Skills image-gen

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 Labnana API. Supports text prompts with optional reference images, multiple resolutions, and aspect ratios. Images are saved as local files.

Hard Constraints

• No shell scripts. Construct curl commands from the API reference files listed in Resources
• Always read

  shared/authentication.md

  for API key and headers
• Follow

  shared/common-patterns.md

  for error handling
• Image generation uses a different base URL:

  https://api.labnana.com/openapi/v1

• Always read config following

  shared/config-pattern.md

  before any interaction
• Output saved to

  .listenhub/image-gen/YYYY-MM-DD-{jobId}/

  — never

  ~/Downloads/

Step -1: API Key Check

Follow

shared/config-pattern.md

Step 0: Config Setup

Follow

shared/config-pattern.md

If file doesn't exist — ask location, then create immediately:

mkdir -p ".listenhub/image-gen"
echo '{"outputDir":".listenhub","outputMode":"inline"}' > ".listenhub/image-gen/config.json"
CONFIG_PATH=".listenhub/image-gen/config.json"
# (or $HOME/.listenhub/image-gen/config.json for global)

Then run Setup Flow below.

If file exists — read config, display summary, and confirm:

当前配置 (image-gen)：
  输出方式：{inline / download / both}

Ask: "使用已保存的配置？" → 确认，直接继续 / 重新配置

Setup Flow (first run or reconfigure)

1. outputMode: Follow

   shared/output-mode.md

   § Setup Flow Question.

Save immediately:

# Follow shared/output-mode.md § Save to Config
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-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)

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

4:1

1:8

8:1

Step 4: Reference Images (optional)

Question: "Any reference images for style guidance?"
Options:
  - "Yes, I have URL(s)" — Provide reference image URLs
  - "No references" — Generate from prompt only

If yes, 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

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 URLs) / no}

  Proceed?

Wait for explicit confirmation before calling the API.

Workflow

1. Build request: Construct JSON with provider, model, prompt, imageConfig, and optional referenceImages
2. Submit:

   POST https://api.labnana.com/openapi/v1/images/generation

   with timeout of 600s
3. Extract image: Parse base64 data from response
4. Decode and present result

Read

OUTPUT_MODE

shared/output-mode.md

inline

both

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

Present:

图片已生成！

download

both

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.

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

• Image generation:

  shared/api-image.md

• Error handling:

  shared/common-patterns.md

  § Error Handling

Composability

• Invokes: nothing (direct API call)
• Invoked by: platform skills for cover images (Phase 2)

Example

User: "Generate an image: cyberpunk city at night"

Agent workflow:

1. Prompt is short → offer enrichment → user declines
2. Ask model → "pro"
3. Ask resolution → "2K"
4. Ask ratio → "16:9"
5. No references

RESPONSE=$(curl -sS -X POST "https://api.labnana.com/openapi/v1/images/generation" \
  -H "Authorization: Bearer $LISTENHUB_API_KEY" \
  -H "Content-Type: application/json" \
  --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

shared/output-mode.md