rory-data
36 verified skills
docker-best-practices
Docker and container image best practices including multi-stage builds, security hardening, layer optimization, and Alpine/slim variants. Use when writing or reviewing Dockerfiles, container configurations, or docker-compose files.
development
markdown-conventions
Documentation and content creation standards for Markdown files. Use when writing, reviewing, or formatting Markdown documentation, README files, or technical content.
development
airflow
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
tools
airflow-hitl
Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, HITLTrigger. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).
data-ai
blueprint
Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.
development
instructions-builder
Create, organise, and refine custom instruction files for GitHub Copilot using a modular, rules-based approach. Invoke this skill whenever the user wants to write Copilot instructions, add rules to an existing instructions directory, convert team conventions into instruction files, scope instructions to specific file types or directories, migrate rules from another AI tool into Copilot format, or asks how to structure a .github/copilot-instructions.md or instructions/ directory. Also invoke when someone says "create instructions for X", "write a rule for Y", "add a Copilot rule about Z", or "how should I organise my Copilot instructions".
tools
managing-astro-local-env
Manage local Airflow environment with Astro CLI (Docker and standalone modes). Use when the user wants to start, stop, or restart Airflow, view logs, query the Airflow API, troubleshoot, or fix environment issues. For project setup, see setting-up-astro-project.
tools
principal-data-engineer
Expert-level guidance on data platform architecture, pipeline design patterns, and engineering rigor. Use when designing data platforms, reviewing Airflow DAGs, working with Polars/DuckDB/dbt, establishing data quality contracts, implementing composable data stacks, or architecting lakehouse solutions with Iceberg.
development
python-conventions
Python coding standards including PEP 8, type hints, modern syntax (3.11+), ruff formatting, uv package management, and best practices. Use when writing, reviewing, or refactoring Python code, setting up Python projects, or managing dependencies.
development
python-testing-patterns
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing unit or integration tests, setting up test infrastructure, implementing TDD, testing async code, mocking external dependencies, writing property-based tests, testing database operations, or debugging failing tests. Invoke this skill any time the user wants to write or improve Python tests.
development
troubleshooting-astro-deployments
Troubleshoot Astronomer production deployments with Astro CLI. Use when investigating deployment issues, viewing production logs, analyzing failures, or managing deployment environment variables.
tools
airflow-hitl
Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, HITLTrigger. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).
data-ai
airflow-plugins
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
tools
airflow
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
tools
engineering-workflow
Structured engineering workflow for complex projects: the 6-phase loop (Analyse → Design → Implement → Validate → Reflect → Handoff), confidence-based execution strategy, EARS requirements notation, and decision records. Use this skill whenever the user wants to plan a feature, architect a system, write requirements, or needs structured guidance for any non-trivial engineering task. Also invoke when asked to produce PRDs, user stories, specifications, technical designs, implementation task breakdowns, or decision records — templates for all of these are in references/templates.md.
testing
code-smell-detector
Detects and fixes common code smells during review or refactoring. Invoke whenever reviewing code for quality issues, before merging a PR, when refactoring legacy code, or when the user asks about code quality, anti-patterns, or technical debt. Detects: over-abstraction, complex inheritance, large functions, tight coupling, hidden dependencies, magic numbers, boolean traps, swallowed exceptions, global state, and duplicate code. Provides specific fixes with before/after examples. Also invoke when someone says "review this code", "is this clean?", "can I improve this?", "this feels messy", or "find problems in my code".
development
annotating-task-lineage
Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.
testing
deploying-airflow
Deploy Airflow DAGs and projects. Use when the user wants to deploy code, push DAGs, set up CI/CD, deploy to production, or asks about deployment strategies for Airflow.
development
tracing-downstream-lineage
Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.
testing
warehouse-init
Initialize warehouse schema discovery. Generates .astro/warehouse.md with all table metadata for instant lookups. Run once per project, refresh when schema changes. Use when user says "/astronomer-data:warehouse-init" or asks to set up data discovery.
data-ai
airflow-plugins
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
tools
checking-freshness
Quick data freshness check. Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it.
testing
cosmos-dbt-fusion
Use when running a dbt Fusion project with Astronomer Cosmos. Covers Cosmos 1.11+ configuration for Fusion on Snowflake/Databricks with ExecutionMode.LOCAL. Before implementing, verify dbt engine is Fusion (not Core), warehouse is supported, and local execution is acceptable. Does not cover dbt Core.
testing
authoring-dags
Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.
development
cosmos-dbt-core
Use when turning a dbt Core project into an Airflow DAG/TaskGroup using Astronomer Cosmos. Does not cover dbt Fusion. Before implementing, verify dbt engine, warehouse, Airflow version, execution environment, DAG vs TaskGroup, and manifest availability.
testing
creating-openlineage-extractors
Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.
data-ai
debugging-dags
Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline", "full root cause analysis", "why is this failing and how to prevent it". For simple debugging ("why did dag fail", "show logs"), the airflow entrypoint skill handles it directly. This skill provides structured investigation and prevention recommendations.
development
managing-astro-deployments
Manage Astronomer production deployments with Astro CLI. Use when the user wants to authenticate, switch workspaces, create/update/delete deployments, or deploy code to production.
tools
migrating-airflow-2-to-3
Guide for migrating Apache Airflow 2.x projects to Airflow 3.x. Use when the user mentions Airflow 3 migration, upgrade, compatibility issues, breaking changes, or wants to modernize their Airflow codebase. If you detect Airflow 2.x code that needs migration, prompt the user and ask if they want you to help upgrade. Always load this skill as the first step for any migration-related request.
development
profiling-tables
Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.
testing
setting-up-astro-project
Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.
testing
golang-conventions
Go coding standards for Go 1.21+, including idioms, error handling, testing patterns, concurrency, and golangci-lint configuration. Use when writing, reviewing, or refactoring Go code, working with goroutines, or setting up Go projects.
development
testing-dags
Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For simple test requests ("test dag", "run dag"), the airflow entrypoint skill handles it directly. This skill is for iterative test-debug-fix cycles.
development
tracing-upstream-lineage
Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.
testing
analyzing-data
Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.
data-ai
skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
testing