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Found 17 Skills
Production Python coding standards with automatic version detection (3.10-3.13). Use when writing, reviewing, or refactoring Python to ensure adherence to modern type syntax, LBYL exception handling, pathlib operations, ABC-based interfaces, and production-tested patterns. Not Dagster-specific - applies to any Python project.
Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.
Expert guidance for Dagster data orchestration including assets, resources, schedules, sensors, partitions, testing, and ETL patterns. Use when building or extending Dagster projects, writing assets, configuring automation, or integrating with dbt/dlt/Sling.
Skill that helps users discover and understand Dagster integration libraries. Used when users have requests related to integrating with other tools / technologies, or when have users have questions related to specific integration libraries (dagster-*).
Python coding standards with automatic version detection. Use when writing, reviewing, or refactoring Python to ensure adherence to LBYL exception handling patterns, modern type syntax (list[str], str | None), pathlib operations, ABC-based interfaces, absolute imports, and explicit error boundaries at CLI level. Also provides production-tested code smell patterns from Dagster Labs for API design, parameter complexity, and code organization. Essential for maintaining erk's dignified Python standards.
Structure and organize Dagster code locations using dg. Use this skill when creating or migrating code locations, placing assets or sensors in the correct location, scaffolding new dg projects, or setting up the dg_projects/ workspace layout.
Builds data infrastructure — ETL/ELT pipelines, data warehousing, stream processing, data quality, orchestration (Airflow/Dagster), and analytics engineering (dbt). Use when the user asks to build data pipelines, set up ETL/ELT workflows, design a data warehouse, configure stream processing, or implement analytics engineering with dbt, Airflow, or Dagster.
This skill should be used when running CI checks iteratively and fixing failures. Use when executing make targets (fast-ci, all-ci, ci), iterating on lint/format/type/test errors, or needing the devrun agent pattern for pytest/ty/ruff/prettier/make/gt commands.
Simplifies and refines Python code for clarity, consistency, and maintainability while preserving all functionality. Applies dignified-python standards. Focuses on recently modified code unless instructed otherwise.
Guide for erk exec subcommands. Use when running erk exec commands to understand syntax, find the right command for a task, or learn common workflows. Always check syntax with -h or load this skill before running erk exec commands.
This skill should be used when inspecting, analyzing, or querying Claude Code session logs. Use when users ask about session history, want to find sessions, analyze context usage, extract tool call patterns, debug agent execution, or understand what happened in previous sessions. Essential for understanding Claude Code's ~/.claude/projects/ structure, JSONL session format, and the erk extraction pipeline.
Fetches and classifies PR review feedback with context isolation. Returns structured JSON with thread IDs for deterministic resolution. Use when analyzing PR comments before addressing them.