rest-api-pipeline
claudeBuild dlt pipelines for REST APIs: find a source, scaffold the pipeline, add endpoints and incremental loading, debug it, validate the data and view it in a report. Requires dlt[hub].
8 skills · 1 MCP server
65
claude
init
Shared rules, secrets handling, background agent authoring and the workspace MCP server used by the other dlthub toolkits. Requires dlt[hub].
65 on GitHubby ScaleVector GmbHv0.1.4https://github.com/dlt-hub/dlthub-ai-harness/blob/master/LICupdated yesterdaydata
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Plugin init from marketplace dlthub-ai-harness in dlt-hub/dlthub-ai-harness. Commands read from the repo's manifest — yesterday.
This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.
What it installs
Skills (5)
create-background-agentWrite 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 it for me', 'every Monday summarise what b
dlthub-routerThe 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 from a REST API', 'ingest from a SQL dat
improve-skillsImprove 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 personaliz
setup-secretsSafely 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,
troubleshoot-pipeline-runGeneral pipeline troubleshooting, when no troubleshooting guide specific to the source or runtime context is present.
MCP servers (1)
dlt-workspace-mcpruns: uv run dlthub ai mcp --stdio
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claudeLoad tables from any SQLAlchemy-supported database into a destination with dlt, and tune performance with the available backends. Requires dlt[hub].
8 skills · 1 MCP server
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transformations
claudeTurn raw dlt pipeline data into a Canonical Data Model: annotate sources, build an ontology, design a Kimball-style model and write @dlt.hub.transformation functions. Requires dlt[hub].
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data-quality
claudeAdd column-level checks and load metrics to dlt pipelines, run them on every load and review failures with diagnosis. Requires dlt[hub].
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dlthub-platform
claudePrepare and deploy your dlthub workspace and pipelines to the dltHub platform, and debug failed deployments. Requires dlt[hub].
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filesystem-pipeline
claudeLoad CSV, Parquet, JSONL or custom files from local disk, S3, GCS, Azure or SFTP into a destination with dlt. Requires dlt[hub].
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