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Apache Spark Job Manager

Verwaltet und führt Apache Spark-Jobs aus

Was ist Apache Spark Job Manager?

Der Apache Spark Job Manager ist eine Skill-Integration, die es KI-Agenten wie Claude, Cursor oder Codex ermöglicht, Apache Spark-Jobs direkt zu verwalten und auszuführen. Er unterstützt die Sprachen Scala, Java, Python und R (veraltet) und nutzt die optimierte Engine von Apache Spark für verteilte Datenverarbeitung. Der Skill ermöglicht interaktive Shells (Python, Scala) sowie den Build-Prozess über SBT oder Maven. Er bietet außerdem Zugriff auf Beispielprogramme und umfassende Dokumentation der offiziellen Apache Spark-Repositorys.

Funktioniert mit~Claude Code~Codex CLI~Cursor
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Dokumentation

Was macht Apache Spark Job Manager?

Voraussetzungen

Java, Scala, Python

Installation

Anforderungen und Hinweise aus dem Upstream:

  • High-Level-APIs in Scala, Java, Python und R (veraltet) sowie eine optimierte Engine, die

  • Interaktive Python-Shell

  • Alternativ können Sie bei Verwendung von Python die Python-Shell nutzen:

Grundlegende Nutzungshinweise:

Dokumentation

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.

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Calls Adobe Firefly

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