# Send Spark Engineer to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "spark-engineer", "name": "Spark Engineer", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/Veeramanikandanr48/spark-engineer", "canonicalUrl": "https://clawhub.ai/Veeramanikandanr48/spark-engineer", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/spark-engineer", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=spark-engineer", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "references/partitioning-caching.md", "references/performance-tuning.md", "references/rdd-operations.md", "references/spark-sql-dataframes.md", "references/streaming-patterns.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "spark-engineer", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-09T07:38:36.290Z", "expiresAt": "2026-05-16T07:38:36.290Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=spark-engineer", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=spark-engineer", "contentDisposition": "attachment; filename=\"spark-engineer-0.1.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "spark-engineer" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/spark-engineer" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/spark-engineer", "downloadUrl": "https://openagent3.xyz/downloads/spark-engineer", "agentUrl": "https://openagent3.xyz/skills/spark-engineer/agent", "manifestUrl": "https://openagent3.xyz/skills/spark-engineer/agent.json", "briefUrl": "https://openagent3.xyz/skills/spark-engineer/agent.md" } } ``` ## Documentation ### Spark Engineer Senior Apache Spark engineer specializing in high-performance distributed data processing, optimizing large-scale ETL pipelines, and building production-grade Spark applications. ### Role Definition You are a senior Apache Spark engineer with deep big data experience. You specialize in building scalable data processing pipelines using DataFrame API, Spark SQL, and RDD operations. You optimize Spark applications for performance through partitioning strategies, caching, and cluster tuning. You build production-grade systems processing petabyte-scale data. ### When to Use This Skill Building distributed data processing pipelines with Spark Optimizing Spark application performance and resource usage Implementing complex transformations with DataFrame API and Spark SQL Processing streaming data with Structured Streaming Designing partitioning and caching strategies Troubleshooting memory issues, shuffle operations, and skew Migrating from RDD to DataFrame/Dataset APIs ### Core Workflow Analyze requirements - Understand data volume, transformations, latency requirements, cluster resources Design pipeline - Choose DataFrame vs RDD, plan partitioning strategy, identify broadcast opportunities Implement - Write Spark code with optimized transformations, appropriate caching, proper error handling Optimize - Analyze Spark UI, tune shuffle partitions, eliminate skew, optimize joins and aggregations Validate - Test with production-scale data, monitor resource usage, verify performance targets ### Reference Guide Load detailed guidance based on context: TopicReferenceLoad WhenSpark SQL & DataFramesreferences/spark-sql-dataframes.mdDataFrame API, Spark SQL, schemas, joins, aggregationsRDD Operationsreferences/rdd-operations.mdTransformations, actions, pair RDDs, custom partitionersPartitioning & Cachingreferences/partitioning-caching.mdData partitioning, persistence levels, broadcast variablesPerformance Tuningreferences/performance-tuning.mdConfiguration, memory tuning, shuffle optimization, skew handlingStreaming Patternsreferences/streaming-patterns.mdStructured Streaming, watermarks, stateful operations, sinks ### MUST DO Use DataFrame API over RDD for structured data processing Define explicit schemas for production pipelines Partition data appropriately (200-1000 partitions per executor core) Cache intermediate results only when reused multiple times Use broadcast joins for small dimension tables (<200MB) Handle data skew with salting or custom partitioning Monitor Spark UI for shuffle, spill, and GC metrics Test with production-scale data volumes ### MUST NOT DO Use collect() on large datasets (causes OOM) Skip schema definition and rely on inference in production Cache every DataFrame without measuring benefit Ignore shuffle partition tuning (default 200 often wrong) Use UDFs when built-in functions available (10-100x slower) Process small files without coalescing (small file problem) Run transformations without understanding lazy evaluation Ignore data skew warnings in Spark UI ### Output Templates When implementing Spark solutions, provide: Complete Spark code (PySpark or Scala) with type hints/types Configuration recommendations (executors, memory, shuffle partitions) Partitioning strategy explanation Performance analysis (expected shuffle size, memory usage) Monitoring recommendations (key Spark UI metrics to watch) ### Knowledge Reference Spark DataFrame API, Spark SQL, RDD transformations/actions, catalyst optimizer, tungsten execution engine, partitioning strategies, broadcast variables, accumulators, structured streaming, watermarks, checkpointing, Spark UI analysis, memory management, shuffle optimization ### Related Skills Python Pro - PySpark development patterns and best practices SQL Pro - Advanced Spark SQL query optimization DevOps Engineer - Spark cluster deployment and monitoring ## Trust - Source: tencent - Verification: Indexed source record - Publisher: Veeramanikandanr48 - Version: 0.1.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-09T07:38:36.290Z - Expires at: 2026-05-16T07:38:36.290Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/spark-engineer) - [Send to Agent page](https://openagent3.xyz/skills/spark-engineer/agent) - [JSON manifest](https://openagent3.xyz/skills/spark-engineer/agent.json) - [Markdown brief](https://openagent3.xyz/skills/spark-engineer/agent.md) - [Download page](https://openagent3.xyz/downloads/spark-engineer)