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Found 23 Skills
Teach AI agents how to query data warehouses accurately using ktx - an executable context layer with skills, memory, and a semantic layer
Run queries against a Cube deployment's semantic layer and interpret the results, from the terminal. Use whenever someone wants numbers out of Cube — pull a metric, check a total, compare periods, verify that a measure returns what it should, sanity-check a model change. Triggers on "how many", "what's the total", "show me revenue by month", "did that measure work", "what's our MoM growth", "pull the numbers for", and any request to verify a model change against real data. To find out what is queryable first use cube-explore-model; to change the model use cube-build-model; to save a result as a report or dashboard use cube-build-content.
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
Use this skill for Salesforce Data 360 architecture, implementation, troubleshooting, Connect API payloads, metadata discovery, query SQL, calculated insights, semantic layer, search, segmentation, activation, data actions, governance, data spaces, and agentic validation workflows.
Build and change Metabase data apps, the React apps Metabase serves at `/apps/<slug>` from `data_apps/<slug>/` in the Git repository connected through remote sync. Use when the user asks to create, scaffold, set up, or remove a data app; to show Metabase tables, metrics, or saved questions in one, or work with its generated `metabase.data.ts` schema; to add pages or routing; to add a form or button that writes data through an action; or to migrate an app Metabase marks Outdated, or whose `npm run typecheck` or `npm run build` fails after an SDK upgrade.
Context layer for data agents - builds semantic layer, wiki, and warehouse metadata to enable accurate AI-powered analytics queries
Context layer for AI data agents - teach Claude Code, Codex, and AI agents to query data warehouses accurately with semantic layer, wiki knowledge, and MCP tools
Context layer for AI data agents - query warehouses accurately with semantic layers, metrics, and wiki knowledge through MCP
Run queries against Omni Analytics' semantic layer using the Omni CLI, interpret results, and chain queries for multi-step analysis. Use this skill whenever someone wants to query data through Omni, run a report, get metrics, pull numbers, analyze data, ask "how many", "what's the trend", "show me the data", retrieve dashboard query results, or perform any data retrieval through Omni's query engine. Also use when someone wants to programmatically extract data from an existing Omni dashboard or workbook.
Expert data engineering with Metabase at the center of any company's stack: explore and profile raw warehouse tables, build clean tables as Metabase transforms pinned by transform tests, build the semantic layer (models, metrics, measures, segments, metadata), design dashboards, answer questions with checked numbers, reconcile against a reference, change delivered definitions safely. Use for data work, not one `mb` command (`metabase-cli`): "make sense of my data", "model this raw schema", "set up analytics for X", "build a data model", "define MRR / active customers officially", "build a semantic layer", "go from raw tables to a dashboard", "does this number match finance", "our numbers look wrong", "two cards disagree", "test this transform", "why does this transform get case X wrong", "make Metabot answer questions about X", "explain this table", "change this definition", "build a data app", "add a page / a form to my data app", "donor retention", "event attendance", "quarterly board report", "be my data engineer / analyst".
Install and configure ktx, the self-improving context layer that teaches AI agents to query data warehouses accurately with approved metrics, semantic layer, and business knowledge.