Agent Skills: Gemini Image Generation

Gemini Image Generation

You detected an image generation request. Confirm intent before proceeding.

Say: "It sounds like you want to generate an image. I can do that using the Gemini API. Let me walk you through the options."

If the user confirms, proceed. If not, stop.

Tip: You can also use /gemini-image <description> to generate images directly.

1. Check Prerequisites

echo "GEMINI_API_KEY set: $([ -n "$GEMINI_API_KEY" ] && echo 'yes' || echo 'no')"
which python3

If GEMINI_API_KEY is not set:

GEMINI_API_KEY is not set. Get one free at https://aistudio.google.com/apikey Then export it: export GEMINI_API_KEY="your-key-here"

Stop and wait for the user to set the key. If python3 is missing, inform the user Python 3 is required.

Note: The Gemini CLI's Nano Banana extension was investigated as an alternative generation path, but it has a known MCP tool registration bug (Gemini CLI v0.34.0 / Nano Banana v1.0.12). The REST API is the only reliable path right now.

2. Gather Image Details

Extract the image description from the user's request and confirm it. Then collect (ask the user or infer from context):

• Model — pick from the model table in reference.md (it lists the current default and trade-offs)
• Aspect ratio — infer from "banner"/"avatar"/"vertical"/etc., default 1:1
• Resolution — 1K default; 2K, 4K, or 512 available
• Service tier — standard default; flex (~50% cheaper, may queue) or priority (~80% more, fastest)
• Reference image — optional path to an existing file
• Output path — auto-generate a descriptive filename in CWD if not given

For full option matrices, model trade-offs, the aspect-ratio inference table, and tier pricing details — Read reference.md.

Export the gathered values as environment variables. Always single-quote user-provided values to prevent shell injection (quotes, backticks, $):

export GEMINI_PROMPT='<user image description, single-quote wrapped>'
export GEMINI_MODEL='<selected model from the reference.md table>'
export GEMINI_ASPECT_RATIO='<selected ratio, e.g. 1:1>'
export GEMINI_IMAGE_SIZE='<selected resolution, e.g. 1K>'
export GEMINI_REF_IMAGE='<reference image path, or empty>'
export GEMINI_OUTPUT_PATH='<output file path>'
export GEMINI_SERVICE_TIER='<FLEX, PRIORITY, or empty for standard>'

3. Build Request JSON

Read request-builder.md and run the build block. It writes the request to /tmp/gemini-image-request-<pid>.json and prints the path. Capture as REQUEST_FILE.

4. API Call

RESPONSE_FILE="/tmp/gemini-image-response-$$.json"
HTTP_STATUS=$(curl -s -o "$RESPONSE_FILE" -w "%{http_code}" -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/${GEMINI_MODEL}:generateContent?key=$GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d @"${REQUEST_FILE}")
export GEMINI_RESPONSE_FILE="$RESPONSE_FILE"
echo "HTTP status: $HTTP_STATUS"

If HTTP_STATUS is 429, wait 30s and retry once. If 400/403/404, see troubleshooting.md — a 404 (or 400 naming the model) usually means the model ID was retired; the "Model Not Found" section there shows how to discover current IDs and retry. Only proceed if 200.

5. Extract and Save Image

Read request-builder.md and run the parse block. It handles base64 decoding, JPEG→PNG conversion when needed, and writes to GEMINI_OUTPUT_PATH.

6. Save Prompt File

PROMPT_FILE="${GEMINI_OUTPUT_PATH%.*}_prompt.txt"
cat > "$PROMPT_FILE" << EOF
Prompt: ${GEMINI_PROMPT}
Model: ${GEMINI_MODEL}
Aspect Ratio: ${GEMINI_ASPECT_RATIO}
Resolution: ${GEMINI_IMAGE_SIZE}
Service Tier: ${GEMINI_SERVICE_TIER:-standard}
Reference Image: ${GEMINI_REF_IMAGE:-none}
Date: $(date -u +"%Y-%m-%dT%H:%M:%SZ")
EOF

7. Cleanup and Report

rm -f "${REQUEST_FILE}" "${RESPONSE_FILE}"

Report:

• Image saved to: {output_path}
• Prompt saved to: {prompt_file}
• File size and any model notes from text parts

Ask: "Would you like to regenerate with different settings, adjust the prompt, or generate another image?"

Error Handling

For HTTP errors, missing image data, JPEG-vs-PNG handling, lowercase imageSize, and invalid service-tier values — Read troubleshooting.md.

Further Reading

• reference.md — model comparison, aspect-ratio inference, resolution/tier matrices
• request-builder.md — the Python build and parse blocks for steps 3 and 5
• troubleshooting.md — error triage and JPEG→PNG conversion paths