broomva

Content Loop + Distribution — compounds existing broomva skills into a unified content lifecycle. Orchestrates /blog-post, /content-creation, /social-intelligence, /brainrot-for-good, and /brand-icons for multi-platform distribution. Adds performance tracking, feedback-driven identity refinement, content calendar, and campaign management. Triggers on: 'distribute content', 'content loop', 'publish

Fresh-session handoff doc drafting. Produces a stable, single-file human-readable narrative state for the NEXT agent context (fresh session, after `/clear`, after persist iteration, after a tab close). The artifact lives at `docs/handoffs/YYYY-MM-DD-<arc>.md` and follows a stable shape: TL;DR + State-of-the-world (P15 snapshot) + What-was-delivered (PR table with SHAs) + First action + Pickup stat

Takes one set of project facts and generates three versions: technical for engineering, business-impact for leadership, and customer-facing for success teams. Use when the user says "stakeholder update", "write an update", "communicate this to different audiences", "translate for leadership", "customer-facing version", or needs to communicate the same information to different audiences.

Orchestrate Google Colab Pro/Pro+ GPU instances as remote training backends via SSH. Compounds /agent-browser (to launch Colab sessions and install colab-ssh) with SSH (to operate the runtime remotely). Use when: (1) launching a Colab notebook for GPU training, (2) running training jobs on Colab from the local terminal, (3) transferring datasets/checkpoints to/from Colab, (4) monitoring GPU utiliz

Agentic robotics with CaP-X — LLM-driven robot manipulation via code generation. Use when: (1) Setting up CaP-X / CaP-Gym environments for robot manipulation benchmarks, (2) Running CaP-Bench evaluations across LLMs/VLMs on robotic tasks, (3) Building or extending CaP-Agent0 agentic harnesses (skill libraries, visual differencing, parallel reasoning), (4) Training robot coding agents with CaP-RL (

Use Meta's TRIBE v2 brain encoder to validate cortical alignment of AI model representations (LLaMA, V-JEPA2, Wav2Vec, or any encoder) and inform model selection in the Life/Arcan agent OS stack. Use when: (1) Benchmarking whether a new model encoder aligns with human cortical processing, (2) Comparing text encoders by language cortex alignment score, (3) Comparing video encoders by visual cortex