dataflow-spark-oom-troubleshooter
dataflow-spark-oom-troubleshooter is a Runtime diagnostics & profiling skill for AI agents, published by oracle-samples in the oracle-dataflow-samples repository on GitHub. Skillspace rates dataflow-spark-oom-troubleshooter B (81/100) and it passed the safety scan; the rating is based on static analysis only.
Diagnose Oracle Data Flow and Spark SQL OOM and executor-loss incidents. Use when troubleshooting Spark event logs, Spark History Server metrics, physical plans, OutOfMemoryError, Kubernetes OOMKilled or exit 137, FetchFailedException, ExecutorDeadException, broadcast or shuffle memory pressure, AQE plan changes, memoryOverhead sizing, driver/executor logs, or cluster sizing.
- Grade
- B
- Quality
- 81 / 100
- Safety
- Passed
- GitHub stars
- 44
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- UPL-1.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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