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.
npx agentmods add skills/awslabs/agent-plugins/finetuningnpx skills add awslabs/agent-plugins --skill finetuninggit clone --depth 1 https://github.com/awslabs/agent-pluginsWhat 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.
| Model | Per session | Once 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 |
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.
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.
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:
-
A
use_case_spec.mdfile exists- If missing: Activate the
use-case-specificationskill first, then resume - DON'T EVER offer to create a use case spec without activating the use-case-specification skill.
- If missing: Activate the
-
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-selectionand/orfinetuning-techniqueskills to collect what's missing, then resume - Don't make recommendations on the spot. You MUST activate the appropriate skill.
- If missing: Activate the
-
A base model name available on SageMakerHub has been identified
- If missing: Activate the
model-selectionskill to get it - Important: Only use the model name that
model-selectionretrieves, as it may differ from other commonly used names for the same model
- If missing: Activate the
-
The SDK environment has been verified (SDK version, region, execution role)
- If not done: Activate the
sdk-getting-startedskill first, then resume
- If not done: Activate the
-
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_cellis 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
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- code_templates/dpo.py 5.7 KB runs code
- code_templates/rlaif_builtin.py 5.5 KB runs code
- code_templates/rlaif_custom_prompt.py 6.0 KB runs code
- code_templates/rlvr.py 6.2 KB runs code
- code_templates/sft.py 5.5 KB runs code
- references/code_output_guide.md 3.2 KB
- references/continuous_customization.md 8.5 KB
- references/eula_links.md 7.8 KB
- references/rlaif_guide.md 3.8 KB
- references/rlvr_reward_function.md 10 KB
- scripts/mlflow_reference.py 840 B runs code
- templates/nova_rlvr_reward_function_source_template.py 13 KB runs code
- templates/rlvr_reward_function_source_template.py 9.2 KB runs code
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.
- 2d ago First seen · 183 lines · 85 tokens per session scan A 02cacd845c06
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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