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 seb1n/awesome-ai-agent-skills --skill model-traininggit clone --depth 1 https://github.com/seb1n/awesome-ai-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/seb1n/awesome-ai-agent-skills/model-training)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/model-training"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/model-training/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/seb1n/awesome-ai-agent-skills/model-training"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/model-training.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 | $0.00044 | $0.02108 |
| Opus 5 | $0.00022 | $0.01054 |
| Sonnet 5 | $0.00009 | $0.00422 |
| Haiku 4.5 | $0.00004 | $0.00211 |
Grade A, and why
model-training 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- model-training — 94% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Training
This skill enables an AI agent to train machine learning models on structured or unstructured datasets. It covers the full training lifecycle: loading and preprocessing data, defining model architectures, configuring optimizers and loss functions, running training loops with validation, applying learning rate scheduling, and saving checkpoints. The agent can handle both classical ML and deep learning workflows across frameworks like PyTorch, TensorFlow, and scikit-learn.
Workflow
-
Load and inspect data: Read the dataset from disk, database, or remote storage. Profile the data to understand feature distributions, class balance, missing values, and data types. Split into training, validation, and test sets using stratified sampling when class imbalance is present.
-
Preprocess and transform: Apply feature engineering such as normalization, standardization, tokenization (for text), or augmentation (for images). Build preprocessing pipelines that are reproducible and serializable so the same transforms apply at inference time.
-
Define model architecture: Select or construct the model architecture appropriate for the task. For classical ML, choose estimators like gradient boosting or SVMs. For deep learning, define layers, activation functions, and regularization such as dropout or weight decay. When transfer learning is applicable, load a pre-trained backbone and attach task-specific heads.
-
Configure training: Set the optimizer (Adam, SGD, AdamW), loss function (cross-entropy, MSE, focal loss), learning rate schedule (cosine annealing, step decay, warmup), and batch size. Enable mixed precision training with
torch.amportf.keras.mixed_precisionwhen training on GPUs to reduce memory usage and speed up computation. -
Execute training loop with validation: Train for the specified number of epochs, logging training loss and metrics per batch or epoch. Evaluate on the validation set at regular intervals. Implement early stopping to halt training when validation performance plateaus for a configurable number of epochs (patience).
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 · 166 lines · 44 tokens per session scan A 4a55054ec8aa
model-training is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 2,108 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.
Other skills, from other repositories
shipping-reproducible-results
Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.
diagnosing-ml-failures
Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance…
validating-models-and-claims
Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.
auditing-data-and-ground-truth
Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.
designing-leakage-safe-experiments
Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.
using-data-analysis
Route data analysis and machine learning work to the right skill in this suite. Use when starting any analysis, modeling, validation, or reproducibility task and the matching specialized skill is not yet clear; not needed when one specific skill already clearly applies.