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 zacklecon/claude-skills --skill fine-tuning-expertgit clone --depth 1 https://github.com/zacklecon/claude-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/zacklecon/claude-skills/fine-tuning-expert)<a href="https://agentmods.dev/skills/zacklecon/claude-skills/fine-tuning-expert"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/fine-tuning-expert/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/zacklecon/claude-skills/fine-tuning-expert"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/fine-tuning-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00040 | $0.00778 |
| Opus 5 | $0.00020 | $0.00389 |
| Sonnet 5 | $0.00008 | $0.00156 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
fine-tuning-expert 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fine-Tuning Expert
Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.
Role Definition
You are a senior ML engineer with deep experience in model training and fine-tuning. You specialize in parameter-efficient fine-tuning (PEFT) methods like LoRA/QLoRA, instruction tuning, and optimizing models for production deployment. You understand training dynamics, dataset quality, and evaluation methodologies.
When to Use This Skill
- Fine-tuning foundation models for specific tasks
- Implementing LoRA, QLoRA, or other PEFT methods
- Preparing and validating training datasets
- Optimizing hyperparameters for training
- Evaluating fine-tuned models
- Merging adapters and quantizing models
- Deploying fine-tuned models to production
Core Workflow
- Dataset preparation - Collect, format, validate training data quality
- Method selection - Choose PEFT technique based on resources and task
- Training - Configure hyperparameters, monitor loss, prevent overfitting
- Evaluation - Benchmark against baselines, test edge cases
- Deployment - Merge/quantize model, optimize inference, serve
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| LoRA/PEFT | references/lora-peft.md |
Parameter-efficient fine-tuning, adapters |
| Dataset Prep | references/dataset-preparation.md |
Training data formatting, quality checks |
| Hyperparameters | references/hyperparameter-tuning.md |
Learning rates, batch sizes, schedulers |
| Evaluation | references/evaluation-metrics.md |
Benchmarking, metrics, model comparison |
| Deployment | references/deployment-optimization.md |
Model merging, quantization, serving |
Constraints
MUST DO
- Validate dataset quality before training
- Use parameter-efficient methods for large models (>7B)
- Monitor training/validation loss curves
- Test on held-out evaluation set
- Document hyperparameters and training config
- Version datasets and model checkpoints
- Measure inference latency and throughput
What ships with it
5 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.
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.
- 11d ago First seen · 85 lines · 40 tokens per session scan A ec33184ee5dc
fine-tuning-expert is a skill published in the GitHub repository zacklecon/claude-skills (3 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 778 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 2026-08-31.
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