broomva

Produce high-retention, dopamine-aware video content using brainrot editing techniques — fast cuts, word-by-word captions, sound design, visual velocity, pattern interrupts — all in service of genuinely valuable content. The Fireship model: brainrot pacing meets real substance. Built on Remotion with Imagen/Veo for assets. Use when: (1) creating edutainment content that competes with brainrot for

Brand icon and visual identity management for BroomVA projects. Generates favicons, app icons, Open Graph images, and social media avatars from brand assets. Maintains consistent visual identity across web, mobile, and social platforms. Use when: (1) generating favicon sets (ICO, PNG, SVG) for a web project, (2) creating app icons for iOS/Android, (3) generating Open Graph and Twitter Card images,

Autonomous social engagement + knowledge extraction loop for Moltbook and X/Twitter. Runs the engagement loop (post substantive comments, solve verification challenges, reply to mentions, quote-tweet), extracts knowledge from all interactions into the broomva research vault, and compounds with /blog-post and /content-creation to turn discovered insights into published content. Integrates with know

Grounded procurement research for any real-world need. Turn a problem ("reduce bedroom noise from the avenue", "add air conditioning", "hire an accountant", "buy a TV mount", "replace the kitchen sink") into 3–5 ranked alternatives spanning DIY-retail → mid-retail → specialty → contractor → consultant/turnkey, each with cited price bands (low / typical / high), confidence scores, locale-aware curr

Generate a polished Remotion video and X thread showcasing the full agent skills inventory. Use when creating social media content about skills, rendering category-based skill visualizations, or producing animated showcases of agent capabilities. Triggers on "skills video", "showcase skills", "skills thread", "render skills", "social content for skills", or requests to visualize the skills invento

Full-stack AI content studio — orchestrates visual DNA compilation, cinematic generation (via Higgsfield CLI or MCP), browser-automated tool execution, and multi-platform distribution into a unified content pipeline. Compiles brand identity, character sheets, and style guides into persistent knowledge (Karpathy compile-then-query pattern), then generates premium cinematic content using Higgsfield

OpenCaptions extension for Content Engine — adds intent-driven CWI (Caption With Intent) captions to the post-production pipeline. Hooks into the grade → caption → final stage. Generates captions that understand video intent (pitch, volume, emotion, emphasis) and style themselves accordingly with variable font weight, size, and color. Uses the OpenCaptions CLI or MCP server. Triggers on: 'add capt

Captures a decision with context, alternatives considered, and rationale, then links it to the relevant project doc in the vault. Creates a searchable decision record. Use when the user says "decision log", "log this decision", "record this choice", "document why we chose", "decision record", "ADR", or has just made an important decision.

Compares stated priorities against where time and effort actually went, and produces a strategy drift report. Uses git log, vault priorities, and project docs to detect misalignment. Use when the user says "drift check", "am I on track", "priority alignment", "where did my time go", "strategy drift", or wants to audit focus vs intention.

Wealth management, financial planning, and investment analytics skill. Compounds on finance-substrate for Colombian tax-optimized wealth building. Runs descriptive (portfolio health), predictive (compound growth projections, Monte Carlo), and prescriptive (allocation, rebalancing, tax-efficient withdrawal) analytics. Use when: (1) projecting long-term wealth growth, (2) optimizing asset allocation

Microsoft BitNet — 1-bit LLM setup, inference, and benchmarking on CPU. Automates the full workflow: clone bitnet.cpp, create conda env, download GGUF models from HuggingFace, build optimized ternary kernels, and run inference. Supports official Microsoft models (2B) and community models (0.7B-10B). Use when: (1) setting up BitNet/bitnet.cpp for local CPU inference, (2) downloading and running 1-b

Headless rocket design, simulation, and optimization using OpenRocket's Java core engine. Includes rocket-sim CLI tool for batch simulations and parameter sweeps, plus an AI-driven optimization loop using Claude. Use when: (1) designing model rockets programmatically, (2) running headless flight simulations, (3) optimizing rocket designs for altitude/stability/velocity, (4) batch-processing .ork f