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Found 739 Skills
Generate Go GORM models following Pingo modular architecture conventions. Use when creating or updating persistence models in internal/modules/<module>/model/, including table mapping, nullable SQL types, timestamps, and relation fields for identity and monitor modules.
Local-first architecture decision framework for web applications. Covers when to go local-first vs server-based vs hybrid, sync engine selection (ElectricSQL, Zero, PowerSync, Replicache, LiveStore, Triplit), client-side storage options (IndexedDB, OPFS, SQLite WASM, PGlite), and conflict resolution strategies (LWW, CRDTs, server-wins, field-level merge). Use when deciding whether to adopt local-first architecture, choosing a sync engine, selecting client storage, or designing conflict resolution strategies.
Expert knowledge for Azure Backup development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when backing up Azure VMs, AKS, SQL/PostgreSQL/MySQL, SAP HANA, files/disks/blobs, or automating via CLI/PowerShell/REST, and other Azure Backup related development tasks. Not for Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), Azure Blob Storage (use azure-blob-storage), Azure Files (use azure-files).
Programmatic JDBC in Quarkus with Agroal DataSource, parameterized SQL, transactions, batching, and Dev Services. Part of the skills-for-java project
Production-grade Next.js chatbot builder. Covers tool calling with human-in-the-loop (HITL) approval, PostgreSQL session persistence, GDPR consent gating, SQL-first search, per-tool UI rendering, message feedback, and follow-up suggestions. Use when building chat apps, conversational AI interfaces, customer support bots, or any chatbot needing database-backed sessions, tool approval workflows, consent gating, or custom tool output components. Reference implementation: fair-helpdesk project.
Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.
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.
Read any data file (CSV, JSON, Parquet, Avro, Excel, spatial, SQLite) or remote URL (S3, HTTPS). Use when user references a data file, asks "what's in this file", or wants to preview/profile a dataset. Not for source code.
Use when managing Cisco CUCM via the cisco-axl CLI — phones, lines, route patterns, partitions, calling search spaces, SIP profiles, and any AXL operation. Covers CRUD operations, SQL queries, operation discovery, bulk provisioning from CSV, and raw AXL execute commands.
Run Commerce Intelligence Platform (CIP/CCAC) analytics reports, metadata discovery, and SQL queries with the b2c cli. Always reference when using the CLI to run analytics reports, query Commerce Intelligence data, discover CIP tables, or export KPI metrics. Also use when users ask about sales, search, or payment analytics.
Use for building and operating Ignis projects with ignis-cli, ignis-sdk, ignis.toml, SQLite, service build/publish/deploy, and example-driven project setup.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.