benchmark-tune
benchmark-tune is an MLOps & model serving skill for AI agents, published by Mesh-LLM in the mesh-llm repository on GitHub. Skillspace rates benchmark-tune D (67/100) and it passed the safety scan; the rating is based on static analysis only.
Use this skill when running, debugging, interpreting, or documenting mesh-llm benchmark tune model-serving throughput trials, including choosing ctx/batch/ubatch/mmap/mlock/speculative-decoding sweeps, running benchmark tune on local or SSH hosts, collecting JSON evidence, and applying tolerance-aware recommendations. Trigger for requests mentioning benchmark tune, tuning tok/s, ctx_size tradeoffs, mmap or mlock tuning, speculative decoding, MTP, ngram, draft models, or replacing old gpu tune usage.
- Grade
- D
- Quality
- 67 / 100
- Safety
- Passed
- GitHub stars
- 2,502
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
- 98 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Apache-2.0
- 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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