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Found 1,152 Skills
Operate the Google Cloud gcloud CLI safely and effectively. Authenticates users, reads cloud resource state freely for debugging and exploration, and creates, updates, or deletes resources only after explicit user confirmation. Use when working with gcloud, Google Cloud CLI, GCP resources, cloud debugging, reading logs, managing Compute Engine, Cloud Run, Cloud Functions, GKE, IAM, networking, Cloud Storage, Cloud SQL, Pub/Sub, or when the user mentions any gcloud command, Google Cloud project, or needs to authenticate with GCP.
Core patterns for AI coding agents based on analysis of Claude Code, Codex, Cline, Aider, OpenCode. Triggers when: Building an AI coding agent or assistant, implementing tool-calling loops, managing context windows for LLMs, setting up agent memory or skill systems, or designing multi-provider LLM abstraction. Capabilities: Core agent loop with while(true) and tool execution, context management with pruning and compression and repo maps, tool safety with sandboxing and approval flows and doom loop detection, multi-provider abstraction with unified API for different LLMs, memory systems with project rules and auto-memory and skill loading, session persistence with SQLite vs JSONL patterns.
Manage the full lifecycle of Alibaba Cloud EMR Serverless StarRocks instances — create, scale, configure, maintain and diagnose. Use this Skill when operations engineers, SREs, or architects need to manage StarRocks instances. Typical scenarios include: "create a StarRocks", "check instance status", "scale up CU", "modify configuration", "restart instance", "diagnose issues", etc. Not applicable for: writing SQL/DDL, data import/export, query tuning, materialized view configuration, or managing non-StarRocks products (EMR clusters, Spark, Milvus, ClickHouse, Doris, RDS, ECS).
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.
Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.
Guide the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.
Every Customer.io action a marketer or ops engineer takes — campaigns, broadcasts, segments, deliveries, exports, suppressions, Reverse-ETL — wrapped in named verbs, backed by a local SQLite cache, and served through a bundled MCP server. Trigger phrases: `use customer-io`, `run customer-io`, `trigger a customer.io broadcast`, `send a customer.io transactional message`, `export a customer.io segment`, `check customer.io delivery health`, `audit customer.io suppressions`, `what fraction of segment X opened journey Y in customer.io`.
Deploy and orchestrate 38 MCP servers for offensive security tools (Nmap, Nuclei, Ghidra, SQLMap, etc.) via Docker
End-to-end data engineering pipeline using MinIO, Airbyte, PostgreSQL, DBT, and Airflow with medallion architecture (Bronze/Silver/Gold layers)
Build end-to-end real-time data pipelines with Kafka, PostgreSQL, Airflow, and Streamlit using Medallion Architecture for streaming analytics.
Complete guide to implementing the Syncfusion QueryBuilder component in ASP.NET Core applications. Use this when working with visual query/filter builders, rule-based filtering UI, SQL/JSON/MongoDB query generation, drag-and-drop rule reordering, or import/export of filter conditions using Syncfusion EJ2 TagHelpers.
Use when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql, set-warehouse), or managing charts, dashboards, spaces and access, AI agents, scheduled content, users, groups, custom roles, metrics, and dimensions as code