dl-training
dl-training is a Model training & evaluation skill for AI agents, published by pvillega in the claude-templates repository on GitHub. Skillspace rates dl-training F (54/100) and it passed the safety scan; the rating is based on static analysis only.
Deep DL-training operational intuition — initialization, optimizer choice (AdamW/Muon/Schedule-Free), decoupled weight decay, LR schedules (cosine/WSD), μP transfer, mixed precision (bfloat16/FP8), normalization (RMSNorm, Peri-LN), NaN forensics, loss-spike rewind. Load when picking optimizer/schedule, weight-decay setup, debugging loss spikes/NaN, normalization choice, or μP transfer. Skip for architecture selection, inference/serving, or sharding topology. Pairs with `pytorch`. Triggers on: "AdamW", "Muon", "Schedule-Free", "decoupled weight decay", "WSD", "muP", "μTransfer", "GradScaler", "bfloat16", "FP8", "RMSNorm", "Peri-LN", "Fixup", "Mixup", "loss spike", "spike skip".
- Grade
- F
- Quality
- 54 / 100
- Safety
- Passed
- GitHub stars
- 23
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 81 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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