gpt-image-2-skill
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ChineseGPT Image 2 Skill
GPT Image 2 技能
Skill by ara.so — Daily 2026 Skills collection.
A prompt gallery, CLI, and agentic skill for OpenAI's model. Provides 162 curated prompts across categories (research figures, UI mockups, typography, photography, anime, maps, product shots), a full-featured CLI, and skill integrations for Claude Code, Codex, and other agent runtimes.
gpt-image-2由ara.so开发的技能 — 2026每日技能合集。
一款面向OpenAI 模型的提示词图库、CLI工具及Agent技能。提供162个分类精选提示词(涵盖科研图表、UI原型、排版设计、摄影、动漫、地图、产品实拍)、功能完整的CLI工具,以及可与Claude Code、Codex等Agent运行时集成的技能插件。
gpt-image-2Install
安装
CLI (fastest)
CLI(最快方式)
bash
undefinedbash
undefinedRun without installing
无需安装直接运行
uvx --from git+https://github.com/wuyoscar/gpt_image_2_skill gpt-image -p "a cat astronaut"
uvx --from git+https://github.com/wuyoscar/gpt_image_2_skill gpt-image -p "a cat astronaut"
Install to PATH permanently
永久安装至系统PATH
uv tool install git+https://github.com/wuyoscar/gpt_image_2_skill
gpt-image -p "a cat astronaut"
undefineduv tool install git+https://github.com/wuyoscar/gpt_image_2_skill
gpt-image -p "a cat astronaut"
undefinedClaude Code
Claude Code
text
/plugin marketplace add wuyoscar/gpt_image_2_skill
/plugin install gpt-image@wuyoscar-skillstext
/plugin marketplace add wuyoscar/gpt_image_2_skill
/plugin install gpt-image@wuyoscar-skillsCodex
Codex
text
$skill-installer install https://github.com/wuyoscar/gpt_image_2_skill/tree/main/skills/gpt-imagetext
$skill-installer install https://github.com/wuyoscar/gpt_image_2_skill/tree/main/skills/gpt-imageManual agent-skill install
手动安装Agent技能
bash
git clone https://github.com/wuyoscar/gpt_image_2_skill.git
cd gpt_image_2_skill
export AGENT_SKILLS_DIR="/path/to/your/agent/skills"
mkdir -p "$AGENT_SKILLS_DIR"
ln -s "$PWD/skills/gpt-image" "$AGENT_SKILLS_DIR/gpt-image"bash
git clone https://github.com/wuyoscar/gpt_image_2_skill.git
cd gpt_image_2_skill
export AGENT_SKILLS_DIR="/path/to/your/agent/skills"
mkdir -p "$AGENT_SKILLS_DIR"
ln -s "$PWD/skills/gpt-image" "$AGENT_SKILLS_DIR/gpt-image"Configuration
配置
The CLI and skill read your OpenAI key from the environment or :
~/.envbash
export OPENAI_API_KEY="sk-..."No other configuration is required.
CLI工具和技能会从环境变量或文件中读取你的OpenAI密钥:
~/.envbash
export OPENAI_API_KEY="sk-..."无需其他配置。
CLI Reference
CLI参考文档
Text → Image (generation)
文本→图像(生成)
bash
undefinedbash
undefinedBasic generation
基础生成
gpt-image -p "a photorealistic convenience store at 10pm"
gpt-image -p "a photorealistic convenience store at 10pm"
With size, quality, and explicit output file
指定尺寸、画质及输出文件
gpt-image -p "a neon-lit Tokyo alley at midnight"
--size portrait --quality high -f tokyo-alley.png
--size portrait --quality high -f tokyo-alley.png
gpt-image -p "a neon-lit Tokyo alley at midnight"
--size portrait --quality high -f tokyo-alley.png
--size portrait --quality high -f tokyo-alley.png
Square, low quality (cheap draft)
方形低画质(低成本草稿)
gpt-image -p "watercolor mountains at sunrise"
--size 1k --quality low -f draft.png
--size 1k --quality low -f draft.png
gpt-image -p "watercolor mountains at sunrise"
--size 1k --quality low -f draft.png
--size 1k --quality low -f draft.png
Batch: generates 4 variants, saved as out_0.png … out_3.png
批量生成:生成4个变体,保存为out_0.png … out_3.png
gpt-image -p "product shot of a ceramic mug on white"
--size square --quality medium -n 4 -f out.png
--size square --quality medium -n 4 -f out.png
undefinedgpt-image -p "product shot of a ceramic mug on white"
--size square --quality medium -n 4 -f out.png
--size square --quality medium -n 4 -f out.png
undefinedText + Reference Image → Image (edit / restyle)
文本+参考图→图像(编辑/重绘)
bash
undefinedbash
undefinedSingle reference restyle
单参考图重绘
gpt-image -p "Make it a winter evening with heavy snowfall"
-i chess.png --quality high -f chess-winter.png
-i chess.png --quality high -f chess-winter.png
gpt-image -p "Make it a winter evening with heavy snowfall"
-i chess.png --quality high -f chess-winter.png
-i chess.png --quality high -f chess-winter.png
Multi-reference composite: dog from image 2, scene from image 1
多参考图合成:图像2的狗,图像1的场景
gpt-image -p "Place the dog from image 2 next to the woman in image 1.
Match the same lighting, composition, and background."
-i woman.png -i dog.png --size portrait --quality medium -f woman-with-dog.png
Match the same lighting, composition, and background."
-i woman.png -i dog.png --size portrait --quality medium -f woman-with-dog.png
undefinedgpt-image -p "Place the dog from image 2 next to the woman in image 1.
Match the same lighting, composition, and background."
-i woman.png -i dog.png --size portrait --quality medium -f woman-with-dog.png
Match the same lighting, composition, and background."
-i woman.png -i dog.png --size portrait --quality medium -f woman-with-dog.png
undefinedMask-based Inpainting
基于蒙版的图像修复
bash
undefinedbash
undefinedopaque pixels = keep, transparent pixels = regenerate
不透明像素=保留,透明像素=重新生成
gpt-image -p "replace sky with aurora borealis"
-i photo.jpg -m sky_mask.png -f aurora.png
-i photo.jpg -m sky_mask.png -f aurora.png
undefinedgpt-image -p "replace sky with aurora borealis"
-i photo.jpg -m sky_mask.png -f aurora.png
-i photo.jpg -m sky_mask.png -f aurora.png
undefinedFull Parameter Reference
完整参数参考
| Flag | Values | Default | Notes |
|---|---|---|---|
| string | required | Full prompt text |
| path | auto-timestamped | Output file path |
| path (repeatable) | — | Triggers |
| path (PNG with alpha) | — | Requires |
| | | Literals must be 16-px multiples, max edge 3840 |
| | | Budget dial: |
| int | 1 | Batch count; suffixes files |
| | API default | |
| | | |
| | | Response encoding format |
| 0–100 | — | JPEG/WebP only |
Exit codes: success · API/refusal error · bad args or missing key
012| 参数标识 | 可选值 | 默认值 | 说明 |
|---|---|---|---|
| 字符串 | 必填 | 完整提示词文本 |
| 文件路径 | 自动生成带时间戳的 | 输出文件路径 |
| 文件路径(可重复) | — | 触发 |
| 文件路径(带透明通道的PNG) | — | 需配合 |
| | | 尺寸值需为16像素的倍数,最大边长3840像素 |
| | | 成本控制: |
| 整数 | 1 | 批量生成数量;文件名将添加 |
| | API默认值 | |
| | | |
| | | 响应编码格式 |
| 0–100 | — | 仅适用于JPEG/WebP格式 |
退出码: 成功 · API拒绝/错误 · 参数错误或密钥缺失
012Python SDK Usage
Python SDK 使用示例
Text → Image
文本→图像
python
from openai import OpenAI
client = OpenAI() # reads OPENAI_API_KEY from environment
result = client.images.generate(
model="gpt-image-2",
prompt="A photorealistic ceramic mug on a white studio background, "
"soft directional light, light shadow beneath",
size="1024x1024", # square
quality="high",
)python
from openai import OpenAI
client = OpenAI() # 从环境变量读取OPENAI_API_KEY
result = client.images.generate(
model="gpt-image-2",
prompt="A photorealistic ceramic mug on a white studio background, "
"soft directional light, light shadow beneath",
size="1024x1024", # 方形
quality="high",
)Save result
保存结果
import base64
from pathlib import Path
image_bytes = base64.b64decode(result.data[0].b64_json)
Path("mug.png").write_bytes(image_bytes)
print("Saved mug.png")
undefinedimport base64
from pathlib import Path
image_bytes = base64.b64decode(result.data[0].b64_json)
Path("mug.png").write_bytes(image_bytes)
print("Saved mug.png")
undefinedPortrait / Tall Generation
竖版/长图生成
python
result = client.images.generate(
model="gpt-image-2",
prompt="Minimalist event poster: 'Boston Spring Jazz Festival · April 2026' "
"in bold serif, pastel cherry-blossom watercolor background, centered layout",
size="1024x1536", # portrait (3:4)
quality="high",
)python
result = client.images.generate(
model="gpt-image-2",
prompt="Minimalist event poster: 'Boston Spring Jazz Festival · April 2026' "
"in bold serif, pastel cherry-blossom watercolor background, centered layout",
size="1024x1536", # 竖版(3:4)
quality="high",
)Image Edit (single reference)
图像编辑(单参考图)
python
result = client.images.edit(
model="gpt-image-2",
image=open("chess.png", "rb"),
prompt="Make it a winter evening with heavy snowfall, keep the chess pieces identical",
size="1024x1024",
quality="high",
)python
result = client.images.edit(
model="gpt-image-2",
image=open("chess.png", "rb"),
prompt="Make it a winter evening with heavy snowfall, keep the chess pieces identical",
size="1024x1024",
quality="high",
)Multi-Reference Edit
多参考图编辑
python
result = client.images.edit(
model="gpt-image-2",
image=[open("woman.png", "rb"), open("dog.png", "rb")],
prompt="Place the dog from image 2 next to the woman in image 1. "
"Match the same lighting, composition, and background. "
"Do not change anything else.",
size="1024x1536",
quality="medium",
)python
result = client.images.edit(
model="gpt-image-2",
image=[open("woman.png", "rb"), open("dog.png", "rb")],
prompt="Place the dog from image 2 next to the woman in image 1. "
"Match the same lighting, composition, and background. "
"Do not change anything else.",
size="1024x1536",
quality="medium",
)Mask-Based Inpainting
基于蒙版的图像修复
python
result = client.images.edit(
model="gpt-image-2",
image=open("photo.jpg", "rb"),
mask=open("sky_mask.png", "rb"), # transparent = regenerate
prompt="Replace the sky with dramatic aurora borealis, keep everything below the horizon identical",
size="1024x1024",
quality="high",
)python
result = client.images.edit(
model="gpt-image-2",
image=open("photo.jpg", "rb"),
mask=open("sky_mask.png", "rb"), # 透明区域=重新生成
prompt="Replace the sky with dramatic aurora borealis, keep everything below the horizon identical",
size="1024x1024",
quality="high",
)Batch Generation with Saving
批量生成并保存
python
import base64
from pathlib import Path
from openai import OpenAI
def generate_batch(prompt: str, n: int = 4, size: str = "1024x1024",
quality: str = "medium", out_prefix: str = "variant") -> list[Path]:
client = OpenAI()
result = client.images.generate(
model="gpt-image-2",
prompt=prompt,
size=size,
quality=quality,
n=n,
)
paths = []
for i, item in enumerate(result.data):
path = Path(f"{out_prefix}_{i}.png")
path.write_bytes(base64.b64decode(item.b64_json))
paths.append(path)
print(f"Saved {path}")
return pathspython
import base64
from pathlib import Path
from openai import OpenAI
def generate_batch(prompt: str, n: int = 4, size: str = "1024x1024",
quality: str = "medium", out_prefix: str = "variant") -> list[Path]:
client = OpenAI()
result = client.images.generate(
model="gpt-image-2",
prompt=prompt,
size=size,
quality=quality,
n=n,
)
paths = []
for i, item in enumerate(result.data):
path = Path(f"{out_prefix}_{i}.png")
path.write_bytes(base64.b64decode(item.b64_json))
paths.append(path)
print(f"Saved {path}")
return pathsUsage
使用示例
variants = generate_batch(
prompt="product shot of a blue glass water bottle, white background, studio lighting",
n=4,
quality="low", # cheap sweep; rerun winner at high
)
---variants = generate_batch(
prompt="product shot of a blue glass water bottle, white background, studio lighting",
n=4,
quality="low", # 低成本快速生成;选中最优后再用high画质重新生成
)
---Prompt Engineering Patterns
提示词设计范式
Structure template
结构模板
[background/scene] → [subject] → [key details] → [constraints/intended use][背景/场景] → [主体] → [关键细节] → [约束条件/使用场景]Research paper figure
科研论文图表
bash
gpt-image -p "Clean scientific diagram: transformer architecture overview. \
White background, labeled encoder/decoder blocks with arrows, \
color-coded attention heads in teal and orange, \
sans-serif labels, publication-ready, 4K resolution" \
--size landscape --quality high -f transformer-diagram.pngbash
gpt-image -p "Clean scientific diagram: transformer architecture overview. \
White background, labeled encoder/decoder blocks with arrows, \
color-coded attention heads in teal and orange, \
sans-serif labels, publication-ready, 4K resolution" \
--size landscape --quality high -f transformer-diagram.pngUI mockup
UI原型
bash
gpt-image -p "Mobile app UI mockup, iOS style, dark mode. \
Fitness tracking dashboard: circular progress ring in neon green, \
daily steps '8,432', heart rate '74 bpm', \
bottom nav with 4 icons, pixel-perfect, no lorem ipsum" \
--size portrait --quality high -f fitness-app.pngbash
gpt-image -p "Mobile app UI mockup, iOS style, dark mode. \
Fitness tracking dashboard: circular progress ring in neon green, \
daily steps '8,432', heart rate '74 bpm', \
bottom nav with 4 icons, pixel-perfect, no lorem ipsum" \
--size portrait --quality high -f fitness-app.pngTypography poster
排版海报
bash
gpt-image -p "Event poster. Text: 'SUMMER SONIC 2026' in bold condensed sans-serif. \
Subtext: 'Tokyo · August 9–10'. Vivid sunset gradient background (magenta to amber). \
Geometric grid overlay, high contrast, print-ready" \
--size portrait --quality high -f poster.pngbash
gpt-image -p "Event poster. Text: 'SUMMER SONIC 2026' in bold condensed sans-serif. \
Subtext: 'Tokyo · August 9–10'. Vivid sunset gradient background (magenta to amber). \
Geometric grid overlay, high contrast, print-ready" \
--size portrait --quality high -f poster.pngPhotorealistic product shot
写实产品实拍
bash
gpt-image -p "Photorealistic product photo: matte black insulated coffee thermos, \
condensation droplets, placed on dark slate surface, \
single soft key light from upper-left, shallow depth of field, \
shot on Canon 5D, 85mm lens, commercial quality" \
--size square --quality high -f thermos.pngbash
gpt-image -p "Photorealistic product photo: matte black insulated coffee thermos, \
condensation droplets, placed on dark slate surface, \
single soft key light from upper-left, shallow depth of field, \
shot on Canon 5D, 85mm lens, commercial quality" \
--size square --quality high -f thermos.pngPut required text in quotes
需精准呈现的文本加引号
python
undefinedpython
undefinedAny text that must appear verbatim in the image — put in straight quotes in the prompt
所有需要在图像中精准呈现的文本——在提示词中用直引号包裹
prompt = '''Storefront sign reading "OPEN 24/7" in red neon.
Below it: "Est. 1987" in smaller white block letters.
Realistic neon glow, night scene, rain-slicked pavement.'''
---prompt = '''Storefront sign reading "OPEN 24/7" in red neon.
Below it: "Est. 1987" in smaller white block letters.
Realistic neon glow, night scene, rain-slicked pavement.'''
---Quality / Budget Strategy
画质/成本策略
| Stage | | When to use |
|---|---|---|
| Exploration sweep | | Generating 8–16 variants to find direction |
| Normal iteration | | Style probing, layout checks |
| Final / shipping | | In-image text, dense diagrams, posters, paper figures |
Rule of thumb: start every new concept at , run 4 variants, pick the best, then rerun at .
lowhighbash
undefined| 阶段 | | 使用场景 |
|---|---|---|
| 探索阶段 | | 生成8–16个变体以确定创作方向 |
| 常规迭代 | | 风格探索、布局校验 |
| 最终交付 | | 含文本的图像、密集图表、海报、科研论文配图 |
经验法则: 所有新创意先以画质生成4个变体,选中最优后再用画质重新生成。
lowhighbash
undefinedStep 1: cheap sweep
第一步:低成本快速探索
gpt-image -p "minimalist logo for a coffee brand" --quality low -n 4 -f logo.png
gpt-image -p "minimalist logo for a coffee brand" --quality low -n 4 -f logo.png
Step 2: pick winner (e.g. logo_2.png), rerun at high
第二步:选中最优版本(如logo_2.png),用high画质重新生成最终版
gpt-image -p "minimalist logo for a coffee brand" --quality high -f logo-final.png
---gpt-image -p "minimalist logo for a coffee brand" --quality high -f logo-final.png
---Size Reference
尺寸参考
| Alias | Pixels | Ratio | Best for |
|---|---|---|---|
| 1024×1024 | 1:1 | Social posts, icons, product shots |
| 1024×1536 | 2:3 | Mobile UI, posters, stories |
| 1536×1024 | 3:2 | Web banners, diagrams |
| 1792×1024 | 7:4 | Cinematic, hero sections |
| 1024×1792 | 4:7 | Long-form mobile content |
| 2048×2048 | 1:1 | High-res assets |
| 别名 | 像素尺寸 | 比例 | 适用场景 |
|---|---|---|---|
| 1024×1024 | 1:1 | 社交帖子、图标、产品实拍 |
| 1024×1536 | 2:3 | 移动端UI、海报、故事类内容 |
| 1536×1024 | 3:2 | web横幅、图表 |
| 1792×1024 | 7:4 | 电影级画面、首页 hero 区域 |
| 1024×1792 | 4:7 | 移动端长内容 |
| 2048×2048 | 1:1 | 高分辨率素材 |
Common Patterns & Recipes
常见场景与示例
Virtual try-on (multi-ref edit)
虚拟试穿(多参考图编辑)
python
undefinedpython
undefinedimage 1 = person, image 2 = garment
图像1=人物,图像2=服装
result = client.images.edit(
model="gpt-image-2",
image=[open("person.png", "rb"), open("shirt.png", "rb")],
prompt="Dress the person in image 1 wearing the shirt from image 2. "
"Keep the person's face, pose, and background identical. "
"Natural fabric draping and lighting.",
size="1024x1536",
quality="high",
)
undefinedresult = client.images.edit(
model="gpt-image-2",
image=[open("person.png", "rb"), open("shirt.png", "rb")],
prompt="Dress the person in image 1 wearing the shirt from image 2. "
"Keep the person's face, pose, and background identical. "
"Natural fabric draping and lighting.",
size="1024x1536",
quality="high",
)
undefinedBillboard / signage mockup
广告牌/标识原型
python
result = client.images.edit(
model="gpt-image-2",
image=open("billboard_photo.jpg", "rb"),
mask=open("billboard_mask.png", "rb"),
prompt='Replace the billboard face with: "SALE ENDS SUNDAY" '
'in bold white text on solid red background. '
'Match perspective and lighting of surrounding scene.',
size="1536x1024",
quality="high",
)python
result = client.images.edit(
model="gpt-image-2",
image=open("billboard_photo.jpg", "rb"),
mask=open("billboard_mask.png", "rb"),
prompt='Replace the billboard face with: "SALE ENDS SUNDAY" '
'in bold white text on solid red background. '
'Match perspective and lighting of surrounding scene.',
size="1536x1024",
quality="high",
)Anime / manga style transfer
动漫/漫画风格转换
bash
gpt-image -p "Anime key visual style (Studio Ghibli-inspired): \
young woman standing on a hillside overlooking a coastal town at golden hour, \
painterly backgrounds, soft cel shading, \
detailed environmental storytelling, cinematic composition" \
--size landscape --quality high -f anime-scene.pngbash
gpt-image -p "Anime key visual style (Studio Ghibli-inspired): \
young woman standing on a hillside overlooking a coastal town at golden hour, \
painterly backgrounds, soft cel shading, \
detailed environmental storytelling, cinematic composition" \
--size landscape --quality high -f anime-scene.pngTranslation / text replacement edit
翻译/文本替换编辑
python
undefinedpython
undefinedReplace text in an existing image in a different language
将现有图像中的文本替换为其他语言
result = client.images.edit(
model="gpt-image-2",
image=open("menu_english.png", "rb"),
prompt='Replace all English text with Japanese translations. '
'Keep the exact same layout, fonts, colors, and imagery. '
'Translate "Grilled Salmon" → "グリルサーモン", '
'"Caesar Salad" → "シーザーサラダ".',
size="1024x1024",
quality="high",
)
---result = client.images.edit(
model="gpt-image-2",
image=open("menu_english.png", "rb"),
prompt='Replace all English text with Japanese translations. '
'Keep the exact same layout, fonts, colors, and imagery. '
'Translate "Grilled Salmon" → "グリルサーモン", '
'"Caesar Salad" → "シーザーサラダ".',
size="1024x1024",
quality="high",
)
---Troubleshooting
故障排查
OPENAI_API_KEY
not found
OPENAI_API_KEY未找到OPENAI_API_KEY
OPENAI_API_KEYbash
export OPENAI_API_KEY="sk-..."bash
export OPENAI_API_KEY="sk-..."or add to ~/.env — the CLI reads it automatically
或添加至~/.env文件——CLI会自动读取
undefinedundefinedRefusal / content policy error (exit code 1)
内容政策拒绝错误(退出码1)
- Full API response is echoed to stderr
- Try rephrasing: be more descriptive and less ambiguous about intent
- Switch →
--moderation autofor broader exploration (already the CLI default)--moderation low
- API完整响应会输出至stderr
- 尝试重新表述提示词:更清晰地描述创作意图,减少歧义
- 将改为
--moderation auto以支持更广泛的创作探索(CLI默认已为该值)--moderation low
Text in image is garbled or wrong
图像中文本模糊或错误
- Always use for any prompt containing required text
--quality high - Wrap required text in straight quotes inside the prompt string
- Keep required text short (under ~6 words per element)
- 任何包含需精准呈现文本的提示词,务必使用
--quality high - 在提示词中用直引号包裹需精准呈现的文本
- 需呈现的文本尽量简短(每个元素不超过约6个单词)
Multi-ref edit ignores one image
多参考图编辑忽略其中一张图
- Be explicit: "the subject in image 1", "the object in image 2"
- Reduce to two input images; more than two can cause ambiguity
- 明确指定:“图像1中的主体”、“图像2中的物体”
- 最多使用两张输入图;超过两张可能导致歧义
Size rejected by API
API拒绝尺寸参数
- Dimensions must be multiples of 16
- Max edge: 3840px
- Max aspect ratio: 3:1
- Total pixels: 655,360–8,294,400
- 尺寸必须为16像素的倍数
- 最大边长:3840像素
- 最大宽高比:3:1
- 总像素数:655,360–8,294,400
gpt-image-2
rejects --input-fidelity
gpt-image-2--input-fidelitygpt-image-2
拒绝--input-fidelity
参数
gpt-image-2--input-fidelity- is a
input-fidelity/gpt-image-1parameter; the CLI drops it automatically for1.5gpt-image-2
- 是
input-fidelity/gpt-image-1的参数;CLI会自动为1.5忽略该参数gpt-image-2
Slow generation at high
quality
highhigh
画质生成速度慢
highExpected — quality is significantly slower. Use for drafts, only for finals.
highlowhigh这是正常现象——画质生成速度明显较慢。用画质生成草稿,仅在最终交付时使用画质。
highlowhighPrompt Gallery Categories
提示词图库分类
The skill ships 162 prompts split across category files under :
skills/gpt-image/references/- — diagrams, charts, architecture visuals
gallery-research-paper-figures.md - — mobile/web UI, dashboards, design systems
gallery-ui-ux-mockups.md - — product shots, food styling, e-commerce
gallery-product-and-food.md - — posters, signage, lettering
gallery-typography.md - — portrait, landscape, macro, street
gallery-photography.md - — key visuals, character design, backgrounds
gallery-anime-manga.md - — illustrated maps, infographic cartography
gallery-maps.md - Start with as a routing index to pick the right category file
gallery.md
本技能内置162个提示词,按类别存放在目录下:
skills/gpt-image/references/- — 图表、示意图、架构可视化
gallery-research-paper-figures.md - — 移动端/web端UI、仪表盘、设计系统
gallery-ui-ux-mockups.md - — 产品实拍、美食摄影、电商素材
gallery-product-and-food.md - — 海报、标识、字体设计
gallery-typography.md - — 人像、风景、微距、街拍
gallery-photography.md - — 关键视觉图、角色设计、背景
gallery-anime-manga.md - — 插画地图、信息图式制图
gallery-maps.md - 可先查看作为索引,选择合适的分类文件
gallery.md