القائمة
Standards, workflows, and helper scripts for generating unit tests and mock data to test the Multi-Explore Experience Pattern (MEEP) in multiExploreUtils.ts.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
This skill enables agents to assist users in programmatically creating, updating, and managing Looker themes using the Looker API. Use this when you need to automate visual styling, implement brand-specific themes, or manage instance-wide default themes.
Detailed guidelines and mandatory workflows for localizing LookML models, views, explores, and dashboards, including string catalog creation and deployment via lkr-dev-cli.
Parent orchestrator skill for coordinating full-stack internationalization and localization across LookML models/dashboards and the React frontend application.
Comprehensive standards, UI design principles, tabbed report architecture, and automation patterns for creating and managing modern LookML dashboards using local LookML files and Looker MCP/CLI tools.
Analyzes a specific LookML dashboard (e.g., brand_lookup.dashboard.lookml), extracts all query elements and merge results, verifies them via run_inline_query(result="sql"), and self-heals missing LookML fields by iteratively adding best-guess calculations to local LookML views.
Standards, workflow rules, and helper patterns for managing LookML files in the Looker project using the `lkr-dev-cli` CLI tool, including full project and single-file push/pull synchronization and production deployment.
| name | meep-testing-generation |
| description | Standards, workflows, and helper scripts for generating unit tests and mock data to test the Multi-Explore Experience Pattern (MEEP) in multiExploreUtils.ts. |
This skill tracks the iterative generation and maintenance of test cases and mock data for the Multi-Explore Experience Pattern (MEEP).
As we iterate on MEEP functionality (such as field layering, group relabeling, timeline linking, and exclusions), we follow a recurring workflow to update our mock Explores and write automated Jest unit tests.
ILookmlModelExplore objects.Whenever changes are made to meep_test.model.lkml or underlying LookML views, we must regenerate the mock Explores in mockExplores.ts.
We maintain MCP executable Python scripts in the ./scripts folder. Each script contains exactly one code block designed to be run via the @mcp:lkr_dev_cli_codemode:run_python_code tool.
json_bi output format to inspect row and pivot metadata.To refresh the mock Explores:
call_mcp_tool using the lkr_dev_cli_codemode server and run_python_code tool.To test query execution and check json_bi formats:
call_mcp_tool with the lkr_dev_cli_codemode server and run_python_code tool.We will build unit tests in multiExploreUtils.test.ts and meepQueryBuilder.test.ts. Specifically, for client-side queries merging (full outer joining on timelines/dimensions/pivots), we mock standard Looker json_bi response objects and assert that mergeMeepResults correctly joins them on overlapping keys.