mtp-tuning
mtp-tuning is an MLOps & model serving skill for AI agents, published by Llaminar in the llaminar repository on GitHub. Skillspace rates mtp-tuning D (62/100) and it passed the safety scan; the rating is based on static analysis only.
Tune, debug, or extend Llaminar V2 MTP/speculative decoding, prefix-cache interaction, vLLM-style accepted-state publication, MTP depth control, grouped verifier kernels, dense/MoE MTP parity, and MTP benchmark dashboards. Use when Codex is asked to improve MTP speed, fix MTP correctness, compare CUDA/ROCm/CPU MTP lanes, work on decode-equivalent grouped verifier rows, remove host/device coherence issues, update the MTP project plan/dashboard, or touch MTP-related tests and perf gates.
- Grade
- D
- Quality
- 62 / 100
- Safety
- Passed
- GitHub stars
- 23
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
- 96 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- AGPL-3.0
- 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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