9 plugin dari marketplace Claude Code publik. Masing-masing membundel skill, slash command, agent, hook, atau MCP server yang di-install bersama.
agent-powerupsyeaight7/agent-powerups1
pmm-skillsstefanoskarakasis/Product-Marketing-Skills1Kirim repo dengan .claude-plugin/marketplace.json dan kami akan mencantumkan setiap plugin.
Kirim marketplaceML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.
Nightly offline self-evolution for this repo's Claude agent: harvests past Claude Code sessions (read-only), mines recurring tasks, replays them offline on your own API budget, and consolidates learnings into validated CLAUDE.md memory and SKILL.md skills behind a held-out gate, staged for review (never auto-applied). Verbatim vendor of microsoft/SkillOpt's stdlib-only skillopt_sleep engine + Claude Code plugin surface (MIT).
Explain technical sources with source-grounded analogies and learner checks.
Train and optimize machine learning models with automated workflows
ML pipeline design, experiment tracking, model evaluation, training debugging, and reproducibility.
developer toolsMeta skills that operate on the skill system itself: skill review, session learning, cross-skill synthesis and output verification.
Deep learning optimization techniques
Transfer learning adaptation
