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 TTAWDTT/elegant-researcher-skill --skill ml-training-recipesgit clone --depth 1 https://github.com/TTAWDTT/elegant-researcher-skillWrote 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/ttawdtt/elegant-researcher-skill/ml-training-recipes)<a href="https://agentmods.dev/skills/ttawdtt/elegant-researcher-skill/ml-training-recipes"><img src="https://agentmods.dev/badge/skills/ttawdtt/elegant-researcher-skill/ml-training-recipes/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/ttawdtt/elegant-researcher-skill/ml-training-recipes"><img src="https://agentmods.dev/badge/skills/ttawdtt/elegant-researcher-skill/ml-training-recipes.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 | $0.00088 | $0.03005 |
| Opus 5 | $0.00044 | $0.01503 |
| Sonnet 5 | $0.00018 | $0.00601 |
| Haiku 4.5 | $0.00009 | $0.00300 |
Grade A, and why
ml-training-recipes 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.
This is a copy
100% identical to ml-training-recipes — 638 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Training Recipes
Battle-tested patterns for PyTorch training across domains. Drawn from production codebases (Karpathy's autoresearch/nanochat, torchvision, HuggingFace) and modern training practice.
Reference files (read when needed)
references/architecture.md— Transformer/LLM architecture code patterns, weight initreferences/optimizers.md— Muon, AdamW hybrid, per-group LR, compiled optimizer stepsreferences/domain-specific.md— Vision, diffusion, contrastive, distributed, checkpointing, data loadingreferences/scaling-and-selection.md— Scaling laws, compute budget tables, decision trees, DGX Sparkreferences/biomedical.md— Drug discovery, protein models, medical imaging, genomics, clinical NLPreferences/experiment-loop.md— Autonomous experiment loop (autoresearch keep/discard/revert)
Architecture Selection
Pick the right model by data type and data scale:
| Data Type | < 10K samples | 10K-100K | > 100K |
|---|---|---|---|
| Images | Pretrained CNN + fine-tune | Fine-tune ViT or CNN | ViT from scratch |
| Text (gen) | Few-shot prompting | Fine-tune GPT/LLaMA (LoRA) | Pretrain from scratch |
| Tabular | XGBoost/LightGBM | Still XGBoost | Neural viable |
| Audio | Pretrained Whisper | Fine-tune AST | Train from scratch |
| Molecules | Pretrained GNN | Fine-tune molecular LM | Train GNN from scratch |
| Proteins | ESM-2 embeddings + head | Fine-tune ESM-2 | Train protein LM |
| Medical img | Pretrained CNN | nnU-Net (auto-config) | Swin-UNETR / MedSAM |
Key principle: architecture matters less than training recipe at equal compute. A well-tuned ResNet beats a poorly-tuned ViT (ref: "ResNet Strikes Back", Wightman 2021).
For biomedical domains, see references/biomedical.md.
For sequence model selection and compute planning, see references/scaling-and-selection.md.
Scaling Laws
Chinchilla rule (Hoffmann et al., 2022)
Compute-optimal training: ~20 tokens per parameter.
What ships with it
6 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.
- 12d ago First seen · 320 lines · 88 tokens per session scan A d7be143e8dbb
ml-training-recipes is a skill published in the GitHub repository TTAWDTT/elegant-researcher-skill (5 stars, last pushed 3mo ago), licensed MIT. It adds 88 tokens to every session and 3,005 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ml-training-recipes, differing in 638 lines, and is treated as a copy.
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