dlt-hub/dlthub-ai-harness

debug-deployment

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-workspace

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.

create-background-agent

Write a dltHub background agent: an AGENT.md that runs unattended on the platform, on a schedule, after a job fails, or when someone starts it from the web UI. Use when the user wants work to happen on its own after an event ('whenever a job fails, diagnose…

dlthub-router

The entry point for building anything with dlthub: 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…

create-eval

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',…

rename-component

Rename a skill, command, rule, or agent within a toolkit and update all references. Use when the user wants to rename a plugin component.

run-eval

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…

validate-toolkits

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…

build-notebook

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…

explore-data

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…

define-data-quality-checks

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.…

deploy-minimal-ingestion-pipeline

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".

prepare-deployment

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.

create-filesystem-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).

setup-secrets

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…

optimize-performance

Make a dlt pipeline faster or lighter on memory. Use when the user says a pipeline is slow, takes too long, runs out of memory, uses too much RAM, or wants to optimize, speed up, parallelize, or increase throughput. Covers source-agnostic levers (parallelism,…

view-data

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…

create-sql-database-pipeline

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…

find-source

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…

view-data

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…

annotate-sources

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.

create-transformation

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

incremental-transformation

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.

debug-pipeline

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 load jobs.

debug-pipeline

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 load jobs.

debug-transformation

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.

adjust-endpoint

Adjust a working dlt pipeline for production — remove dev limits, verify pagination (including stuck or looping paginators), configure incremental loading, expand date ranges, and handle rate-limit/429 errors with retries, backoff, and request timeouts. Use…

adjust-table

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. For…

create-ontology

Build a business entity graph (ontology) from annotated sources and taxonomy. Use after annotate-sources to design the entity model before CDM generation.

generate-cdm

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

add-incremental-loading

Add incremental loading to a dlt filesystem pipeline — filter files by modification date, optionally filter records by a timestamp column, and switch to merge with a primary key to deduplicate updated records, so each run only reads new or modified files. Use…

optimize-filesystem-performance

Speed up a dlt filesystem pipeline. Use when reading files (CSV, Parquet, JSONL) from local disk, S3, GCS, Azure, or SFTP is slow or memory-heavy and the user wants to optimize it — choose a faster reader, read files in parallel, narrow the glob, or stream in…

optimize-rest-api-performance

Speed up a dlt REST API pipeline. Use when a REST/HTTP API pipeline is slow because of many sequential requests, nested child resources, or large responses, and the user wants higher throughput — parallelize resources, use async, tune page size and…

optimize-sql-performance

Speed up a dlt SQL database pipeline. Use when extraction from a relational database (postgres, mysql, mssql, oracle, snowflake, etc.) is slow or memory-heavy and the user wants to optimize it — pick a faster backend, tune chunk size, parallelize tables, or…

deploy-run-sample-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…

quick-start

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…

create-rest-api-pipeline

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.

new-endpoint

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.

review-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…

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…