finetuning-technique
finetuning-technique is a Fine-tuning & training data skill for AI agents, published by awslabs in the agent-plugins repository on GitHub. Skillspace rates finetuning-technique B (81/100) and it passed the safety scan; the rating is based on static analysis only.
Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be selected (via model-selection skill).
- Grade
- B
- Quality
- 81 / 100
- Safety
- Passed
- GitHub stars
- 834
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
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 1 collections
- Scope
- Vendor-specific: built around one product. Finetuning technique choice on SageMaker AI, a public AWS platform
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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