Generate AI images using the Labnana API. Supports text prompts with optional reference images, multiple resolutions, and aspect ratios. Images are saved as local files.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionimage-genExecute the skills CLI command in your project's root directory to begin installation:
Fetches image-gen from marswaveai/skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate image-gen. Access via /image-gen in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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/podcast, /speech)/explainer)/content-parser)Generate AI images using the Labnana API. Supports text prompts with optional reference images, multiple resolutions, and aspect ratios. Images are saved as local files.
shared/authentication.md for API key and headersshared/common-patterns.md for error handlinghttps://api.marswave.ai/openapi/v1shared/config-pattern.md before any interaction.listenhub/image-gen/YYYY-MM-DD-{jobId}/ — never ~/Downloads/Follow shared/config-pattern.md § API Key Check. If the key is missing, stop immediately.
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")
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:
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 "$CONFIG_PATH")
Free text input. Ask the user:
Describe the image you want to generate.
If the prompt is very short (< 10 words) and the user hasn't asked for verbatim generation, offer to help enrich the prompt. Otherwise, use as-is.
Ask:
Question: "Which model?"
Options:
- "pro (recommended)" — gemini-3-pro-image-preview, higher quality
- "flash" — gemini-3.1-flash-image-preview, faster and cheaper, unlocks extreme aspect ratios (1:4, 4:1, 1:8, 8:1)
Ask both together (independent parameters):
Question: "What resolution?"
Options:
- "1K" — Standard quality
- "2K (recommended)" — High quality, good balance
- "4K" — Ultra high quality, slower generation
Question: "What aspect ratio?"
Options (all models):
- "16:9" — Landscape, widescreen
- "1:1" — Square
- "9:16" — Portrait, phone screen
- "Other" — 2:3, 3:2, 3:4, 4:3, 21:9
If flash model was selected, also offer: 1:4 (narrow portrait), 4:1 (wide landscape), 1:8 (extreme portrait), 8:1 (panoramic)
Question: "Any reference images for style guidance?"
Options:
- "Yes, I have URL(s)" — Provide reference image URLs
- "Yes, I have local file(s)" — Provide local file paths (base64 mode)
- "No references" — Generate from prompt only
If URL mode: Collect URLs (comma-separated, max 14). For each URL, infer mimeType from suffix and build:
{ "fileData": { "fileUri": "<url>", "mimeType": "<inferred>" } }
Suffix mapping: .jpg/.jpeg → image/jpeg, .png → image/png, .webp → image/webp, .gif → image/gif
If local file (base64) mode: Collect file paths (comma-separated, max 14). For each file, encode to base64 and infer mimeType from suffix:
# macOS
BASE64_REF=$(base64 -i /path/to/image.png)
# Linux
BASE64_REF=$(base64 -w 0 /path/to/image.png)
Build:
{ "inlineData": { "data": "<base64-encoded>", "mimeType": "<inferred>" } }
Suffix mapping: .jpg/.jpeg → image/jpeg, .png → image/png, .webp → image/webp, .heic → image/heic, .heif → image/heif
Summarize all choices:
Ready to generate image:
Prompt: {prompt text}
Model: {pro / flash}
Resolution: {1K / 2K / 4K}
Aspect ratio: {ratio}
References: {yes — N URL(s) / yes — N local file(s) / no}
Proceed?
Wait for explicit confirmation before calling the API.
fileData or base64 via inlineData)inlineData objectsPOST https://api.marswave.ai/openapi/v1/images/generation with timeout of 600sRead OUTPUT_MODE from config. Follow shared/output-mode.md for behavior.
inline or both: Decode base64 to a temp file, then use the Read tool.
JOB_ID=$(date +%s)
echo "$BASE64_DATA" | base64 -D > /tmp/image-gen-${JOB_ID}.jpg
Then use the Read tool on /tmp/image-gen-{jobId}.jpg. The image displays inline in the conversation.
Present:
图片已生成！
download or both: Save to the artifact directory.
JOB_ID=$(date +%s)
DATE=$(date +%Y-%m-%d)
JOB_DIR=".listenhub/image-gen/${DATE}-${JOB_ID}"
mkdir -p "$JOB_DIR"
echo "$BASE64_DATA" | base64 -D > "${JOB_DIR}/${JOB_ID}.jpg"
Present:
图片已生成！
已保存到 .listenhub/image-gen/{YYYY-MM-DD}-{jobId}/：
{jobId}.jpg
Base64 decoding (cross-platform):
# Linux
echo "$BASE64_DATA" | base64 -d > output.jpg
# macOS
echo "$BASE64_DATA" | base64 -D > output.jpg
# or
echo "$BASE64_DATA" | base64 --decode > output.jpg
Retry logic: On 429 (rate limit), wait 15 seconds and retry. Max 3 retries.
Default: Pass the user's prompt directly without modification.
When to offer optimization:
When to never modify:
Optimization techniques (if user agrees):
shared/api-image.mdshared/common-patterns.md § Error HandlingUser: "Generate an image: cyberpunk city at night"
Agent workflow:
RESPONSE=$(curl -sS -X POST "https://api.marswave.ai/openapi/v1/images/generation" \
-H "Authorization: Bearer $LISTENHUB_API_KEY" \
-H "Content-Type: application/json" \
-H "X-Source: skills" \
--max-time 600 \
-d '{
"provider": "google",
"model": "gemini-3-pro-image-preview",
"prompt": "cyberpunk city at night",
"imageConfig": {"imageSize": "2K", "aspectRatio": "16:9"}
}')
BASE64_DATA=$(echo "$RESPONSE" | jq -r '.candidates[0].content.parts[0].inlineData.data // .data')
JOB_ID=$(date +%s)
DATE=$(date +%Y-%m-%d)
JOB_DIR=".listenhub/image-gen/${DATE}-${JOB_ID}"
mkdir -p "$JOB_DIR"
echo "$BASE64_DATA" | base64 -D > "${JOB_DIR}/${JOB_ID}.jpg"
Decode the base64 data per outputMode (see shared/output-mode.md).
User: "Generate an image in this style" (provides a local file path)
Agent workflow:
/path/to/style-reference.pngMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Keeps context tight: image-gen is the kind of skill you can hand to a new teammate without a long onboarding doc.
image-gen has been reliable in day-to-day use. Documentation quality is above average for community skills.
image-gen is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: image-gen is focused, and the summary matches what you get after install.
We added image-gen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
image-gen fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added image-gen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
image-gen fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for image-gen matched our evaluation — installs cleanly and behaves as described in the markdown.
image-gen reduced setup friction for our internal harness; good balance of opinion and flexibility.
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