dlthub-ai-workbench

Build and deploy a minimal custom REST API pipeline to dltHub Platform. Use when the user says "Help me build and deploy a minimal pipeline", "I've finished onboarding, now what?", or "I just ran dlthub-start and want to build my own pipeline".

Deploy and run the pre-shipped Jaffle Shop sample pipeline on dltHub Platform — the final onboarding step after uvx dlthub-start. Use when the user says 'deploy the pipeline', 'deploy the pre-built pipeline', 'deploy the onboarding pipeline', 'do the onboarding task', 'finish the onboarding task', or is ready to complete onboarding. Assumes scaffolding, login, and playground workspace connection are already done.

The guided entry point for dltHub workbench use cases — ingestion from APIs, data exploration, transformations, deployment, data quality. Use when the user names a use case or wants to be oriented before starting: 'I want to ingest from Stripe', 'show me how to go from data to dashboard', 'take me through the full workflow', 'explore the workbench', 'what can I do with dlthub', 'give me a quick start', 'show me a demo', 'walk me through ingestion to visualization', 'I want to try everything end-to-end', 'teach me dltHub'. Do NOT use when the user is asking what's available or where to start in general — use the `dlthub-router` skill (in init) for capability-discovery questions ('what can you do', 'what toolkits are there', 'I'm new to dlthub'). Do NOT use when the user already has a specific task underway (debugging, adding an endpoint, deploying).

Create trigger evaluation setup for a toolkit skill. Use when the user wants to test whether a skill's description triggers correctly, set up eval workspaces, or generate trigger test queries for a skill. Use when user says 'create eval', 'test triggers', 'eval skill', or wants to measure skill triggering accuracy.

Run trigger evaluation for a skill and analyze results. Use when the user wants to run evals, check trigger accuracy, analyze clashes, or improve a skill description based on eval results. Use when user says 'run eval', 'test triggers', 'check skill triggering', or 'analyze eval results'.

The entry point for building anything with dlthub. Use this skill to route the user to the right workflow toolkit and install it on demand. MUST use when the user asks 'what can you do', 'what can I build', 'what are toolkits', 'how do I build a pipeline', 'I want to pull data from a REST API', 'ingest from a SQL database', 'load CSVs from S3', 'make reports / dashboards', 'transform / model my data', 'add data quality checks', 'how do I deploy / schedule a pipeline', 'I'm new to dlthub', 'where do I start', or seems unsure what to do next after setup. Also use whenever the user expresses a data-engineering goal but no matching workflow toolkit is installed yet — this skill installs it on demand. Do NOT use when the toolkit matching the user's intent is already installed — go straight to its entry skill instead; only route/install when the matching toolkit is missing. Do NOT use when a specific task is already in progress (debugging a pipeline, validating data, adding endpoints) and its toolkit is installed.

Query, explore, or view data loaded by a dlt SQL database pipeline. Use when the user asks to query data, explore loaded tables, check row counts, write Python that reads pipeline data, or asks questions like "show me the data", "what users are there", "how much did we spend". Covers dlt dataset API, ibis expressions, and ReadableRelation. NOT for querying the source database — use the pipeline's destination.

Adjust a working dlt pipeline for production — remove dev limits, verify pagination, configure incremental loading, expand date ranges. Use when the user wants to remove .add_limit(), load more data, fix pagination, or set up incremental loading.

Create a dlt REST API pipeline. Use for the rest_api core source, or any generic REST/HTTP API source. Not for sql_database or filesystem sources.

Debug and inspect a dlt pipeline after running it. Use after a pipeline run (success or failure) to inspect traces, load packages, schema, data, and diagnose errors like missing credentials or failed jobs.

Add a new REST API endpoint/resource to an existing dlt pipeline. Use when the user wants to pull additional data from an API that already has a working pipeline.

Query, explore, or view data loaded by a dlt pipeline. Use when the user asks to query data, explore loaded tables, check row counts, write Python that reads pipeline data, or asks questions like "show me the data", "what users are there", "how much did we spend". Covers dlt dataset API, ibis expressions, and ReadableRelation.

This skill should be used when the user asks to "build the notebook", "launch the dashboard", "generate the marimo notebook", or when an analysis_plan.md artifact exists and the user wants to assemble or regenerate the dashboard. Reads chart specs with ibis queries and altair code from analysis_plan.md, assembles a marimo Python file, validates, and launches. Do NOT use for exploring data or planning charts (use explore-data), building pipelines (use rest-api-pipeline toolkit), or deploying (use dlthub-platform toolkit).

This skill should be used when the user asks to "explore my data", "what can I learn from this pipeline", "what's the revenue trend", "show me charts", "visualize my pipeline", "analyze my data", "profile data quality", "what questions can I ask about my data", "map my data to business concepts", or wants to explore, profile, analyze, or chart data from a dlt pipeline. Connects to a pipeline, profiles tables or scans schema, plans charts with ibis + altair code, and writes an analysis_plan.md artifact. Do NOT use for building or fixing pipelines (use rest-api-pipeline toolkit), deploying pipelines (use dlthub-platform toolkit), or assembling the marimo notebook from an analysis plan (use build-notebook).

Use when the user asks to "define checks", "add validation rules", "what checks should I add", "translate requirements into checks", or wants to map schema hints or business rules to dlthub data quality check and metric calls for a specific pipeline or table. Do NOT use to run checks (use run-data-quality) or to set up the pipeline environment (use setup-data-quality).

Use when the user asks to "review data quality results", "what failed", "show me data quality results", "analyze check results", "investigate data quality failures", or wants to understand check and metric outcomes from a pipeline run. Do NOT use to run new checks (use run-data-quality).

Use when the user asks to "run data quality checks", "execute checks", "run my data quality checks", "check the data now", "run validations", or wants to execute already-defined checks against a loaded pipeline. Do NOT use to define new checks (use define-data-quality-checks) or to review existing results (use review-data-quality).

Use when the user asks to "set up data quality", "enable data quality checks", "add data quality to my pipeline", "validate my pipeline data", "I want to check data quality", "check my tables for issues", or wants to start any data quality workflow on a dlt pipeline. Do NOT use for exploring or charting data (use data-exploration toolkit), running existing checks (use run-data-quality), or reviewing results (use review-data-quality).

Debug a failed or misbehaving dltHub Platform deployment. Use when a runtime job fails, produces unexpected results, or the user wants to check job status and logs.

Deploy dlt pipelines to dltHub Platform. Use when the user says "deploy to dltHub", "launch on dltHub", "run on dltHub", "schedule pipeline", or wants to deploy a pipeline or notebook to dltHub.

Prepare production credentials and destinations for dltHub Platform. Use when setting up prod profile secrets, splitting dev/prod credentials, or configuring a production destination like Motherduck.

Verify dlthub workspace is ready for dltHub Platform. Use when user wants to deploy for the first time, or when another skill reports missing prerequisites like .workspace file or dlt[hub] dependency.

Add incremental loading to a dlt filesystem pipeline — filter files by modification date and optionally filter records by a timestamp column. Use after create-filesystem-pipeline produces a working replace-mode pipeline.

Create a dlt filesystem pipeline that reads files (CSV, Parquet, JSONL, or custom) from local disk, S3, GCS, Azure, or SFTP into a destination. Use for the filesystem core source. Not for REST APIs (rest_api) or databases (sql_database).

Safely manage dlthub secrets in *.secrets.toml. Use when the user directly asks to set up, configure, or inspect credentials (API keys, database passwords, tokens). Also use when writing Python code that needs to read secrets via dlt.secrets without exposing values. Do NOT use for pipeline creation, source discovery, or debugging pipeline execution — those skills call setup-secrets when they need credentials configured.

Find a dlt source for a given API or data provider. Use when the user asks about a source, wants to find a connector, or asks to implement a pipeline for a specific data source.

Validate schema and data after a successful dlt pipeline load. Use when the user wants to check if loaded data looks correct, inspect table schemas, fix data types, flatten nested structures, or refine the data shape.

Adjust a working dlt SQL database pipeline for production — remove dev limits, add incremental loading, configure merge keys. Use when the user wants to remove .add_limit(), load the full table, or set up incremental loading on a cursor column.

Create a dlt pipeline from a SQL database source (postgres, mysql, mssql, oracle, sqlite, or any SQLAlchemy-supported database). Use when the user wants to load tables from a relational database to a destination like DuckDB, BigQuery, or Snowflake. Not for REST APIs or file sources.

Debug and inspect a dlt SQL database pipeline after running it. Use after a pipeline run (success or failure) to inspect traces, load packages, schema, and diagnose errors like connection failures, missing credentials, driver issues, or failed jobs.

Find and explore a SQL database source for a dlt pipeline. Use when the user wants to load data from a relational database (postgres, mysql, mssql, oracle, sqlite, or any SQLAlchemy-supported database), mentions a database connection, wants to discover available tables, or asks to build a pipeline from a SQL source.

Validate schema and data after a successful dlt SQL database pipeline load. Use when the user wants to check if loaded data looks correct, inspect table schemas, fix data types, or verify column mappings from the source database.

Write dlthub transformation functions that map source tables to CDM entities. Use after generate-cdm to produce the transformation Python script.

Debug dlthub transformation failures. Use when a transformation fails on a different destination than it was developed on, SQL dialect errors occur after deployment, pipeline recovery is needed after a failed run, or columns are silently dropped from output.

Switch a dlthub transformation from full-replace to incremental loading. Use when the user wants to process only new or changed rows, reduce transformation run time, or schedule frequent transformation runs without reprocessing all data.

Validate toolkit components and project docs — check external doc URLs, cross-references between skills/commands/rules, and verify README.md and CLAUDE.md are in sync with actual toolkit state. Use when the user asks to validate, review, or check toolkit quality.

Generate a Canonical Data Model (CDM) in DBML using Kimball dimensional modeling. Use after create-ontology to produce the implementation-ready CDM schema.

Add a new table or view to an existing dlt SQL database pipeline. Use when the user wants to load additional tables from the same database that already has a working pipeline.

Improve existing skills based on the current session. Use at the end of a session (or when the user asks) to capture new debugging patterns, data issues, data validation tracks, querying techniques, doc references, or workflow improvements learned during the session. Keeps skills lean and personalized.

Annotate dlt pipeline sources for transformation. Use when the user wants to transform data, do data modelling, design a data model, describes their data sources and use cases, or wants to build a CDM from existing pipelines.