Finetuning Technique

by awslabs

Finetuning Technique is an AI Engineering skill for Claude Code, published by awslabs in agent-plugins.

867 stars145 forkson awslabs/agent-pluginsAdded 2026/08/22Repository updated 2026/08/21

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agent-pluginsagent-skillsagentsawscoding-agent-skillscoding-agents

Install in seconds

Install Finetuning Technique

Copy Finetuning Technique into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/finetuning-technique ~/.claude/skills/finetuning-technique

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/awslabs/agent-plugins.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: plugins/sagemaker-ai/skills/finetuning-technique/SKILL.md in awslabs/agent-plugins
Installs to: ~/.claude/skills/finetuning-technique
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What Finetuning Technique does

Finetuning Technique helps choose and validate a fine-tuning method such as SFT, DPO, RLVR, or RLAIF for a selected base model. Use it after model selection when you need to match a technique to a use case and supported recipes.

Finetuning Technique is cataloged under AI Engineering on DirSkills. Finetuning Technique comes from a repository tagged agent-plugins, agent-skills, agents, aws and coding-agent-skills.

Documentation

README

Excerpt

Finetuning Technique

Guides the user through selecting a fine-tuning technique based on their use case and validates compatibility with the selected model.

When to Use

User has decided to finetune and needs to choose a technique
User wants to change their finetuning technique
Technique needs to be validated against a selected model

Prerequisites

A base model has been selected (via model-selection skill). The model name and hub must be known.
A use_case_spec.md file exists. If not, activate the use-case-specification skill to generate it first.

Workflow

Step 1: Determine Finetuning Technique

Consult references/finetune_technique_selection_guide.md to recommend the best-fit technique based on the use case and the user's needs (SFT, DPO, RLVR, RLAIF).

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Finetuning Technique

What else does awslabs publish alongside Finetuning Technique?
Finetuning Technique is one of 25 skills that DirSkills catalogs from awslabs/agent-plugins, the repository it ships in. Its siblings there include AWS Architecture Diagram, AWS Lambda and AWS Lambda Durable Functions. Each one is a separate skill with its own page in this directory, installs the same way Finetuning Technique does, and is maintained by awslabs in that same repository. The rest of the collection is listed on the awslabs/agent-plugins page.
How does Finetuning Technique compare to other AI Engineering skills?
Finetuning Technique ranks #1516 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Finetuning Technique against them. Open each page to compare what they document and how they install.