e2e-agentic-data-prototyping

Scan design/ for backlog/todo artifacts, check if work has been merged into the codebase, and auto-rename completed ones to done-. Use for periodic hygiene, after merging branches, or to audit design artifact status.

Use when writing Dagger pipelines in Python, building containerized CI/CD with the Dagger Python SDK, creating Dagger modules/functions/objects, integrating services (PostgreSQL, Redis) in Dagger, using Dagger LLM/agent features, or running Dagger in GitHub Actions.

Use when creating specifications, implementation plans, or tools for a feature - enforces consistent naming across related artifacts

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Use this skill when the user wants to run hotdata CLI commands, query the Hotdata API, list workspaces, list connections, create connections, list tables, manage datasets, execute SQL queries, inspect query run history, search tables, manage indexes, manage sandboxes, manage workspace context and the data model via the context API (`hotdata context`), or interact with the hotdata service. Activate when the user says "run hotdata", "query hotdata", "list workspaces", "list connections", "create a connection", "list tables", "list datasets", "create a dataset", "upload a dataset", "execute a query", "search a table", "list indexes", "create an index", "list query runs", "list past queries", "query history", "list sandboxes", "create a sandbox", "run a sandbox", "workspace context", "pull context", "push context", "data model", or asks you to use the hotdata CLI.

Use when building, deploying, or debugging applications on Modal (modal.com) — serverless cloud compute for Python. Covers container images, GPU functions, web endpoints, volumes, secrets, scheduling, distributed training, calling deployed functions, and scaling patterns.

Use when calling deployed Modal functions from a CLI or external service without needing the Modal app running locally

Use when you have a spec or requirements for a multi-step task, before touching code

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, designs the solution, and writes a structured spec document. Also use directly when you have requirements and need to produce a spec.

Use when executing implementation plans with independent tasks in the current session

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.

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-runtime toolkit).

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.

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-runtime toolkit), or assembling the marimo notebook from an analysis plan (use build-notebook).

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.

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.

Safely manage dlt 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.

Helps users figure out what they can build with dlt and which workflow to start. MUST use this skill when the user asks questions like 'what can you do', 'how do I build a pipeline', 'how do I make reports', 'how do I deploy', 'what are toolkits', 'what's available', 'I'm new to dlt', 'where do I start', or seems confused about what to do next after initial setup. Also use when the user asks broad capability questions about data engineering with dlt. Do NOT use when the user has a specific task in progress like debugging a pipeline, validating data, or adding endpoints.

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.

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.