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Updated 2mo ago

by EmbrasureAI

spark-observability-skills

Open-source agent skills for debugging and optimizing Apache Spark workloads

View on GitHub (opens in a new tab)

54 GitHub stars (54)

+2 in 16 days

Stars gained per day · daily snapshots since Sep 30
Stars gained per day, Sep 30 to Oct 16: +2 in total; the busiest day was Oct 2 with +1 stars.Oct 1: 0 starsOct 2: +1 starsOct 3: +1 starsOct 4: 0 starsOct 5: 0 starsOct 6: 0 starsOct 7: 0 starsOct 8: 0 starsOct 9: 0 starsOct 10: 0 starsOct 11: 0 starsOct 12: 0 starsOct 13: 0 starsOct 14: 0 starsOct 15: 0 starsOct 16: 0 stars
Stars gained between daily snapshots
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  • 3 skills (3 skills found in its SKILL.md files)
  • 14 forks (14 forks)
  • 2mo ago last update (Last updated 2mo ago)

Install

Skills CLI

npx skills add EmbrasureAI/spark-observability-skills

Asks which skills and agents to install. Just one: npx skills add EmbrasureAI/spark-observability-skills --skill debug-slow-spark-job — add -g for every project.

Claude Code

git clone https://github.com/EmbrasureAI/spark-observability-skillscp -r "spark-observability-skills/skills/debug-slow-spark-job" ~/.claude/skills/

Copies the debug-slow-spark-job skill into ~/.claude/skills/ for every project (use .claude/skills/ for one project). Repeat for the other skills listed below.

Plugin

/plugin marketplace add EmbrasureAI/spark-observability-skills

Run inside Claude Code if the repository ships a plugin marketplace, then pick plugins from /plugin.

Skills can run code on your machine — read the SKILL.md first.

What it does

In the author's own words, from each skill's SKILL.md.

  • debug-slow-spark-job

    Diagnose slow, expensive, or regressed Apache Spark and PySpark applications by comparing runtime evidence against a healthy run. Use for long stages, stragglers, skew, shuffle, spill, garbage collection, poor parallelism, small files, slow scans, scheduler delay, executor imbalance, and unexplained compute-cost growth.

    skills/debug-slow-spark-job/SKILL.md on GitHub (opens in a new tab)

  • debug-spark-failure

    Diagnose failed Apache Spark and PySpark applications from History Server evidence, logs, and cluster-manager state. Use for driver or executor crashes, out-of-memory errors, fetch failures, task exceptions, timeouts, repeated retries, aborted stages, and intermittent production failures.

    skills/debug-spark-failure/SKILL.md on GitHub (opens in a new tab)

  • optimize-spark-sql-plan

    Optimize Apache Spark SQL and DataFrame queries using the final Adaptive Query Execution plan and runtime statistics rather than source code alone. Use to reduce runtime, shuffle, spill, scan cost, skew, join amplification, Python UDF overhead, poor partitioning, or unnecessary work while preserving query semantics.

    skills/optimize-spark-sql-plan/SKILL.md on GitHub (opens in a new tab)

Review before you install

A skill is a set of instructions and scripts your agent will follow, and it can run code on your machine. Read the SKILL.md and any scripts first — this listing is not a security review.