Early access: ruflo's cockpit as a Claude Code mod (function hooks) and the one /ruflo command for every ruflo mod — overview with health alerts, swarm topology, claims flow, federation map, plugin health, learning pipeline, MetaHarness radar, memory, cost gauge, agent timeline, approvals and events; a command palette for confirm-gated actions through the ruflo CLI; auto-opens where it docks; no network by default
Control and inspect a live Chrome browser from your coding agent. Record performance traces, analyze network requests, check console messages with source-mapped stack traces, and automate browser actions with Puppeteer.
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.
Persistent project planning files with lifecycle hooks that inject selected plan context. Automatic recovery uses project files only. Explicit catchup modes may read same-project local session records to emit aggregate counts or bounded nonce-framed excerpts. Optional gated mode can request continuation on capable hosts and never runs commands declared in Markdown. No network upload path. Installs across 60+ agents.
Neural network interpretability tools including TransformerLens, SAELens, NNSight, and pyvene. Use when analyzing model internals, finding circuits, or understanding how models compute.
98 skills
Orchestra-Research/AI-Research-SKILLs 14,502
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