hwy-game

direct-image-creation

Turn a creative request into a production-ready AI image brief, reference plan, node workflow, and model-profile prompt. Use for text-to-image, surgical editing, exact text or infographics, image analysis and style transfer, logos and brand kits, ads and publishing assets, ecommerce listing sets, product/food/fashion/portrait imagery, virtual try-on and identity-preserving effects, interiors and floor plans, UI mockups, character sheets, presentation visuals, social covers and thumbnails, multi-panel grids, storyboards, visual variants, composition changes, or improving an image-generation node.

hwy-game 0 Updated 2mo ago

Resources

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GitHub

Install

npx skillscat add hwy-game/metoai/direct-image-creation

Install via the SkillsCat registry.

SKILL.md

Direct AI image creation

Use $operate-content-workflow for inspection and mutations. Capabilities are the source of truth for executable models and modes.

Route the task

  1. Inspect project state, references, and image capabilities.
  2. If no concrete visual concept exists, invoke $develop-creative-concept before authoring prompts.
  3. Classify the request as new generation, edit, variation, composition transfer, character/product continuity, or multi-asset set.
  4. Define the brief and acceptance criteria before creating nodes.
  5. Generate a small candidate set when direction is uncertain; select before producing expensive derivatives.
  6. Review the actual output with $review-content-quality and repair the smallest failing dimension.

Read only the references needed:

Brief requirements

Record purpose, audience, publishing surface, aspect ratio, subject, action/pose, environment, composition, lighting, palette, medium, references and their roles, immutable details, exclusions, deliverables, and acceptance criteria. Distinguish hard constraints from preferences.

Prefer an explicit visual decision over a pile of adjectives. Make each variation change one named axis such as composition, palette, lens, pose, or rendering medium.

This method is an original Vetta adaptation informed by Generative-Media-Skills (MIT), visual-skills by Serge Shima (CC BY 4.0, https://github.com/smixs/visual-skills), and ViMax (MIT).

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