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 nimadorostkar/Claude-Skills-collection --skill fine-tuninggit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/fine-tuning)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/fine-tuning"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/fine-tuning/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/nimadorostkar/claude-skills-collection/fine-tuning"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/fine-tuning.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 | $0.00046 | $0.01456 |
| Opus 5 | $0.00023 | $0.00728 |
| Sonnet 5 | $0.00009 | $0.00291 |
| Haiku 4.5 | $0.00005 | $0.00146 |
Grade A, and why
fine-tuning 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 12d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fine-Tuning
Purpose
Decide whether fine-tuning is warranted, and do it properly if it is. Most fine-tuning projects should have been prompt engineering or retrieval, and the ones that should be fine-tuning usually fail on dataset quality rather than on the training.
When to Use
- A task where prompting has plateaued below the required accuracy.
- A specific output format, style, or domain vocabulary the model will not adopt reliably.
- Reducing cost by making a small model do what currently requires a large one.
- Evaluating an existing fine-tuned model that underperforms.
Capabilities
- Deciding between prompting, RAG, and fine-tuning.
- Dataset construction, curation, and splitting.
- LoRA and QLoRA versus full fine-tuning.
- Hyperparameter selection and overfitting detection.
- Evaluation against the base model on the same set.
Inputs
- The task, and the accuracy prompting achieves on it.
- Available training data — its volume, and honestly, its quality.
- Latency and cost constraints.
Outputs
- A justified decision to fine-tune, or not to.
- A curated dataset with clean train/validation/test splits.
- A model measurably better than the base model on a held-out set.
Workflow
- Exhaust prompting first — Few-shot examples, a clearer output contract, a better model. Fine-tuning cannot teach knowledge the model lacks; it teaches behavior. If the problem is that the model does not know something, use retrieval instead.
- Decide what fine-tuning is actually for — Format adherence, tone, a domain-specific classification boundary, or distilling a large model's behavior into a small one. Those are the cases where it works.
- Build the dataset carefully — This is where the project succeeds or fails. A thousand clean, consistent examples beat fifty thousand noisy ones. Inconsistent labels teach the model to be inconsistent.
- Split before you look — Train, validation, test. The test set is touched once, at the end. Selecting a checkpoint on the test set is how you produce a model that scores well and performs badly.
- Start with LoRA — It is cheap, fast, and sufficient for the majority of tasks. Full fine-tuning is warranted rarely.
- Compare against the base model on the same test set — With the same prompt. A fine-tuned model that does not beat a well-prompted base model is a liability, not an asset.
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.
- 12d ago First seen · 121 lines · 46 tokens per session scan A 0143da6fc7d8
fine-tuning is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 25d ago), licensed MIT. It adds 46 tokens to every session and 1,456 once invoked, about $0.0002 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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