一键导入
google-image
Generate images using Google Gemini/Imagen API. Requires GOOGLE_API_KEY (Gemini Developer API) or GOOGLE_APPLICATION_CREDENTIALS (Vertex).
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Generate images using Google Gemini/Imagen API. Requires GOOGLE_API_KEY (Gemini Developer API) or GOOGLE_APPLICATION_CREDENTIALS (Vertex).
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Produce a labeled figure with PERFECT text — render a label-free image locally (or take an external image) then overlay leader lines and typeset labels programmatically from vision-derived anchor coordinates. Text is correct by construction; only pointer placement is reviewed.
Ask which image models fit a set of tasks — get evidence-based recommendations with per-image costs, which external services to pay for, and which local models to install (with disk/RAM/licence guidance). Reads the live model catalog and probes what is actually available on this machine.
Check mflux (MLX) availability, per-family runtime presence, cached weights, and report readiness status.
Top-level image orchestrator. Routes all slide image generation to the appropriate skill (jack-tar-ollama:image, jack-tar-ollama:icon, jack-tar-ollama:pattern, jack-tar-ollama:diagram, jack-tar-cloud:image, jack-tar-cloud:icon, render_chart). Produces ImageManifest and ChartManifest. Also reads strategy-map.json to determine per-slide rendering approach (full_render, backdrop_render, composed).
Meta-verify — discover all jack-tar engine plugins, call each verify, report aggregate pipeline capability and discipline-hook readiness.
Generate an image locally on Apple Silicon via the mflux CLI (MLX). Flag-compatible with jack-tar-ollama's /image — a $0 local tier, no API keys needed.
| name | google-image |
| description | Generate images using Google Gemini/Imagen API. Requires GOOGLE_API_KEY (Gemini Developer API) or GOOGLE_APPLICATION_CREDENTIALS (Vertex). |
Generate an image via Google Gemini/Imagen API.
PLUGIN_ROOT=$(python3 -c "
from pathlib import Path
import sys, os
if os.environ.get('JACK_TAR_CLOUD_ROOT'):
print(os.environ['JACK_TAR_CLOUD_ROOT']); sys.exit()
home = Path.home()
for base in [home / '.claude' / 'plugins' / 'cache']:
for p in base.rglob('jack-tar-cloud/.claude-plugin/plugin.json'):
print(str(p.parent.parent)); sys.exit()
dev = Path.cwd() / 'plugins' / 'jack-tar-cloud'
if dev.exists():
print(str(dev)); sys.exit()
print('NOT_FOUND')
" 2>/dev/null)
if [ -z "$PLUGIN_ROOT" ] || [ "$PLUGIN_ROOT" = "NOT_FOUND" ]; then echo "ERROR: jack-tar-cloud not found" && exit 1; fi
Parse $ARGUMENTS for:
Prompt: The quoted description
--output PATH: Where to save (default: output/google-YYYYMMDD-HHMMSS.png)
--aspect-ratio RATIO: Image aspect ratio (16:9, 1:1, 4:3, 9:16, 3:4). Default: 16:9. Imagen 4 honours this; Nano Banana ignores it (resolution alone determines output dimensions).
--resolution RES: Tier (1K, 2K, 4K). Default: 1K. Per-model support:
imagen-4.0-fast-generate-001: 1K onlyimagen-4.0-generate-001 / imagen-4.0-ultra-generate-001: 1K, 2Kgemini-3.1-flash-image: 512, 1K, 2K, 4Kgemini-3-pro-image: 1K, 2K, 4KUnsupported model/resolution combinations raise ProviderResolutionUnsupportedError; the exception message lists supported tiers so callers can retry intelligently.
--model MODEL: Specific Google model ID (overrides --tier)
--tier TIER: Shorthand for model selection (prices shown @1K; see Cost Reference for full tier ladder):
draft → imagen-4.0-fast-generate-001 ($0.020 @1K, no higher tiers)standard → gemini-3.1-flash-image ($0.067 @1K, $0.151 @4K) — defaultpremium → gemini-3-pro-image ($0.134 @1K, $0.240 @4K)If both --model and --tier are provided, --model takes precedence.
If neither is provided, defaults to standard tier (Nanobanana Flash).
PYTHONPATH="$PLUGIN_ROOT" python3 -c "
import os
configured = bool(os.environ.get('GOOGLE_API_KEY') or os.environ.get('GOOGLE_APPLICATION_CREDENTIALS'))
print('available' if configured else 'not_configured')
"
If not_configured, tell the user to set GOOGLE_API_KEY (Gemini Developer API) or GOOGLE_APPLICATION_CREDENTIALS (Vertex) and stop.
PYTHONPATH="$PLUGIN_ROOT" python3 -c "
import json
from src.generate_cloud_image import generate_cloud_image
result = generate_cloud_image(
prompt='$PROMPT',
provider='google',
output_path='$OUTPUT_PATH',
model='$MODEL',
aspect_ratio='$ASPECT_RATIO',
resolution='$RESOLUTION',
)
print(json.dumps(result, indent=2))
"
If successful, report the file path, model used, and cost. If failed, report the error.
| Model | 512 | 1K | 2K | 4K |
|---|---|---|---|---|
| imagen-4.0-fast-generate-001 (Vertex) | — | $0.020 | n/a | n/a |
| imagen-4.0-generate-001 (Vertex) | — | $0.040 | $0.040 | n/a |
| imagen-4.0-generate-001 (Dev API) | — | $0.040 | $0.101 | n/a |
| imagen-4.0-ultra-generate-001 (Vertex) | — | $0.060 | $0.060 | n/a |
| imagen-4.0-ultra-generate-001 (Dev API) | — | $0.060 | $0.101 | n/a |
| gemini-3.1-flash-image | $0.045 | $0.067 | $0.101 | $0.151 |
| gemini-3-pro-image | — | $0.134 | $0.134 | $0.240 |
Imagen pricing depends on backend: GOOGLE_APPLICATION_CREDENTIALS set → Vertex AI flat per-image; GOOGLE_API_KEY only → Gemini Developer API token-based.
Nano Banana (Flash and Pro) bills identically across both backends — no Vertex/Dev split — so its rows have no backend tag.