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 skills add j4flmao/agent-skills --skill llm-finetuninggit clone --depth 1 https://github.com/j4flmao/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/j4flmao/agent-skills/llm-finetuning)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/llm-finetuning"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/llm-finetuning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/j4flmao/agent-skills/llm-finetuning"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/llm-finetuning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.00292 |
| Opus 5 | $0.00010 | $0.00146 |
| Sonnet 5 | $0.00004 | $0.00058 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
llm-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 10d 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.
What it actually says
LLM Fine-Tuning (PEFT/LoRA)
Dataset Preparation
- Format data strictly as Instruction/Input/Output pairs.
- Clean and deduplicate to prevent overfitting.
Fine-Tuning Pipeline
%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
A[Raw Data] --> B[Formatting & Tokenization]
B --> C[Base Model]
C --> D{Apply LoRA Adapters}
D --> E[Training Loop]
E --> F[Merged Checkpoint]
LoRA Configuration Snippet
import torch
from transformers import AutoModelForCausalLM
from peft import LoraConfig, get_peft_model
def setup_lora_model(model_id):
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
return get_peft_model(model, lora_config)
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.
- 10d ago First seen · 41 lines · 21 tokens per session scan A acfcede7c298
llm-finetuning is a skill published in the GitHub repository j4flmao/agent-skills (22 stars, last pushed 3d ago), licensed MIT. It adds 21 tokens to every session and 292 once invoked, about $0.0001 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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