Loading...
Loading...
Found 36 Skills
Create, modify, and organise Grafana dashboards including panels, variables, transformations, and alerting. Use when the user asks to create a Grafana dashboard, add a panel, configure a time series or stat panel, add template variables, set up dashboard linking, use transformations, configure thresholds, build a dashboard for a service, or export dashboard JSON. Triggers on phrases like "create dashboard", "add panel", "time series panel", "Grafana dashboard JSON", "template variables", "dashboard variable", "panel transformation", "threshold", "stat panel", "table panel", "Grafana annotations", or "dashboard folder".
Integrate external APIs and services with error handling, retry logic, and data transformation. Use when connecting to payment processors, messaging services, analytics platforms, or other third-party providers.
Create and manage Infrahub transforms. Use when building data transformations, config generation, or any workflow that converts Infrahub data into a different format (JSON, text, CSV, device configs) using Python or Jinja2 templates.
Use when analyzing FileMaker DDR to extract calculations, custom functions, and business logic for PostgreSQL import processes or maintenance scripts - focuses on understanding and adapting FileMaker logic rather than direct schema migration
Consult this skill when designing data pipelines or transformation workflows. Use when data flows through fixed sequence of transformations, stages can be independently developed and tested, parallel processing of stages is beneficial. Do not use when selecting from multiple paradigms - use architecture-paradigms first. DO NOT use when: data flow is not sequential or predictable. DO NOT use when: complex branching/merging logic dominates.
Work with JSONB data - queries, indexing, transformations
Guide for creating GreptimeDB Pipeline, by which user can add a process layer to GreptimeDB between ingestion and storage, to transform data.
Use this for SQL queries, database schema design, ETL pipelines, data transformations (pandas/Spark), and data validation.
Use when CSV, TSV, or Excel (.xlsx) is the primary input/output: inspect, transform, validate, convert, recalc formulas, or create/fix spreadsheets. Do not trigger when tabular data is incidental.
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.
Guide for creating Nushell plugins in Rust using nu_plugin and nu_protocol crates. Use when users want to build custom Nushell commands, extend Nushell with new functionality, create data transformations, or integrate external tools/APIs into Nushell. Covers project setup, command implementation, streaming data, custom values, and testing.
dbt (data build tool) patterns for model organization, incremental strategies, and testing.