Engineering Culture

Use when the user asks for founder product video or a task matching the examples below. Generate a 65-second founder-style product video from a product URL + user-supplied imagery. Output is a 16:9 1080p MP4 — 4 × 15s SeeDance acts of a talking founder + 5s branded end card + background music. The user's actual product screenshots are composited onto product reveal shots after Seedance generation, so readable UI and brand text are real, not AI-imagined. Triggers — "founder video", "product video", "60s pitch video", "make a video of [founder] for [URL]", "talking founder explainer". Requires Pika MCP. Uses a supplied brand kit folder (`brand.json` or an exported build-a-brand kit with `brand.md`, tokens, logo assets); if no kit exists, run build-a-brand first.

This skill should be used when the user asks to "find a dashboard", "search dashboards", "does a dashboard exist for X", "find widgets that query Y", "which dashboards use this field", "find a dashboard about errors", "look up dashboards by description", "search for existing monitoring dashboards", "find widgets that reference a field", or wants to discover existing Coralogix dashboards or widgets using natural-language or field-based search.

Help users build and manage marketplace liquidity. Use when someone is working on a marketplace business, struggling with supply/demand balance, trying to improve match rates, or asking how to reach critical mass in a two-sided market.

Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill.

Build and scale developer-led adoption through ecosystem programs. Use when deciding open vs curated ecosystems, building developer programs, scaling platform adoption, or designing student program pipelines.

Help users build relationships with journalists and get press coverage. Use when someone is pitching reporters, preparing for media outreach, trying to get press coverage, or managing ongoing journalist relationships.