Genel Claude Code pazaryerlerinden 12 eklenti. Her biri birlikte yüklenen beceriler, eğik çizgi komutları, ajanlar, kancalar veya MCP sunucuları içerir.
Bu filtrelere uyan 12 eklenti gösteriliyor.
open-stanleyHarmix/open-stanley1
agent-powerupsyeaight7/agent-powerups1.claude-plugin/marketplace.json içeren depoyu gönderin, biz de her plugini listeleyelim.
Marketplace gönderCurate auto-memory, promote learnings to CLAUDE.md and rules, extract patterns into skills. Ships 5 slash commands (/si:memory-review, /si:promote, /si:extract, /si:memory-status, /si:remember) and 2 sub-agents (memory-analyst, skill-extractor).
ML 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.
Explain technical sources with source-grounded analogies and learner checks.
Open head of content: source-first LinkedIn/X drafting in your voice, feed scouting and comments, scheduled rituals, learning from your edits.
productivitySelf-Learning Multi-agent orchestration framework for spec-driven development and automated verification. With smarter tool calling and leaner context.
Teach and coach rather than deliver answers: Socratic mentoring for juniors, study modes, understanding checks, critical thinking and devil's advocate review, self-learning architecture paths, and scientific paper research.
ML pipeline design, experiment tracking, model evaluation, training debugging, and reproducibility.
developer toolsRigorous methods for statistics, machine learning, and meta-analysis, from question to paper: pre-specified SAPs, statistical and reporting-checklist review, and reproducible R and Python workflows. Every study compounds.
Machine learning framework specialists for PyTorch, TensorFlow, and scikit-learn. Expert guidance for ML model development and training.
Adaptive implementation-grounded learning for Claude Code.
Adaptive, hands-on learning: define one outcome, diagnose the next prerequisite, guide and validate attempts without completing the learning target, verify load-bearing claims, and optionally preserve progress.
