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Official agent skill

Finetuning Technique

by awslabs in awslabs/agent-plugins

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.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Finetuning Technique

skills CLI
$ npx skills add awslabs/agent-plugins --skill finetuning-technique -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install awslabs/agent-plugins finetuning-technique --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/sagemaker-ai/skills/finetuning-technique .claude/skills/finetuning-technique && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
finetuning-technique
GitHub stars
915
Token cost
~604 tokens
SKILL.md length
260 words
Files
3 (incl. scripts, references)
Skills in repo
33
Repo updated
First seen
Licence
Apache-2.0

At a glance

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.

  • Works in 3 steps: Determine Finetuning Technique → Validate Technique Availability → Confirm Selections
  • The user has decided to finetune and needs to choose a technique
  • SKILL.md covers When to Use, Prerequisites, Workflow and References
  • Runs Python scripts from its folder; calls python

What it does

Finetuning Technique is an agent skill from awslabs/agent-plugins, published by the product's own GitHub organization. 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).

Its SKILL.md is about 600 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts and reference files (for example `references/finetune_technique_selection_guide.md` and `scripts/get_recipes.py`).

It sits in AI & LLM Engineering, covering Fine-tuning. It works with Amazon SageMaker. The repository describes itself as: Agent Plugins for AWS equip AI coding agents with the skills to help you architect, deploy, and operate on AWS. The licence is Apache-2.0.

When your agent uses it

  • The user has decided to finetune and needs to choose a technique
  • The technique needs to be validated against a model

Example prompts

  • “s use case and validates it against the selected model”
  • “Use the finetuning-technique skill to select a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the…”
  • “/finetuning-technique”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Determine Finetuning Technique
  2. Validate Technique Availability
  3. Confirm Selections

What it can do on your machine

Read from SKILL.md and the folder at commit da51970. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Finetuning Technique loads about 604 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 260 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~604
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from awslabs/agent-plugins at commit da51970, republished under its Apache-2.0 licence (© awslabs). 260 words, ~604 tokens.

Download SKILL.mdSave it as .claude/skills/finetuning-technique/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
finetuning-technique
description
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).
metadata.version
1.0.0

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).

Present the recommendation and reasoning to the user. Ask if they'd like to go with the recommendation or prefer a different technique.

Step 2: Validate Technique Availability
  1. Once the user confirms a technique, retrieve the finetuning techniques available for the selected model by running: python finetuning-technique/scripts/get_recipes.py <model-name> <hub-name>
    • This returns only the techniques the model actually supports, filtered to SFT, DPO, RLVR, and RLAIF. Only these four techniques are supported — ignore any other techniques even if the model's recipes include them.
  2. If the chosen technique is available for the model, proceed to Step 3.
  3. If the chosen technique is not available for the model, explain that the selected model does not support it on SageMaker and offer to go back to model-selection to pick a different model that supports the chosen technique.
Step 3: Confirm Selections

Present a summary to the user:

Here's what we've selected:
- Base model: [model name]
- Fine-tuning technique: [SFT/DPO/RLVR/RLAIF]

References

  • references/finetune_technique_selection_guide.md — Technique guidance (SFT/DPO/RLVR/RLAIF)

© awslabs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts, references) in plugins/sagemaker-ai/skills/finetuning-technique of awslabs/agent-plugins.

  • SKILL.md
  • references/finetune_technique_selection_guide.md
  • scripts/get_recipes.py

Open the folder on GitHubat commit da51970

Compare with similar skills

Finetuning Technique next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Finetuning Technique compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Finetuning Technique this skillawslabs/agent-plugins915—~604Automated safety check: PassApache-2.0
AWS AI MLaws/agent-toolkit-for-aws2.8k—~1.7kAutomated safety check: PassApache-2.0
SageMaker Serving Image Selectionhuggingface/skills11k1 repos~4.6kAutomated safety check: PassApache-2.0
Sentence-Transformers Training Routerhuggingface/skills11k1 repos~2.6kAutomated safety check: PassApache-2.0
Train RlOpenPipe/ART11k—~2.4kAutomated safety check: PassApache-2.0
Qwopus27b Rl TrainingR6410418/Jackrong-llm-finetuning-guide1.7k—~830Automated safety check: PassApache-2.0

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Works with

Categories

Questions about Finetuning Technique

What does Finetuning Technique do?

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. Finetuning Technique is an agent skill from awslabs/agent-plugins, published by the product's own GitHub organization. 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.

When should I use Finetuning Technique?

Finetuning Technique fits situations like: the user has decided to finetune and needs to choose a technique; the technique needs to be validated against a model.

How do I install Finetuning Technique in Claude Code?

Run `npx skills add awslabs/agent-plugins --skill finetuning-technique -a claude-code`. Or copy the skill folder (plugins/sagemaker-ai/skills/finetuning-technique in awslabs/agent-plugins) into .claude/skills/finetuning-technique in your project. Claude Code loads it when a task matches its description.

How do I install Finetuning Technique in Codex?

Run `npx skills add awslabs/agent-plugins --skill finetuning-technique -a codex`. Or copy the skill folder (plugins/sagemaker-ai/skills/finetuning-technique in awslabs/agent-plugins) into .agents/skills/finetuning-technique in your project. Codex loads it when a task matches its description.

Can I use Finetuning Technique in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add awslabs/agent-plugins --skill finetuning-technique -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/finetuning-technique, .gemini/skills/finetuning-technique, .github/skills/finetuning-technique and .opencode/skills/finetuning-technique in your project.

What does Finetuning Technique need to run?

Going by SKILL.md and its folder, Finetuning Technique needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Finetuning Technique access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Finetuning Technique safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Finetuning Technique use?

Finetuning Technique is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Finetuning Technique use?

About 604 tokens (SKILL.md is roughly 2.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 599 tokens, read only when the agent opens those files.

What are the alternatives to Finetuning Technique?

Skills that share tags, products or a category with Finetuning Technique: AWS AI ML (aws/agent-toolkit-for-aws, 2.8k stars), SageMaker Serving Image Selection (huggingface/skills, 11k stars), Sentence-Transformers Training Router (huggingface/skills, 11k stars) and Train Rl (OpenPipe/ART, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Finetuning Technique?

awslabs (a GitHub organization, an official publisher) maintains it in awslabs/agent-plugins, which has 915 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 8, 2026.

Source: awslabs/agent-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.