Claude Code 플러그인
공개 Claude Code 마켓플레이스의 플러그인 9개입니다. 각 플러그인은 함께 설치되는 스킬, 슬래시 명령, 에이전트, 훅 또는 MCP 서버를 묶습니다.
› /plugin marketplace add wshobson/agents
› /plugin install machine-learning-ops@claude-code-workflows
agent-powerupsyeaight7/agent-powerups1공개 Claude Code 마켓플레이스의 플러그인 9개입니다. 각 플러그인은 함께 설치되는 스킬, 슬래시 명령, 에이전트, 훅 또는 MCP 서버를 묶습니다.
agent-powerupsyeaight7/agent-powerups1
pmm-skillsstefanoskarakasis/Product-Marketing-Skills1.claude-plugin/marketplace.json이 포함된 repo를 제출하면 모든 plugin을 목록에 추가합니다.
marketplace 제출ML 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
