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Found 322 Skills
Pipeline state management for Goldsky Turbo — pause, resume, restart, and delete commands with their rules and safety behavior. Use this skill when the user asks: will deleting my pipeline lose the data already in my postgres/clickhouse table, how do I pause a pipeline while doing database maintenance, how do I restart from block zero to reprocess all historical data, can I update a running streaming pipeline in place or do I have to delete and redeploy, will resuming a paused pipeline pick up from where it left off (checkpoint), how do I re-run a completed job pipeline from the beginning, can I pause or restart a job-mode pipeline. Also covers what happens to checkpoint state on delete, and job auto-deletion 1 hour after termination. For actively diagnosing why a pipeline is broken or erroring, use /turbo-doctor instead.
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.
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.
Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.
Query the ExoPriors Scry API -- SQL-over-HTTPS search across 229M+ entities spanning forums, papers, social media, government records, and prediction markets. Includes cross-platform author identity resolution (actors, people, aliases), OpenAlex academic graph navigation (authors, citations, institutions, concepts), shareable artifacts, and structured agent judgements. Use when the task involves: Scry API, ExoPriors, /v1/scry/query, scry.search, scry.entities, materialized views, corpus search, epistemic infrastructure, 229M entities, lexical search, BM25, structured agent judgements, scry shares, cross-corpus analysis, who is this person, cross-platform identity, OpenAlex, citation graph, coauthor graph, academic papers, author lookup. NOT for: semantic/vector search composition or embedding algebra (use scry-vectors), LLM-based reranking (use scry-rerank), or the user's own local Postgres / non-ExoPriors data sources.
Microsoft SQL Server specific features. Covers data types, indexes, partitioning, and SQL Server-specific syntax. Use for SQL Server database work. USE WHEN: user mentions "sql server", "mssql", "IDENTITY", "GETDATE()", "temporal tables", "columnstore", "SQL Server specifics", "Azure SQL" DO NOT USE FOR: T-SQL programming - use `tsql` instead, PostgreSQL - use `postgresql` instead, Oracle - use `oracle` instead
Generates importable n8n workflow JSON files that sync data between Personize and 400+ apps. Produces ready-to-import workflows for batch sync, webhook ingestion, per-record AI enrichment, and data export — no code required. Use this skill whenever the user wants no-code integrations, visual workflows, n8n automation, or to connect Personize to HubSpot, Salesforce, Google Sheets, Slack, Postgres, or any app without writing code. Also trigger when they mention 'workflow automation', 'scheduled sync without code', 'visual pipeline', or 'connect Personize to [app]' and don't want to write TypeScript.
testcontainers-python specialist. Covers all container modules (PostgreSQL, MySQL, MongoDB, Redis, Kafka, RabbitMQ, MinIO, Elasticsearch, LocalStack), GenericContainer, wait strategies, Docker Compose, networks, pytest fixtures, and CI/CD integration. USE WHEN: user mentions "testcontainers", "docker in tests", "real database in tests", "test with real postgres/redis/kafka", asks about container fixtures or Docker-based testing. DO NOT USE FOR: Spring Boot testcontainers (Java) - use `spring-boot-integration`; Mocking HTTP - use `fastapi-testing`; Pure pytest patterns - use `pytest`
Expert knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Cosmos DB NoSQL/Mongo/Cassandra/PostgreSQL APIs, change feed, vector search, global distribution, or HTAP workloads, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Blob Storage (use azure-blob-storage).
Expert knowledge for Azure Database for MySQL development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when deploying MySQL Flexible Server, tuning performance, configuring HA/networking, securing access, or integrating apps, and other Azure Database for MySQL related development tasks. Not for Azure Database for MariaDB (use azure-database-mariadb), Azure Database for PostgreSQL (use azure-database-postgresql), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance).
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.