ai-research-repo

Turn a research agenda into one cold-readable `/goal` prompt for autonomous AI research loops, with evidence-graph notebook rules, experiment cadence, verification, scope edges, and a finite, batch, or continuous completion contract.

Maintain the research workspace belief layer: open questions, claims, decisions, confidence levels, evidence links, and revisit conditions.

Rank candidate experiments, decide what not to run next, maintain the factory queue, and choose one decisive next research goal before autonomous loops spend compute.

Create, run, and record AI research experiments with manifests, run records, artifact paths, metrics, and experiment index updates.

Compress an AI research experiment batch or branch into lessons, updated claims/questions, and a continue/stop/pivot decision.

Route AI research workspace work across the evidence graph. Use for broad research tasks, choosing which research sub-skill applies, or updating the dashboard after experiment, claims, synthesis, or reference work.

Remote CUDA development workflow for the local NVIDIA DGX Spark at spark-f0a3.local. Use when Codex needs to probe the DGX Spark, choose a developer experience, mirror a research workspace with rsync, run CUDA/PyTorch/NAC/Le-WM training remotely while the agent stays on the Mac, use tmux/screen, manage TensorBoard tunnels, sync artifacts back, or decide between direct SSH, Docker, NVIDIA AI Workbench, and native Python setup.

Capture personal "now it clicked" learning notes in AHA/ when the user says a concept clicked, asks to remember an explanation, or wants a durable note of what they did not understand and what they learned.

Add or update research paper cards, reference notes, external repo links, and evidence links from papers to claims and experiments.