Rather than hand-writing prompts for image/video models directly, the creator describes a scene in plain language and feeds that description to Claude, which converts it into a stronger, more model-ready prompt.
Distinct from Claude-Assisted Prompt Splitting (divides an existing prompt across elements) and Claude-Assisted Element Auto-Insertion (auto-inserts @-elements into a prompt) — here Claude's role is upstream, generating the prompt itself from a plain-language brief.
The same intermediary pattern works with other LLMs, not just Claude: feed the start-frame image to Gemini or ChatGPT, describe the desired animation in plain language, and ask it to write a detailed prompt for the video model — then read and tweak the result rather than pasting it blindly.
Gotcha with multimodal models like Gemini: they tend to try to animate the image themselves instead of returning a text prompt. The workaround is appending an explicit stop instruction to the request, e.g. "Do not generate, just give me the prompt in all caps at the end."
The same intermediary pattern works with Gemini or ChatGPT in place of Claude: upload the start-frame image to the LLM, describe the intended animation in simple terms, and ask it to return a detailed video-generation prompt — then read and tweak the result rather than pasting it unedited. Gemini specifically tends to start animating the uploaded image itself instead of returning text, so the request needs an explicit override appended: 'Do not generate just give me the prompt in all caps at the end.'
Из тем: Workflow Tooling: Node-Based Pipelines, Claude, and Multi-Model Chains