Borrowing it
Nothing to install: this file belongs to Red-Hat-AI-Innovation-Team/training_hub. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Red-Hat-AI-Innovation-Team/training_hub/main/.claude/skills/training-guide/SKILL.mdgit clone --depth 1 https://github.com/Red-Hat-AI-Innovation-Team/training_hubWrote 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/red-hat-ai-innovation-team/training_hub/training-guide)<a href="https://agentmods.dev/skills/red-hat-ai-innovation-team/training_hub/training-guide"><img src="https://agentmods.dev/badge/skills/red-hat-ai-innovation-team/training_hub/training-guide/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/red-hat-ai-innovation-team/training_hub/training-guide"><img src="https://agentmods.dev/badge/skills/red-hat-ai-innovation-team/training_hub/training-guide.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 | $0.00038 | $0.00326 |
| Opus 5 | $0.00019 | $0.00163 |
| Sonnet 5 | $0.00008 | $0.00065 |
| Haiku 4.5 | $0.00004 | $0.00033 |
Grade A, and why
training-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Run Training
Execute LLM training using a saved configuration.
Step 1: Check Environment
"${CLAUDE_PLUGIN_ROOT}/scripts/th_detect.sh"
If not ready
library=missingorconfig=missing: invoke thesetup-guideskill.gpu=unavailable: warn that training requires CUDA-capable GPUs.
If ready (library=installed, config=found)
Proceed to Step 2.
Step 2: Execute Training
Run the training script with any user-provided overrides:
"${CLAUDE_PLUGIN_ROOT}/scripts/th_train.sh" $ARGUMENTS
Step 3: Present Results
- Training status — Whether training completed successfully
- Algorithm used — Which algorithm and backend were used
- Checkpoint location — Where the trained model was saved
- Loss summary — If available, show final loss values
If training failed, consult the training-hub-guide skill for troubleshooting (OOM, loss interpretation, backend-specific issues).
Remind the user they can visualize training loss with training_hub.plot_loss().
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 · 44 lines · 38 tokens per session scan A 04e6f2f7dd21
training-guide is a skill published in the GitHub repository Red-Hat-AI-Innovation-Team/training_hub (95 stars, last pushed yesterday), licensed Apache-2.0. It adds 38 tokens to every session and 326 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
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…
tao-run-automl
Run container-backed AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm selection (bayesian, hyperband, asha, bohb, llm, hybrid, autoresearch), WandB experiment tracking, job execution on any TAO SDK platform, result interpretation, and per-rec custom evaluation…
unsloth
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization.
unsloth
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization.
hugging-face-model-trainer
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV…
Training
Guides fine-tuning and post-training work with explicit data, objective, hardware, and rollback assumptions.