Claude Code plugins
5 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
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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.
Six Xcode build optimization skills in one plugin: benchmark, compilation analysis, project analysis, SPM build analysis, a recommend-first orchestrator, and a fixer for applying approved changes.
development
U.S. customs and trade-law research assistant for HTS classification, CROSS ruling research, CIT/CAFC decision briefing, duty compilation (General + Special + Chapter 99 + AD/CVD + MPF/HMF), country of origin determination, FTA qualification, and end-to-end import compliance review. Triggers on product classification, tariff questions, customs rulings, trade remedy screening (Section 301/232/201), Partner Government Agency admissibility, and UFLPA forced-labor analysis. Produces attorney-reviewable draft work product with an enforced HTSUS authority hierarchy and explicit evidence ledger.
GraphQL server for Fastify with Mercurius — loaders, subscriptions, federation, JIT compilation
Optimize and build programmatic prompt systems with Stanford DSPy. Signatures, modules (Predict, ChainOfThought, ReAct), optimizer/teleprompter selection, compilation, caching, evaluation. Use when doing programmatic prompt optimization or building compiled prompt programs. Do not use this skill for unrelated requests; route to the nearest named specialist.