ai-observability
ai-observability is an MLOps & model serving skill for AI agents, published by RHEcosystemAppEng in the agentic-plugins repository on GitHub. Skillspace rates ai-observability D (65/100) and it passed the safety scan; the rating is based on static analysis only.
Analyze AI model performance, GPU utilization, and cluster health on OpenShift AI. Use when: - "How is my model performing?" - "What GPUs are available in the cluster?" - "Show me inference latency for Llama" - "Check OpenShift cluster health metrics" - "Trace a slow inference request" - "Correlate errors across my inference stack" Query-driven, read-only analysis. Routes to the appropriate observability domain based on user intent. NOT for deploying models (use /model-deploy). NOT for debugging failed deployments (use /debug-inference).
- Grade
- D
- Quality
- 65 / 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
- 78 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
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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