Open source alternatives to spark-optimization
24 open source AI Skills apps to use instead, best rated first.
Top picks
- G
github-ops
Launchgithub-ops is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It manages a GitHub repository with a focus on community health, CI reliability and the experience of contributors. It covers triaging issues by classify
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opensource-pipeline
Launchopensource-pipeline is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It prepares a private project for safe public release through a three-stage pipeline. The fork stage strips secrets from a copy. The sanitise st
Open source
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andrej-karpathy-skills
Launchandrej-karpathy-skills is a single CLAUDE.md file from the multica-ai project that is meant to improve how Claude Code behaves on coding tasks. It is derived from Andrej Karpathy's public observations about LLM coding pitfalls. The file dis
Open source
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microsoft/markitdown
Launchmarkitdown-mcp is the MCP server package for MarkItDown, Microsoft's tool for converting many file formats to Markdown for language models. It is a lightweight server that runs over stdio, Streamable HTTP or SSE, and exposes one tool, conve
Open source
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od-contribute
Launchod-contribute is an agent skill in the OpenDesign project that provides a guided, one-click contribution flow to the OpenDesign repository, written so that people without a coding background can send a real pull request. It is locked to the
Open source
- G
github-deep-research
Launchgithub-deep-research is an agent skill from the DeerFlow project that researches a single GitHub repository in several rounds and writes a report in Markdown. It is for requests that need more than a README summary, such as a comprehensive
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About spark-optimization
spark-optimization
Optimize Apache Spark jobs
spark-optimization is an agent skill from the wshobson/agents collection for optimizing Apache Spark jobs. It covers production patterns for partitioning data sensibly, caching data that is reused, reducing the cost of shuffles, which move data between machines and are often the slowest part of a job, and tuning memory and executor configuration. The core concepts begin with the Spark execution model, which explains how a job is split into stages and tasks, so that the agent can reason about where time is spent. The skill applies when optimizing slow Spark jobs, tuning memory and executor settings, implementing efficient partitioning, debugging Spark performance problems, scaling pipelines to large datasets, and reducing shuffle and data skew, where a few partitions hold most of the data and hold back the whole job. It is written for data engineers who run Spark on large datasets and need faster, cheaper jobs without guessing at the causes. The skill's guidance is most reliable when it is checked against the job's own metrics, such as the Spark UI's stage and task timings, before and after each change. The agent proposes changes and the data team decides which ones to adopt on production clusters. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 10.5K installs and passing results from three security audits at the time of the scan.
- Price
- Free
- Made by
- wshobson
- Category
- AI Skills
- Website
- github.com/wshobson/agents/spark-optimization