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Compétence Apache Superset pour Tableaux de Bord et Exploration…

Apache Superset est une plateforme BI open source largement adoptée pour l'exploration SQL, la création de graphiques et la diffusion de tableaux de bord. Cette compétence est utile lorsqu'un agent doit interroger des données d'entrepôt, assembler des tableaux de bord ou expliquer des métriques à l'aide d'une interface d'analyse mature plutôt que de code notebook ad hoc.

Qu'est-ce que Compétence Apache Superset pour Tableaux de Bord et Exploration… ?

Cette compétence permet à un agent d'utiliser Apache Superset pour interroger des données, construire des graphiques et livrer des tableaux de bord de manière structurée. Apache Superset est une plateforme BI open source mature qui prend en charge de nombreux moteurs de données compatibles SQL (Presto, Trino, Athena, etc.) via des pilotes Python DB-API et SQLAlchemy. L'agent peut ainsi remplacer des scripts notebook improvisés par des requêtes et visualisations reproductibles et professionnelles. La compétence est utile pour les développeurs, analystes de données ou ingénieurs IA qui souhaitent intégrer Superset dans leurs workflows automatisés. Elle s'appuie sur la documentation officielle et la communauté active du projet, avec un accès à des ressources comme le guide de démarrage rapide, les forums StackOverflow, et le code source GitHub. L'installation nécessite un environnement Python compatible, et la compétence est référencée sur l'Agent Skill Exchange.

Compatible avec~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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Compétence Apache Superset pour Tableaux de Bord et Exploration SQL

Apache Superset est une plateforme BI open source largement adoptée pour l'exploration SQL, la création de graphiques et la diffusion de tableaux de bord. Cette compétence est utile lorsqu'un agent doit interroger des données d'entrepôt, assembler des tableaux de bord ou expliquer des métriques à l'aide d'une interface d'analyse mature plutôt que de code notebook ad hoc.

Installation

Exigences et mises en garde en amont :

  • Build Status
  • PyPI
  • Superset peut interroger des données à partir de tout entrepôt de données ou moteur de données compatible SQL (Presto, Trino, Athena, et plus) disposant d'un pilote Python DB-API et d'un dialecte SQLAlchemy.

Notes d'utilisation de base ou de démarrage :

Source

Individual skills in this repo

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

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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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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