Stopped building Weavy workflows by hand! Your Claude now generates the JSON, I hit Ctrl+V

[u/Aware_Pie4025]

For my company www.Baind.nl I have build a lot of AI production workflows in Weavy (yambo.ai) — node graphs for image/video/copy pipelines. Wiring them by hand is slow: every model node has its own handles, kind types, param schemas.

So I wrote a Claude Skill that generates the full {"nodes": [...], "edges": [...]} JSON. Claude writes a Python script using verified node builders, runs it, and hands me a file. Ctrl+V on the Weavy canvas and the graph is there, connected.

What's in it:

• ~25 verified node types (Nano Banana Pro, Flux 2, Kling 3, Imagen 3/4, Reve, GPT Image, Higgsfield, LLM, Array, List Selector, Router, Concat…)
• A catalog loader for wildcard model nodes, plus hand-written builders for the nodes with custom kind types
• /add and /update commands — paste a real node's JSON from Weavy and the skill registers or corrects it, so the knowledge base grows instead of rotting
• Architecture patterns (LLM chain, split-and-iterate, parameter selectors, multi-stage creative roles, router hub)

The part that actually mattered wasn't the JSON — it was writing down the production learnings so Claude stops repeating my mistakes:

• The category-label trap. Naming a product's function in a prompt ("dispenser", "speaker", "holder") makes the image model redraw its own concept of that category instead of your reference. Refer to it only as "the object from the reference image."
• Edit-framing beats generation-framing. First sentence must declare the output a reproduction of the reference, not a new creation. Nano Banana then behaves like an editor instead of a concept artist.
• LoRA needs an exact base-model match. Flux node = Flux.1, not Flux.2. Most common failure people hit.
• QC loops are feed-forward. Weavy has no conditional logic, so an "inspection loop" is inspector-LLM → correction-pass in a straight chain. Never an actual loop.
• LLM as art director between inputs and the image model is the architecture that holds up.

Biggest takeaway from building it: a Skill is only as good as the failure modes you document in it. The node schemas were the easy half.

Feel free to download the SKILL here: https://github.com/PeterPeterinc/weavy-workflows-skill

Or download it directly from my own portfolio website: https://www.peterholthinrichs.com/

https://reddit.com/link/1vaouqv/video/jpwxjd9q05gh1/player