Png Compress

Q: Why use Png Compress on TypingMind?

Because you install it once and use it with any model. Png Compress is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

Q: How do I install Png Compress in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Job-Yang/jobyang-ai-skills/tree/main/skills/png-compress-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Q: Which AI models can use Png Compress?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

Q: How many AI models can I use with Png Compress?

As many as you like. As long as a model supports skills, you can use Png Compress with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Q: Is the Png Compress AI skill free?

Yes. It is published on GitHub by Job-Yang under the MIT license. You only pay your own AI provider for the tokens you use.

Q: What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

Q: How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Community

Job-Yang

png-compress

App 工程 PNG 批量压缩。用 pngquant 量化、oxipng 无损收尾，并通过内容缓存和质量门禁避免重复压缩与明显劣化。给出工程或图片目录、要求包体积优化或批量压 PNG 时使用。

Overview

Publisher	Job-Yang
Repository	`jobyang-ai-skills`
Skill name	`png-compress`
Stars	100
Forks	5
Bundled files	4
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Job-Yang on GitHub. Read the source before you install it.

Installation

Install the Png Compress AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Job-Yang/jobyang-ai-skills/tree/main/skills/png-compress-skill
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/Job-Yang/jobyang-ai-skills.git /tmp/jobyang-ai-skills
mkdir -p .claude/skills
cp -r /tmp/jobyang-ai-skills/skills/png-compress-skill .claude/skills/png-compress

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Png Compress in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Png Compress on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Png Compress is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PNG 批量压缩

这个技能能做什么

给它一个 App 工程或普通图片目录,它会:

自动递归找出所有 PNG(自动跳过 Pods / Carthage / build / DerivedData 等目录)
自动判断压没压过——用文件内容 hash 存本地缓存,压过的直接跳过,绝不二次量化毁图
自动压缩新增/改动的图,增量持续可用(缓存可提交进仓库,团队/CI 共享)
小图友好——20K 以下 icon 用更保守的质量下限,避免糊
变大回退——压完比原图还大就丢弃,保留原图(icon 常见)
压后自检——默认校验可解码/宽高不变/alpha 不丢,再用 PSNR/SSIM/MAE 做自动质量门禁;不通过就回退原图
动图保护——检测到 APNG 直接保留原图,不把动画误压成单帧
原地覆盖,不依赖具体 App、Target 或构建系统;多进程跑满 CPU

内核 = pngquant(有损量化,24/32bit → 8bit 调色板)+ oxipng(无损重编码和元数据清理)。这条路线与 TinyPNG 官方公开的“颜色量化 + 元数据清理”原理一致,但不声称复刻其未公开实现。

适用范围

不读取 .xcodeproj、Scheme、Bundle ID 或业务源码,不依赖任何特定业务工程或私有构建设施。
只要目录里有 PNG 就能运行;默认排除依赖目录和构建产物。
目标是 iOS App 源码资源,同样可以用于其他 App 或独立图片目录。
只处理静态 PNG;检测到 APNG 会保留原图。

怎么用

最简单:一键跑当前目录

bash
./compress.sh

不传目录 = 默认当前目录递归。依赖(pngquant / oxipng)缺失时会自动尝试用 brew 安装。

指定工程目录

bash
./compress.sh /path/to/iOSProject

只看效果不改文件(先预估收益)

bash
./compress.sh /path/to/iOSProject --dry-run

指定并行数

bash
./compress.sh /path/to/iOSProject -j 8

输出长啥样


扫描到 PNG: 1234 张 | 缓存已记录: 0 张 | 并行: 8
自检门禁: hard=on | quality=on | ssim=on
===== 完成 =====
压缩: 900  跳过(缓存): 0  保留原图(压不动): 330  跳过(APNG): 4  硬门禁回退: 0  质量门禁回退: 2  失败: 0
本轮压缩前: 45.2MB  压缩后: 12.8MB  节省: 32.4MB (71.7%)
耗时: 38.5s

再跑第二次,压过的全部走"跳过(缓存)",秒回。

实现细节(感兴趣再看)

脚本本体是 compress_images.py,compress.sh 只是加了依赖自检 + 自动安装的一层壳,两个交付形态(纯脚本 / 技能)复用同一份 compress_images.py。

核心命令(每张图):

bash
# ① 有损量化,达不到质量或变大就放弃(退出码 98/99 视为正常)
pngquant --quality 65-85 --speed 1 --floyd=1.0 --strip --skip-if-larger \
         --force --output out.png  input.png
# ② 无损收尾:榨 DEFLATE + 删所有元数据
oxipng -o max --strip all -q out.png

压后自检(默认开启):

硬门禁: 用 Pillow(没有则退到 macOS sips)解码原图和候选图,要求候选图可解码、宽高不变、原图有 alpha 时候选图不能丢 alpha、动画帧数不能变化。
质量门禁: 用 Pillow 计算 before/after 像素差异。默认阈值是 PSNR >= 28、SSIM >= 0.95(有 numpy 时启用)、MAE <= 12、alpha MAE <= 3。透明图会分别垫白底/黑底对比,避免只看透明像素导致误判。
动图保护: APNG 不进入有损量化,直接保留并写入缓存。
回退策略: 任何门禁不通过都保留原图,并把原图 hash 记入缓存,下次不再重复尝试。输出会打印 [HARD-GATE] 或 [QUALITY-GATE] 的文件清单。

这套自动门禁只负责拦“坏图/尺寸变了/alpha 丢了/明显灾难性劣化”。“到底糊不糊”仍然有主观性,建议每轮再按体积下降最大、压缩比例最高、AppIcon/launchscreen、透明 icon 等维度做肉眼抽检。

可调参数(在 compress_images.py 顶部):

参数	默认值	说明
`QUALITY_NORMAL`	`65-85`	普通图质量区间,上限对齐 TinyPNG 观感
`QUALITY_SMALL`	`70-90`	小图/icon 区间,下限抬高防糊
`SMALL_THRESHOLD`	`20*1024`	20KB 以下算小图
`DITHER`	`1.0`	抖动强度,TinyPNG 默认开启
`OXIPNG_LEVEL`	`max`	无损级别,嫌慢可改 `4`
`CACHE_NAME`	`.png_compress_cache.json`	缓存文件,建议提交进仓库
`QUALITY_MIN_PSNR`	`28.0`	自动质量门禁最低 PSNR
`QUALITY_MIN_SSIM`	`0.95`	自动质量门禁最低 SSIM(有 numpy 时启用)
`QUALITY_MAX_MAE`	`12.0`	自动质量门禁最大平均绝对误差
`QUALITY_ALPHA_MAX_MAE`	`3.0`	透明通道最大平均绝对误差

Python 直接调用:

bash
python3 compress_images.py                       # 当前目录递归
python3 compress_images.py /path/to/iOSProject   # 指定目录
python3 compress_images.py /path/to/proj --dry-run
python3 compress_images.py /path/to/proj -j 8
python3 compress_images.py /path/to/proj --no-quality-gate       # 关闭质量门禁(不建议)
python3 compress_images.py /path/to/proj --quality-min-ssim 0.98  # 调高质量阈值

依赖手动安装:

macOS:brew install pngquant oxipng
Ubuntu:sudo apt-get install pngquant + cargo install oxipng(或下 GitHub release 二进制)
自检: 硬门禁可用 macOS 自带 sips;质量门禁默认开启且需要 Python Pillow,有 numpy 时会额外启用 SSIM 指标。缺 Pillow 时脚本会报错,除非显式加 --no-quality-gate。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Png Compress AI skill do?

Why use Png Compress on TypingMind?

Because you install it once and use it with any model. Png Compress is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Png Compress in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Job-Yang/jobyang-ai-skills/tree/main/skills/png-compress-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Png Compress?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Png Compress?

As many as you like. As long as a model supports skills, you can use Png Compress with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Png Compress AI skill free?

Yes. It is published on GitHub by Job-Yang under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

PNG 批量压缩

这个技能能做什么

适用范围

怎么用

最简单:一键跑当前目录

指定工程目录

只看效果不改文件(先预估收益)

指定并行数

输出长啥样

实现细节(感兴趣再看)

Bundled files

Frequently asked questions

Related AI Skills

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