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Found 61 Skills
Diagnose and fix broken Goldsky Turbo pipelines interactively. Use whenever the user has a specific pipeline that is misbehaving — error state, stuck in 'starting', connection refused, slow backfill, not getting data in postgres/clickhouse, duplicate rows, missing fields, named pipeline failing ('my base-usdc-transfers keeps failing'), or any symptom where something is wrong with a deployed pipeline. Runs goldsky turbo logs and status commands, identifies root cause, and offers to run fixes. For looking up CLI syntax or error message definitions WITHOUT an active problem, use /turbo-monitor-debug instead.
Design and architect Goldsky Turbo pipelines. Use this skill for 'should I use X or Y' decisions: kafka source vs dataset source, streaming vs job mode, which resource size (xs/s/m/l/xl/xxl) for my workload, postgres vs clickhouse vs kafka sink, fan-in vs fan-out data flow, one pipeline vs many, dynamic table vs SQL join, how to handle multi-chain deployments. Also use when the user asks 'what's the best way to...' for a pipeline design problem, or is unsure how to structure their pipeline before building it.
Perses datasource lifecycle management: create, update, delete datasources at global, project, or dashboard scope. Supports Prometheus, Tempo, Loki, Pyroscope, ClickHouse, and VictoriaLogs. Uses MCP tools when available, percli CLI as fallback. Use for "perses datasource", "add datasource", "configure prometheus perses", "perses data source". Do NOT use for dashboard creation (use perses-dashboard-create).
Build and deploy new Goldsky Turbo pipelines from scratch. Triggers on: 'build a pipeline', 'index X on Y chain', 'set up a pipeline', 'track transfers to postgres', or any request describing data to move from a chain/contract to a destination (postgres, clickhouse, kafka, s3, webhook). Covers the full workflow: requirements → dataset selection → YAML generation → validation → deploy. Not for debugging (use /turbo-doctor) or syntax lookups (use /turbo-pipelines).
Goldsky Turbo pipeline YAML reference — the authoritative source for field names, required vs optional fields, and valid values. Use whenever the user asks about specific YAML fields: what does `start_at: earliest` vs `latest` do, what fields does a postgres/clickhouse/kafka sink require, what is the `from:` field in a sink, how does `checkpoint` work, what's the syntax for `batch_size` or `primary_key`. Also use for validation errors like 'unknown field' or 'missing required field'. For interactive pipeline building end-to-end, use /turbo-builder instead.
Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).
Database operations including querying, schema exploration, and data analysis. Activates for tasks involving PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis, Elasticsearch, or ClickHouse databases.
Data lake and lakehouse platform patterns: ingestion/CDC, transformations, open table formats (Iceberg/Delta/Hudi), query and serving engines (Trino/ClickHouse/DuckDB), orchestration, governance/lineage, cost and operations. Self-hosted and cloud options.
Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.
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).
Open-source lightweight cross-platform database management tool built with Tauri, Vue 3, and Rust supporting MySQL, PostgreSQL, SQLite, Redis, MongoDB, DuckDB, ClickHouse, and SQL Server.
HogQL queries for PostHog analytics