test isolation, and immutable-friendly patterns. This is the Quarkus analogue to Spring Data for relational persistence. This should trigger for requests such as Review Panache entities or repositories in Quarkus; Improve Hibernate ORM data access with Panache; Add DTO projections, JOIN FETCH, pagination, or optimistic locking to Panache queries; Fix N+1 query problems or add @Version concurrency control in Quarkus Panache. Part of cursor-rules-java project

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You are a coding standards expert specializing in pandas data manipulation rules.

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Populates an Excel template with financial data (stock prices, market trends, ownership) for a specified company. Reads requirement files, fetches data from Yahoo Finance, analyzes template structure, formats data, writes to Excel, and renames the output file.

majiayu000 Bundle

Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

majiayu000 Bundle

Use when you need to write or review programmatic JDBC with Spring — including JdbcClient (Spring Framework 7+) as the default API, JdbcTemplate only where batch/streaming APIs require JdbcOperations, NamedParameterJdbcTemplate for legacy named-param code, parameterized SQL, RowMapper mapping to records, batch operations, transactions, safe handling of generated keys, DataAccessException handling, read-only transactions, streaming large result sets, and @JdbcTest slice testing. This should trigger for requests such as Review Java code for Spring JDBC (JdbcTemplate, JdbcClient, NamedParameterJdbcTemplate); Apply best practices for Spring JDBC data access in Java code; Detect and fix SQL injection risks in JDBC code; Improve transaction boundaries or exception handling for JDBC operations. Part of cursor-rules-java project

majiayu000 Bundle

Fastify Node.js expert for .ts API files, REST endpoints, routes, middleware, handlers, PostgreSQL, SQL queries, pg.Pool, Zod schemas, validation, authentication, authorization, async/await, database connections, camelCase, type safety, error handling

majiayu000 Bundle

description: "Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Codex needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas"

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Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.

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Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.

majiayu000 Bundle

Aplica padrões de excelência em testes com NestJS e Jest seguindo Clean Architecture, DDD e boas práticas de testabilidade. Use quando criar ou refatorar testes unitários, de integração ou E2E em projetos NestJS.

majiayu000 Bundle

This skill should be used when inserting files (images, PDFs, Excel files, documents) into the CRM database and linking them to companies, people, events, or sales opportunities. The skill handles base64 encoding, hash calculation, MIME type detection, and SQL insertion automatically through a Python script.

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Expert system for generating, validating, and optimizing ClickHouse SQL. Use this when the user needs data, queries, or analysis.

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Analyzes Fulfil order data to determine profitable free shipping thresholds using order value distribution, hero product analysis, and shipping economics. Use when merchants ask about free shipping strategy, threshold optimization, shipping offers, AOV analysis, or need data-driven shipping decisions. Requires access to Fulfil data warehouse (sales_orders, shipments tables).

majiayu000 Bundle

Fix for Traefik rewrite-body plugin (packruler/rewrite-body) failing with "flate: corrupt input before offset 5" errors when backends send gzip-compressed responses. Use when: (1) rewrite-body plugin logs show "Error loading content: flate: corrupt input before offset 5", (2) rybbit analytics script injection breaks WebSocket connections or authentication flows, (3) HA Companion app stuck in external_auth loop with repeated GET /?external_auth=1 requests, (4) mobile apps fail to connect while browser works fine, (5) HTTP 499 errors on webhook POST requests. Root cause: the plugin attempts to decompress all responses before checking content-type, and fails on certain gzip encodings, corrupting the response body.

majiayu000 Bundle

静的解析によるセキュリティ脆弱性検出を整理し、SAST運用と検出ルールの設計を支援するスキル。 SQLインジェクション、XSS、コマンドインジェクションなどの検出と改善方針を扱う。 Anchors: • Web Application Security (Andrew Hoffman) / 適用: 脅威分析と検出観点 / 目的: 脆弱性検出の精度向上 • OWASP ASVS / 適用: 検出基準の整理 / 目的: セキュリティ要件の明文化 • Secure by Design (OWASP) / 適用: 改善方針 / 目的: 安全な設計判断 Trigger: Use when running SAST, defining detection rules, auditing injection vulnerabilities, or documenting static analysis findings. static analysis, SAST, SQL injection, XSS, command injection, security review

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Convert an attached Word document, presentation, spreadsheet, OpenDocument file, RTF, EPUB, CSV, or text-based PDF into local Markdown. Use when a message supplies a local attachment path that must be read without uploading it to an external parser.

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Extract validated financial data from Datarails Finance OS to Excel. Creates workbooks with P&L, Balance Sheet, KPIs (including ARR), and validation checks.

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Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.

majiayu000 Bundle

Presentation-focused Natural Language to SQL app with PPT-style visualizations.

majiayu000 Bundle

Neon PostgreSQL patterns for Pitchey. Raw SQL only, no ORM. Uses Hyperdrive for connection pooling. Activates for database queries, migrations, or schema work.