Cover Image

Generate article cover images with structured dimensions and bundled generation tooling. Use when the user asks to create a cover image, article cover, blog header, newsletter hero image, or banner-style key visual.

Cover Image Generation (cover-image)

Reference Images (Important)

If you use reference images (image-to-image / series reference / consistency refs):

• Reference images must be public URLs.
• HTTPS is strongly recommended.
• http:// may work but is insecure and can be blocked by some networks.
• Local file paths and data: URLs are not supported by the WeryAI gateway.

This skill turns a vague "make a cover image" request into a stable set of structured decisions instead of starting from scratch every time.

Script:

• scripts/scaffold.ts
• scripts/build-batch.ts

Safety & Scope

• Network: This skill calls the WeryAI gateway over HTTPS (https://api.weryai.com).
• Auth: Uses IMAGE_GEN_API_KEY. The key is never printed. It may be persisted only when you explicitly run npm run setup -- --persist-api-key.
• Reference images: Must be public URLs (https:// recommended). http:// may work but is insecure. Local file paths and data: URLs are rejected.
• No arbitrary shell: The generation runtime does not execute arbitrary shell commands.
• Files written: Output images and optional local config under .image-skills/cover-image/ (project) and/or ~/.image-skills/cover-image/ (home).

Use Cases

• article cover images
• blog headers or hero images
• newsletter covers
• banner-style key visuals
• any single visual that should feel like a cover rather than an infographic or comic

Core Dimensions

Choose these six dimensions, then assemble the prompt:

1. type
2. palette
3. rendering
4. text
5. mood
6. aspect

See references/dimensions.md.

Commands

Script	Purpose
scripts/scaffold.ts	Initialize brief.md and prompts/cover.md
scripts/build-batch.ts	Generate batch.json from multiple prompt variants
npm run generate	Generate the cover image
./scripts/vendor/compression-runtime/scripts/main.ts	Compress output for delivery

Workflow

Step 1: Initialize Working Files

Create the working directory:

bash

${BUN_X} {baseDir}/scripts/scaffold.ts \
  --output-dir cover-image/topic-slug \
  --topic "Why Habits Stick" \
  --concept "A repeating loop turning into visible momentum" \
  --type conceptual \
  --palette elegant \
  --rendering editorial \
  --text title-only \
  --mood balanced \
  --aspect 16:9 \
  --lang en

This creates:

• brief.md
• prompts/cover.md

Step 2: Understand the Content

Extract:

• the main theme
• the target reader
• the keywords
• whether the content fits a person, a scene, or an abstract metaphor
• whether title text needs to appear
• the user's language, when it matters for on-canvas text

Step 3: Choose the Dimensions

Default priorities:

• type: conceptual
• palette: elegant
• rendering: editorial or poster
• text: title-only
• mood: balanced
• aspect: 16:9

If the user explicitly specifies style, mood, or aspect ratio, follow that preference.

Language rules:

• default to the source content language
• if the cover includes a title or subtitle, state the target language explicitly in the prompt

Step 4: Refine brief.md and prompts/cover.md

Use brief.md to lock the audience, concept, and references before polishing the final prompt.

Make sure prompts/cover.md clearly states:

• the article or video topic
• the main visual concept or metaphor
• the chosen type / palette / rendering / text / mood / aspect
• the target language for any on-canvas text

Step 5: Build batch.json for Variants

If you want to explore multiple cover directions, put several prompt files in prompts/, for example:

• 01-editorial.md
• 02-poster.md
• 03-cinematic.md

Then generate a batch file:

bash

${BUN_X} {baseDir}/scripts/build-batch.ts \
  --prompts cover-image/topic-slug/prompts \
  --output cover-image/topic-slug/batch.json \
  --images-dir cover-image/topic-slug \
  --model "$M" \
  --jobs 3

The script reads Rendering style: and Aspect ratio: from each prompt file when possible, then maps them into generation task fields.

Step 6: Map to the Bundled Runtime

The bundled image runtime currently exposes one structured style argument, --style, so:

• map rendering to --style
• write palette, type, text, and mood into the prompt body
• keep using --ref when the user provides references

Recommended mapping:

cover rendering	runtime --style
editorial	editorial
poster	poster
cinematic	cinematic
watercolor	watercolor
flat-vector	flat-illustration
ink-brush	ink-brush
chalk	chalk
manga	manga
anime	anime
photoreal	photoreal
3d-render	3d-render
infographic	infographic

Step 7: Build the Prompt

Use references/prompt-template.md. Make sure to:

• put the topic and subject in the first paragraph
• describe cover composition and visual focus in the second paragraph
• describe palette, mood, and text treatment in the third paragraph
• request reserved title space instead of relying on the model to typeset well
• state the title language explicitly if the cover needs on-canvas text

Step 8: Run Generation

Generate through the bundled runtime at npm run generate.

On first use in a new project, run npm run ensure-ready -- --project <your-project> --workflow cover from this skill directory before generation starts. This reads the doctor report and auto-runs bootstrap if local script dependencies are still missing. If the report shows a missing IMAGE_GEN_API_KEY and the user approves, run npm run setup -- --project <your-project> --workflow cover --persist-api-key when the key is already in env, or persist it to .image-skills/cover-image/.env on the user's behalf, then continue without leaving this workflow.

When this skill is first connected, tell the user that the default generation model is Nano Banana 2 (GEMINI_3_1_FLASH_IMAGE). Also tell them it can be switched later whenever another model fits the task better.

Example:

bash

${BUN_X} {baseDir}/npm run generate \
  --promptfiles prompts/cover.md \
  --style editorial \
  --image cover-image/topic-slug/cover.png \
  --ar 16:9 \
  -m "$M"

Batch example:

bash

${BUN_X} {baseDir}/npm run generate \
  --batchfile cover-image/topic-slug/batch.json \
  --jobs 3

If the user has not chosen a model yet, follow this skill's model-selection rules first.

Output Convention

Suggested output directory:

text

cover-image/<topic-slug>/

Suggested minimum files:

• brief.md
• prompts/cover.md
• batch.json
• cover.png

If the content comes directly from chat instead of a file, it is also useful to save the summary or title into source.md for reproducibility.

Iteration

When the user wants changes after seeing the generated cover:

• Style mismatch ("wrong style", "too busy") → change rendering / --style, re-generate. Keep other dimensions.
• Color issues ("too dark", "wrong colors") → adjust palette in the prompt body, re-generate.
• Composition issues ("bad layout", "title position is off") → revise the composition and text-space description in prompts/cover.md, re-generate.
• Want to explore alternatives → create additional prompt variants in prompts/, use build-batch.ts to generate multiple options in one batch.
• Minor tweaks with reference → if the model supports --ref, use the current output as reference with adjusted prompt to refine.

Only re-generate the specific image that needs changes. Do not re-run the entire workflow from scratch unless the user wants a fundamentally different direction.

Definition of Done

• brief.md and prompts/cover.md exist in the output directory.
• The generated cover image matches the chosen type / palette / rendering / text / mood / aspect.
• The image is shown directly to the user with a summary of the parameters used.
• A compressed webp version is produced for delivery.

When Not to Use It

Choose a different higher-level skill for:

• multi-page knowledge comics
• multi-card RedNote image series
• dense infographics
• pure product cutout or white-background product shots

Delivery

When the cover image is ready:

1. Show the image directly — do not just print a file path.
2. Briefly state what was generated: style, aspect ratio, model used.
3. Ask if the user wants changes or is satisfied.
4. Auto-compress: once the user confirms, run the bundled compression runtime to produce a webp version for web/social upload. Deliver both the original and the compressed file.

bash

${BUN_X} {baseDir}/./scripts/vendor/compression-runtime/scripts/main.ts cover-image/topic-slug/cover.png -f webp -q 80

Internal checklist (for agent, not shown to user): chosen type / palette / rendering / text / mood / aspect, model, whether --style or --ref was used, compression done.