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Apache Superset Dashboard und SQL-Exploration Skill

Apache Superset ist eine weit verbreitete Open-Source-BI-Plattform für SQL-Exploration, Diagrammerstellung und Dashboard-Bereitstellung. Dieser Skill ist nützlich, wenn ein Agent Warehouse-Daten abfragen, Dashboards zusammenstellen oder Metriken mit einer ausgereiften Analyseschnittstelle anstelle von Ad-hoc-Notebook-Code erklären muss.

Was ist Apache Superset Dashboard und SQL-Exploration Skill?

Dieser Skill ermöglicht es einem KI-Agenten, Apache Superset zu nutzen, eine führende Open-Source-BI-Plattform für SQL-Exploration, Diagrammerstellung und Dashboard-Bereitstellung. Er ist besonders wertvoll, wenn ein Agent strukturierte Daten aus einem Data Warehouse abfragen, interaktive Dashboards erstellen oder Metriken auf eine professionelle und wiederholbare Weise erklären muss. Der Skill kann mit jeder SQL-fähigen Datenquelle (Presto, Trino, Athena u.v.m.) zusammenarbeiten und nutzt die SQLAlchemy- und Python-DB-API-Integration von Superset. Entwickler können diesen Skill nutzen, um Ad-hoc-Analysen zu standardisieren, die Zusammenarbeit zu verbessern und Berichte zu erstellen, ohne auf temporären Notebook-Code zurückgreifen zu müssen. Er unterstützt sowohl schnelle Einrichtungen (Quickstart) als auch produktive Bereitstellungen und ist in der Apache-Superset-Community weit verbreitet.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/apache-superset-dashboard-sql-exploration-skill
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Aus dem Skill-README

Build Status
PyPI

Dokumentation

Was macht Apache Superset Dashboard und SQL-Exploration Skill?

Apache Superset ist eine weit verbreitete Open-Source-BI-Plattform für SQL-Exploration, Diagrammerstellung und Dashboard-Bereitstellung. Dieser Skill ist nützlich, wenn ein Agent Warehouse-Daten abfragen, Dashboards zusammenstellen oder Metriken mit einer ausgereiften Analyseschnittstelle anstelle von Ad-hoc-Notebook-Code erklären muss.

Installation

Anforderungen und Hinweise aus dem Upstream:

  • Build Status
  • PyPI
  • Superset kann Daten aus jeder SQL-fähigen Datenquelle oder Daten-Engine (Presto, Trino, Athena, und mehr) abfragen, die einen Python-DB-API-Treiber und einen SQLAlchemy-Dialekt besitzt.

Grundlegende Nutzung oder Erste-Schritte-Notizen:

Quelle

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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act Local GitHub Actions Runner

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Add a browser review checkpoint before risky agent actions with

Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.

Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

Add IDE-style semantic code navigation and symbol-safe refactors

Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

Add managed long-term memory to LangGraph agents with LangMem

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

Add multimodal lifelong memory to MCP and Python agents with Sim

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

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