agents-optimize
agents-optimize is an LLM evaluation & observability skill for AI agents, published by NVIDIA-NeMo in the nemo-platform repository on GitHub. Skillspace rates agents-optimize F (53/100) and it passed the safety scan; the rating is based on static analysis only.
Optimizes and accelerates a deployed NeMo agent by suggesting switchyard routing splits, skill optimization, prompt/hyperparameter tuning, and evaluations against newly available models. Persists snapshots and structured suggestions to the NeMo files service for diffing and UI rendering. Use when the user asks to optimize, accelerate, tune, route, or reduce the cost of an agent or its underlying model. Trigger keywords - agent optimization, optimize agent, accelerate agent, smaller model, switchyard router, routing split, agents optimize-skills, agents optimize, agents evaluate, new model scan, nemotron, frontier model.
- Grade
- F
- Quality
- 53 / 100
- Safety
- Passed
- GitHub stars
- 47
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
- 80 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- Apache-2.0
- 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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