finetuning

A workflow for generating code that fine-tunes a model with Amazon SageMaker serverless training jobs. Fine-tuning means training an existing model further on a chosen dataset or task.

In plain words
What is it for?
Use it to prepare SageMaker training for supervised fine-tuning, preference optimization, reinforcement learning with verifiable rewards, AI feedback training, or continued pretraining when the required setup is available.
Why use it?
It checks that the required model, training method, files, and cloud setup are ready before generating training code.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/awslabs/agent-plugins/finetuning
Any agent
npx skills add awslabs/agent-plugins --skill finetuning
Clone the repo
git clone --depth 1 https://github.com/awslabs/agent-plugins

Made for: Claude Code, Codex.

Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,269 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00085 $0.02269
Opus 5 $0.00043 $0.01135
Sonnet 5 $0.00017 $0.00454
Haiku 4.5 $0.00009 $0.00227

Measured 2d ago against content hash 02cacd845c06, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

finetuning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 8 executable files (code_templates/dpo.py, code_templates/rlaif_builtin.py, code_templates/rlaif_custom_prompt.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/sagemaker-ai/skills/finetuning/SKILL.md · 183 lines

How it starts

The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prerequisites

Before starting this workflow, verify:

  1. A use_case_spec.md file exists

    • If missing: Activate the use-case-specification skill first, then resume
    • DON'T EVER offer to create a use case spec without activating the use-case-specification skill.
  2. A fine-tuning technique (SFT, DPO, RLVR, RLAIF, or CPT/RFT (for Nova)) and base model have already been selected

    • If missing: Activate the model-selection and/or finetuning-technique skills to collect what's missing, then resume
    • Don't make recommendations on the spot. You MUST activate the appropriate skill.
  3. A base model name available on SageMakerHub has been identified

    • If missing: Activate the model-selection skill to get it
    • Important: Only use the model name that model-selection retrieves, as it may differ from other commonly used names for the same model
  4. The SDK environment has been verified (SDK version, region, execution role)

    • If not done: Activate the sdk-getting-started skill first, then resume
  5. A training dataset uploaded to a bucket in the environment's default region.

    • If not met: Help the user upload the dataset to the correct S3

Critical Rules

Code Generation Rules

  • ✅ Use EXACTLY the imports shown in each code template
  • ❌ Do NOT add additional imports even if they seem helpful
  • ❌ Do NOT create variables before they're needed in that section
  • 📋 Copy the code structure precisely - no improvisation
  • 🎯 Follow the minimal code principle strictly
  • ✅ When writing code, make sure the indentation and f strings are correct

User Communication Rules

  • ❌ NEVER offer to move on to a downstream skill while training is in progress (logically impossible)
  • ❌ NEVER set ACCEPT_EULA to True without explicit user confirmation in the conversation
  • ✅ Always mention both the number AND title of sections you reference
  • ✅ If user asks how to run (notebook): If run_cell is available, offer to run it. Otherwise, tell them to run cells one by one (mention ipykernel requirement).
  • ✅ If user asks how to run (script): Tell them to run with python3 <script>.py

Read the full file on GitHub · 183 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 183 lines · 85 tokens per session scan A 02cacd845c06

Subscribe to this mod's changes

finetuning is a skill published in the GitHub repository awslabs/agent-plugins (876 stars, last pushed 5d ago), licensed Apache-2.0. It adds 85 tokens to every session and 2,269 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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