domino-distributed-computing
Work with distributed computing frameworks in Domino including Apache Spark, Ray, and Dask clusters. Covers cluster configuration, on-demand clusters, choosing between frameworks, PySpark usage, and scaling workloads. Use when processing large datasets, parallel ML training, or running distributed compute jobs.
58
66%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
High
Do not use without reviewing
Fix and improve this skill with Tessl
tessl review fix ./skills/distributed-computing/SKILL.mdW007: Insecure credential handling detected in skill instructions.
What this means
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
Why it was flagged
The prompt includes code examples that embed credentials as literal strings (e.g., properties={"user": "user", "password": "pass"}), which encourages placing secrets directly in generated code/commands and would require the LLM to output secret values verbatim if real credentials are used.
- Repository
- dominodatalab/domino-claude-plugin
- Commit
92a240b
- Audited
- Security analysis
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.