bootstrap

Build 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

Load 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

Turn 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].

6 skills · 1 MCP server

Shared rules, secrets handling, background agent authoring and the workspace MCP server used by the other dlthub toolkits. Requires dlt[hub].

5 skills · 1 MCP server

Add column-level checks and load metrics to dlt pipelines, run them on every load and review failures with diagnosis. Requires dlt[hub].

4 skills

Prepare and deploy your dlthub workspace and pipelines to the dltHub platform, and debug failed deployments. Requires dlt[hub].

4 skills

Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data

9 skills · 9 commands · 11 agents · 1 MCP server

AI memory system — mine projects and conversations into a searchable palace. 47 MCP tools, auto-save hooks, guided setup.

3 skills · 6 commands · 1 MCP server · hooks

Automates browser interactions for web testing, form filling, screenshots, and data extraction

12 skills

Perform AI code review on Git diffs — supports workspace changes, branch ranges, and single commits with concurrent per-file analysis, codebase search, and deep context-aware review.

2 commands

The official CCXT MCP server plus the ccxt-mcp usage skill — market data, balances and opt-in trading across 100+ crypto exchanges and prediction markets, with keys kept local.

1 MCP server

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents