Loading...
Loading...
Found 3,414 Skills
MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.
Track reviews, ratings, sentiment, and brand mentions across Google Maps, Booking.com, TripAdvisor, Facebook, Instagram, YouTube, and TikTok. Use when user asks to monitor brand reputation, analyze reviews, track mentions, or gather customer feedback.
Use when creating cloud sandboxes (microVMs) to run code, start dev servers, and generate live preview URLs. Also covers deploying AI agents, MCP servers, batch jobs, and Agent Drives (shared filesystems) on Blaxel's serverless infrastructure. Reach for this skill when you need isolated compute environments, real-time app previews, shared file storage across sandboxes, or to deploy agentic workloads.
Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
Verify whether a live web app matches its design specification using deterministic CSS checks against the DOM, AND verify user flows by executing Gherkin scenarios via Nova Act. Translates design claims into rules and runs them via verify_* MCP tools; executes flows via act() and act_get(). Produces a combined visual + flow report with annotated screenshots and per-flow detail. Use when checking visual style, component rules, accessibility, project conventions, platform patterns, or end-to-end user journeys against a live site.
Use when creating or modifying dbt Semantic Layer components including semantic models, metrics, and dimensions leveraging MetricFlow.
Use when fetching dbt documentation, looking up dbt features, or answering questions about dbt Cloud, dbt Core, or the dbt Semantic Layer
Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt
AWS S3 object storage for bucket management, object operations, and access control. Use when creating buckets, uploading files, configuring lifecycle policies, setting up static websites, managing permissions, or implementing cross-region replication.
Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.
Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI applications.