image-gen
marswaveai/skills
Generate AI images from text prompts using ListenHub CLI with customizable models, resolutions, and style references.
What is image-gen?
This skill generates AI images from text descriptions using the ListenHub CLI. It supports multiple models (pro and flash), resolutions (1K to 4K), aspect ratios, and optional reference images for style guidance. Use it when you need to create illustrations, cover images, or concept art from text prompts.
- Generate images from text prompts in English or Chinese
- Choose between pro (higher quality) and flash (faster, cheaper) models
- Select resolution (1K, 2K, 4K) and aspect ratio (16:9, 1:1, 9:16, and more)
- Provide up to 5 reference images (local files or URLs) for style guidance
- Save generated images locally to .listenhub/image-gen/ directory
How to install image-gen
npx skills add https://github.com/marswaveai/skills --skill image-gen- ListenHub CLI installed and authenticated
- Node.js environment with npx available
How to use image-gen
- 1.Describe the image you want to generate in natural language
- 2.Select a model: pro for higher quality or flash for speed
- 3.Choose resolution (1K, 2K, or 4K) and aspect ratio (16:9, 1:1, 9:16, etc.)
- 4.Optionally provide up to 5 reference images (file paths or URLs) for style guidance
- 5.Review the summary of your choices and confirm to generate
- 6.Wait for generation to complete; image saves to .listenhub/image-gen/{date}-{jobId}/
Use cases
- Create cover images or illustrations for documents and presentations
- Generate concept art and visual mockups from descriptions
- Produce styled images using reference photos for consistency
- Create multiple variations of an image by adjusting prompts and models
- Generate images in both English and Chinese prompts
- Content creators and designers needing quick visual assets
- Product teams prototyping visual concepts
- Writers and marketers creating illustrated content
- Anyone generating AI art from text descriptions
image-gen FAQ
Pro (gemini-3-pro-image) offers higher quality but slower generation. Flash (gemini-3.1-flash-image) is faster and cheaper, and unlocks extreme aspect ratios like 1:4, 4:1, 1:8, and 8:1.
Yes, you can provide up to 5 reference images as local file paths or URLs. The CLI supports jpg, png, webp, and gif formats, up to 10MB per file.
The skill detects your prompt language automatically (English or Chinese) and passes the appropriate language hint to the model. English keywords generally work best for style descriptions.
Images are saved to .listenhub/image-gen/{YYYY-MM-DD}-{jobId}/ by default. You can reconfigure the output mode (inline, download, or both) in the config.
No, this skill only generates new images from text prompts. For editing existing images, use a different tool.
Full instructions (SKILL.md)
Source of truth, from marswaveai/skills.
name: image-gen description: | Generate AI images from text prompts. Triggers on: "生成图片", "画一张", "AI图", "generate image", "配图", "create picture", "draw", "visualize", "generate an image". metadata: openclaw: emoji: "🖼️" requires: bin: ["listenhub"] primaryBin: "listenhub"
When to Use
- User wants to generate an AI image from a text description
- User says "generate image", "draw", "create picture", "配图"
- User says "生成图片", "画一张", "AI图"
- User needs a cover image, illustration, or concept art
When NOT to Use
- User wants to create audio content (use
/podcast,/speech) - User wants to create a video (use
/explainer) - User wants to edit an existing image (not supported)
- User wants to extract content from a URL (use
/content-parser)
Purpose
Generate AI images using the ListenHub CLI. Supports text prompts with optional reference images (local files or URLs), multiple resolutions, and aspect ratios. Images are saved as local files.
Hard Constraints
- Always check CLI auth following
shared/cli-authentication.md - Follow
shared/cli-patterns.mdfor command execution and error handling - Always read config following
shared/config-pattern.mdbefore any interaction - Output saved to
.listenhub/image-gen/YYYY-MM-DD-{jobId}/— never~/Downloads/
Step -1: CLI Auth Check
Follow shared/cli-authentication.md § Auth Check. If CLI is not installed or not logged in, auto-install and auto-login — never ask the user to run commands manually.
Then follow shared/cli-authentication.md § Auth Mode Detection to determine AUTH_MODE and set:
if [ "$AUTH_MODE" = "openapi" ]; then
CMD_PREFIX="listenhub openapi image"
else
CMD_PREFIX="listenhub image"
fi
All subsequent CLI calls use $CMD_PREFIX instead of hardcoded listenhub image.
Step 0: Config Setup
Follow shared/config-pattern.md Step 0 (Zero-Question Boot).
If file doesn't exist — silently create with defaults and proceed:
mkdir -p ".listenhub/image-gen"
echo '{"outputDir":".listenhub","outputMode":"inline"}' > ".listenhub/image-gen/config.json"
CONFIG_PATH=".listenhub/image-gen/config.json"
CONFIG=$(cat "$CONFIG_PATH")
Do NOT ask any setup questions. Proceed directly to the Interaction Flow.
If file exists — read config silently and proceed:
CONFIG_PATH=".listenhub/image-gen/config.json"
[ ! -f "$CONFIG_PATH" ] && CONFIG_PATH="$HOME/.listenhub/image-gen/config.json"
CONFIG=$(cat "$CONFIG_PATH")
Setup Flow (user-initiated reconfigure only)
Only run when the user explicitly asks to reconfigure. Display current settings:
当前配置 (image-gen):
输出方式:{inline / download / both}
Then ask:
- outputMode: Follow
shared/output-mode.md§ Setup Flow Question.
Save immediately:
NEW_CONFIG=$(echo "$CONFIG" | jq --arg m "$OUTPUT_MODE" '. + {"outputMode": $m}')
echo "$NEW_CONFIG" > "$CONFIG_PATH"
CONFIG=$(cat "$CONFIG_PATH")
Interaction Flow
Step 1: Image Description
Free text input. Ask the user:
Describe the image you want to generate.
If the prompt is very short (< 10 words) and the user hasn't asked for verbatim generation, offer to help enrich the prompt. Otherwise, use as-is.
Step 2: Model
Ask:
Question: "Which model?"
Options:
- "pro (recommended)" — gemini-3-pro-image, higher quality
- "flash" — gemini-3.1-flash-image, faster and cheaper, unlocks extreme aspect ratios (1:4, 4:1, 1:8, 8:1)
Step 3: Resolution and Aspect Ratio
Ask both together (independent parameters):
Question: "What resolution?"
Options:
- "1K" — Standard quality
- "2K (recommended)" — High quality, good balance
- "4K" — Ultra high quality, slower generation
Question: "What aspect ratio?"
Options (all models):
- "16:9" — Landscape, widescreen
- "1:1" — Square
- "9:16" — Portrait, phone screen
- "Other" — 2:3, 3:2, 3:4, 4:3, 21:9
If flash model was selected, also offer: 1:4 (narrow portrait), 4:1 (wide landscape), 1:8 (extreme portrait), 8:1 (panoramic)
Step 4: Reference Images (optional)
Question: "Any reference images for style guidance?"
Options:
- "Yes" — Provide file paths or URLs
- "No references" — Generate from prompt only
If yes: Collect reference image paths or URLs (comma-separated). The CLI handles both local files and URLs natively — no need to distinguish between them.
- Max 5 references
- Supported formats: jpg, png, webp, gif
- Max 10MB per file
Each reference will be passed as a --reference flag to the CLI.
Step 5: Confirm & Generate
Summarize all choices:
Ready to generate image:
Prompt: {prompt text}
Model: {pro / flash}
Resolution: {1K / 2K / 4K}
Aspect ratio: {ratio}
References: {yes — N image(s) / no}
Proceed?
Wait for explicit confirmation before running the CLI command.
Workflow
-
Build CLI command: Construct the
$CMD_PREFIX createcommand with all collected parameters. -
Execute: Run the command with
run_in_background: trueandtimeout: 180000:$CMD_PREFIX create \ --prompt "{description}" \ --model "{model}" \ --lang "{lang}" \ --aspect-ratio {16:9|9:16|1:1} \ --size {1K|2K|4K} \ --jsonIf reference images were provided, add
--referencefor each:$CMD_PREFIX create \ --prompt "{description}" \ --model "{model}" \ --lang "{lang}" \ --aspect-ratio 16:9 \ --size 2K \ --reference ./sketch.png \ --reference ./photo.jpg \ --jsonThe
--langflag provides a language hint for the prompt. Detect from the user's prompt language (e.g., Chinese prompt →zh, English prompt →en). -
Parse result and present
Read
OUTPUT_MODEfrom config. Followshared/output-mode.mdfor behavior.Parse the CLI JSON output to extract the image URL:
IMAGE_URL=$(echo "$RESULT" | jq -r '.imageUrl')inlineorboth: Download to a temp file, then use the Read tool.JOB_ID=$(date +%s) listenhub download "$IMAGE_URL" -o /tmp/image-gen-${JOB_ID}.jpgThen use the Read tool on
/tmp/image-gen-{jobId}.jpg. The image displays inline in the conversation.Present:
图片已生成!downloadorboth: Save to the artifact directory.JOB_ID=$(date +%s) DATE=$(date +%Y-%m-%d) JOB_DIR=".listenhub/image-gen/${DATE}-${JOB_ID}" mkdir -p "$JOB_DIR" listenhub download "$IMAGE_URL" -o "${JOB_DIR}/${JOB_ID}.jpg"Present:
图片已生成! 已保存到 .listenhub/image-gen/{YYYY-MM-DD}-{jobId}/: {jobId}.jpg
Prompt Handling
Default: Pass the user's prompt directly without modification.
When to offer optimization:
- Prompt is very short (a few words) AND user hasn't requested verbatim
- Ask: "Would you like help enriching the prompt with style/lighting/composition details?"
When to never modify:
- Long, detailed, or structured prompts — treat the user as experienced
- User says "use this prompt exactly"
Optimization techniques (if user agrees):
- Style: "cyberpunk" → add "neon lights, futuristic, dystopian"
- Scene: time of day, lighting, weather
- Quality: "highly detailed", "8K quality", "cinematic composition"
- Always use English keywords (models trained on English)
- Show optimized prompt before submitting
API Reference
- CLI authentication:
shared/cli-authentication.md - CLI execution patterns:
shared/cli-patterns.md - Config pattern:
shared/config-pattern.md - Output mode:
shared/output-mode.md
Composability
- Invokes: nothing (direct CLI call)
- Invoked by: platform skills for cover images (Phase 2)
Example
User: "Generate an image: cyberpunk city at night"
Agent workflow:
- Prompt is short → offer enrichment → user declines
- Ask model → "pro"
- Ask resolution → "2K"
- Ask ratio → "16:9"
- No references
$CMD_PREFIX create \
--prompt "cyberpunk city at night" \
--model "gemini-3-pro-image" \
--lang en \
--aspect-ratio 16:9 \
--size 2K \
--json
Parse CLI JSON output per outputMode (see shared/output-mode.md).
Example 2 — With Reference Images
User: "Generate an image in this style" (provides local files and a URL)
Agent workflow:
- Ask prompt → "a serene mountain lake at dawn"
- Ask model → "pro"
- Ask resolution → "2K"
- Ask ratio → "16:9"
- References →
/path/to/style-reference.png,https://example.com/photo.jpg
$CMD_PREFIX create \
--prompt "a serene mountain lake at dawn" \
--model "gemini-3-pro-image" \
--lang en \
--aspect-ratio 16:9 \
--size 2K \
--reference /path/to/style-reference.png \
--reference https://example.com/photo.jpg \
--json
Parse CLI JSON output per outputMode (see shared/output-mode.md).
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