harry0703 / MoneyPrinterTurbo

利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.

Python ⭐ 65,441 🍴 9,493 ⭐ 4,685 stars today

📖 README

MoneyPrinterTurbo 💸

简体中文 | English

只需提供一个视频主题或 关键词 ，就可以全自动生成视频文案、视频素材、视频字幕、视频背景音乐，然后合成一个高清的短视频。

Web界面

![](docs/webui.jpg)

API界面

![](docs/api.jpg)

功能特性 🎯

[x] 完整的 MVC架构，代码 结构清晰，易于维护，支持 API 和 Web界面
[x] 支持视频文案 AI自动生成，也可以自定义文案
[x] 支持多种 高清视频 尺寸
- [x] 竖屏 9:16，1080x1920
- [x] 横屏 16:9，1920x1080
[x] 支持 批量视频生成，可以一次生成多个视频，然后选择一个最满意的
[x] 支持 视频片段时长 设置，方便调节素材切换频率
[x] 支持中文和英文视频文案
[x] 支持 多种语音 合成，可 实时试听 效果
[x] 支持 字幕生成，可以调整 字体、位置、颜色、大小，同时支持字幕描边设置
[x] 支持 背景音乐，随机或者指定音乐文件，可设置背景音乐音量
[x] 视频素材来源高清，而且 无版权，也可以使用自己的 本地素材
[x] 支持 OpenAI、Moonshot、Azure、gpt4free、one-api、通义千问、Google Gemini、Ollama、DeepSeek、MiniMax、 文心一言, Pollinations、ModelScope 等多种模型接入
- 中国用户建议使用 DeepSeek 或 Moonshot 作为大模型提供商（国内可直接访问，不需要VPN。注册就送额度，基本够用）

视频演示 📺

竖屏 9:16

▶️ 《如何增加生活的乐趣》	▶️ 《金钱的作用》更真实的合成声音	▶️ 《生命的意义是什么》

横屏 16:9

▶️《生命的意义是什么》	▶️《为什么要运动》

配置要求 📦

建议系统：Windows 10 或 MacOS 11.0 以上，或主流 Linux 发行版
GPU 不是必需项，但如果你希望本地转录、更快的视频处理或更顺畅的批量生成体验，建议使用带显存的独立显卡

项目	最低配置	推荐配置	理想配置
CPU	4 核	6 到 8 核	8 核及以上
RAM	4 GB	8 GB	16 GB 及以上
GPU	非必须	4 GB 显存及以上	8 GB 显存及以上

如果你主要依赖云端 LLM、云端 TTS 和在线素材源，CPU 与内存比 GPU 更重要
如果你启用 faster-whisper、批量生成或更重的本地处理链路，GPU 会明显提升速度

快速开始 🚀

在 Google Colab 中运行

免去本地环境配置，点击直接在 Google Colab 中快速体验 MoneyPrinterTurbo

Windows一键启动包

下载一键启动包，解压直接使用（路径不要有中文、特殊字符、空格）
当前提供的安装包仍是 v1.2.6 的旧打包版本，建议下载后先执行 update.bat 更新到最新代码。

百度网盘（v1.2.6）: https://pan.baidu.com/s/1wg0UaIyXpO3SqIpaq790SQ?pwd=sbqx 提取码: sbqx
Google Drive (v1.2.6): https://drive.google.com/file/d/1HsbzfT7XunkrCrHw5ncUjFX8XX4zAuUh/view?usp=sharing

下载后，建议先双击执行 update.bat 更新到最新代码，然后双击 start.bat 启动

启动后，会自动打开浏览器（如果打开是空白，建议换成 Chrome 或者 Edge 打开）

安装部署 📥

前提条件

尽量不要使用 中文路径，避免出现一些无法预料的问题
请确保你的网络是正常的，VPN需要打开全局流量模式

① 克隆代码

git clone https://github.com/harry0703/MoneyPrinterTurbo.git

② 修改配置文件（可选，建议启动后也可以在 WebUI 里面配置）

将 config.example.toml 文件复制一份，命名为 config.toml
按照 config.toml 文件中的说明，配置好 pexels_api_keys 和 llm_provider，并根据 llm_provider 对应的服务商，配置相关的
API Key

Docker部署 🐳

① 启动Docker

如果未安装 Docker，请先安装 https://www.docker.com/products/docker-desktop/

如果是Windows系统，请参考微软的文档：

https://learn.microsoft.com/zh-cn/windows/wsl/install
https://learn.microsoft.com/zh-cn/windows/wsl/tutorials/wsl-containers

cd MoneyPrinterTurbo
docker-compose up

注意：最新版的docker安装时会自动以插件的形式安装docker compose，启动命令调整为docker compose up

② 访问Web界面

打开浏览器，访问 http://0.0.0.0:8501

③ 访问API文档

打开浏览器，访问 http://0.0.0.0:8080/docs 或者 http://0.0.0.0:8080/redoc

手动部署 📦

视频教程

完整的使用演示：https://v.douyin.com/iFhnwsKY/
如何在Windows上部署：https://v.douyin.com/iFyjoW3M

① 创建虚拟环境

推荐使用 uv 管理 Python 环境和依赖，默认使用 Python 3.11

git clone https://github.com/harry0703/MoneyPrinterTurbo.git
cd MoneyPrinterTurbo
uv python install 3.11
uv sync --frozen

如果你暂时不使用 uv，也可以继续使用 venv + pip

python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

说明：
- pyproject.toml 是主依赖定义文件
- uv.lock 是锁文件，建议默认执行 uv sync --frozen
- requirements.txt 仅保留给旧的 pip 安装方式兼容使用

② 安装好 ImageMagick

Windows:
- 下载 https://imagemagick.org/script/download.php 选择Windows版本，切记一定要选择 静态库 版本，比如
  ImageMagick-7.1.1-32-Q16-x64-static.exe
- 安装下载好的 ImageMagick，注意不要修改安装路径
- 修改 配置文件 config.toml 中的 imagemagick_path 为你的 实际安装路径
MacOS:
```shell
brew install imagemagick
````
Ubuntu
shell sudo apt-get install imagemagick
CentOS
shell sudo yum install ImageMagick

③ 启动Web界面 🌐

注意需要到 MoneyPrinterTurbo 项目 根目录 下执行以下命令

Windows

uv run streamlit run ./webui/Main.py --browser.gatherUsageStats=False

如果你已经手动激活了虚拟环境，也可以直接执行：

webui.bat

MacOS or Linux

uv run streamlit run ./webui/Main.py --browser.gatherUsageStats=False

如果你已经手动激活了虚拟环境，也可以直接执行：

sh webui.sh

启动后，会自动打开浏览器（如果打开是空白，建议换成 Chrome 或者 Edge 打开）

④ 启动API服务 🚀

uv run python main.py

如果你已经手动激活了虚拟环境，也可以直接执行：

python main.py

特别感谢 🙏

由于该项目的部署和使用，对于一些小白用户来说，还是 有一定的门槛，在此特别感谢
录咖（AI智能多媒体服务平台） 网站基于该项目，提供的免费AI视频生成器服务，可以不用部署，直接在线使用，非常方便。

中文版：https://reccloud.cn
英文版：https://reccloud.com

感谢赞助 🙏

感谢佐糖 https://picwish.cn 对该项目的支持和赞助，使得该项目能够持续的更新和维护。

佐糖专注于图像处理领域，提供丰富的图像处理工具，将复杂操作极致简化，真正实现让图像处理更简单。

启动后，可以查看 API文档 http://127.0.0.1:8080/docs 或者 http://127.0.0.1:8080/redoc 直接在线调试接口，快速体验。

语音合成 🗣

所有支持的声音列表，可以查看：声音列表

2024-04-16 v1.1.2 新增了9种Azure的语音合成声音，需要配置API KEY，该声音合成的更加真实。

字幕生成 📜

当前支持2种字幕生成方式：

edge: 生成速度快，性能更好，对电脑配置没有要求，但是质量可能不稳定
whisper: 生成速度慢，性能较差，对电脑配置有一定要求，但是质量更可靠。

可以修改 config.toml 配置文件中的 subtitle_provider 进行切换

建议使用 edge 模式，如果生成的字幕质量不好，再切换到 whisper 模式

注意：

whisper 模式下需要到 HuggingFace 下载一个模型文件，大约 3GB 左右，请确保网络通畅
如果留空，表示不生成字幕。

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affaan-m / ECC

The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

JavaScript ⭐ 196,900 🍴 30,281 ⭐ 1,388 stars today

📖 README

ECC

ECC - the harness-native operator system for agentic work

182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Cross-harness agent workflows

The harness-native operator system for agentic work. Built from real-world multi-harness engineering workflows.

Not just configs. A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development. Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.

Works across Codex, Claude Code, Cursor, OpenCode, Gemini, Zed, GitHub Copilot, and other AI agent harnesses.

ECC v2.0.0-rc.1 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the rc.1 release notes and cross-harness architecture.

ECC Pro
_{Private repos · GitHub App · $19/seat/mo}

Sponsor
_{Fund the OSS · From $5/mo}

Community
_{Discussions · Q&A · Show & Tell}

GitHub App
_{Install · PR audits · Free tier}

_{OSS stays free. This repo is MIT-licensed forever. ECC Pro is the hosted GitHub App for private repos. Sponsors and Pro subscribers fund the work — that's why a single maintainer ships weekly across 7 harnesses.}

The Guides

This repo is the raw code only. The guides explain everything.


Shorthand Guide Setup, foundations, philosophy. Read this first.	Longform Guide Token optimization, memory persistence, evals, parallelization.	Security Guide Attack vectors, sandboxing, sanitization, CVEs, AgentShield.

Topic	What You'll Learn
Token Optimization	Model selection, system prompt slimming, background processes
Memory Persistence	Hooks that save/load context across sessions automatically
Continuous Learning	Auto-extract patterns from sessions into reusable skills
Verification Loops	Checkpoint vs continuous evals, grader types, pass@k metrics
Parallelization	Git worktrees, cascade method, when to scale instances
Subagent Orchestration	The context problem, iterative retrieval pattern

What's New

v2.0.0-rc.1 — Surface Refresh, Operator Workflows, and ECC 2.0 Alpha (Apr 2026)

Dashboard GUI — New Tkinter-based desktop application (ecc_dashboard.py or npm run dashboard) with dark/light theme toggle, font customization, and project logo in header and taskbar.
Public surface synced to the live repo — metadata, catalog counts, plugin manifests, and install-facing docs now match the actual OSS surface: 63 agents, 249 skills, and 79 legacy command shims.
Operator and outbound workflow expansion — brand-voice, social-graph-ranker, connections-optimizer, customer-billing-ops, ecc-tools-cost-audit, google-workspace-ops, project-flow-ops, and workspace-surface-audit round out the operator lane.
Media and launch tooling — manim-video, remotion-video-creation, and upgraded social publishing surfaces make technical explainers and launch content part of the same system.
Framework and product surface growth — nestjs-patterns, richer Codex/OpenCode install surfaces, and expanded cross-harness packaging keep the repo usable beyond Claude Code alone.
Itô prediction-market skill pack — ito-market-intelligence, ito-basket-compare, ito-trade-planner, ito-data-atlas-agent, prediction-market-oracle-research, and prediction-market-risk-review add public, non-advisory market/basket workflows while keeping live Itô API access gated and separate from ECC Tools billing.
Optimization skill pack — parallel-execution-optimizer, benchmark-optimization-loop, data-throughput-accelerator, latency-critical-systems, and recursive-decision-ledger turn repeated speed/recursion prompts into bounded benchmark, throughput, and decision-ledger workflows.
ECC 2.0 alpha is in-tree — the Rust control-plane prototype in ecc2/ now builds locally and exposes dashboard, start, sessions, status, stop, resume, and daemon commands. It is usable as a

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Leonxlnx / taste-skill

Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop

Shell ⭐ 25,745 🍴 1,946 ⭐ 2,235 stars today

📖 README

Taste Skill

The Anti-Slop Frontend Framework for AI Agents

Portable Agent Skills that upgrade AI-built interfaces: stronger layout, typography, motion, and spacing instead of boilerplate-looking UIs. This repo also includes image-generation skills for reference boards (web, mobile, brand kits). Pair them with ChatGPT Images or similar generators, then hand the frames to Codex, Cursor, or Claude Code for implementation.

Disclaimer

Taste Skill has no official token, coin, or crypto project. Any token using my name, image, or project is unaffiliated and not endorsed by me.

_{Disclaimer · Install · Skills · Settings · Examples · Sponsor · Research · FAQ · License}

Feedback & Contributions

We would love your feedback. Suggestions and bug reports:

Open a Pull Request or Issue on GitHub
DM @lexnlin or @blueemi99
Email us at hello@tasteskill.dev

Installing

The npx skills add CLI scans the skills/ folder in this repo, so all skills below (code and image-generation) install the same way.

npx skills add https://github.com/Leonxlnx/taste-skill

Install a single skill by its install name (the name: field inside the SKILL frontmatter, not the folder name):

npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend"

You can also copy any SKILL.md into your project or paste it into ChatGPT / Codex conversations.

Updating from the previous version

The default taste-skill (install name design-taste-frontend) is now v2 (experimental), a substantial rewrite of the original v1. If you already have v1 installed, just re-run the install command and you will be upgraded:

npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend"

The install name did not change, so no script updates are needed. The newer SKILL.md replaces the older one in place.

If you depend on the exact behavior of v1 and want to pin to it explicitly:

npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend-v1"

See CHANGELOG.md for the full v1 to v2 diff and the rationale.

Skills

Each skill does one job; you do not need all of them at once. Implementation skills output code. Image-generation skills output reference images only.

The Install name column is the exact value you pass to --skill.

Skill (folder)	Install name	Description
taste-skill	`design-taste-frontend`	🆕 v2 (experimental) - substantial rewrite of the default skill. Reads the brief, infers the design language, tunes three dials (VARIANCE / MOTION / DENSITY). Brief inference, design-system map, hard em-dash ban, canonical GSAP code skeletons, redesign-audit protocol, strict pre-flight check. Actively iterating toward v2.0.0 stable.
taste-skill-v1	`design-taste-frontend-v1`	The original v1 of taste-skill, preserved for projects depending on its exact behavior. Use only if the v2 default breaks something specific in your workflow.
gpt-tasteskill	`gpt-taste`	Stricter variant for GPT/Codex: higher layout variance, stronger GSAP direction, aggressive anti-slop.
image-to-code-skill	`image-to-code`	Image-first pipeline: generate site references, analyze them, then implement the frontend to match.
redesign-skill	`redesign-existing-projects`	Existing projects: audit the UI first, then fix layout, spacing, hierarchy, styling.
soft-skill	`high-end-visual-design`	Polished, calm, expensive UI with softer contrast, whitespace, premium fonts, spring motion.
output-skill	`full-output-enforcement`	When the model ships half-finished work: full output, no placeholder comments.
minimalist-skill	`minimalist-ui`	Editorial product UI (Notion/Linear vibes), restrained palette, crisp structure.
brutalist-skill	`industrial-brutalist-ui`	Hard mechanical language: Swiss type, sharp contrast, experimental layout.
stitch-skill	`stitch-design-taste`	Google Stitch-compatible rules, including optional `DESIGN.md` export format.

Image generation skills

These produce design images only (no code). Use with ChatGPT Images, Codex image mode, or any agent that generates images.

Skill (folder)	Install name	Description
imagegen-frontend-web	`imagegen-frontend-web`	Website comps: hero, landing, multi-section with strong typography, spacing, anti-slop art direction.
imagegen-frontend-mobile	`imagegen-frontend-mobile`	Mobile screens and flows: iOS/Android/cross-platform, mockups, readable type, coherent sets.
brandkit	`brandkit`	Brand-kit boards: logo directions, palettes, type, identity applications across categories.

Which one should I use?

Start with taste-skill for the safest general default. (Now v2 experimental - see what changed in the CHANGELOG.)
If you depend on the exact behavior of the original taste-skill, install taste-skill-v1 instead.
Use gpt-taste when you want the stricter GPT/Codex-oriented rules and motion/layout enforcement.
Use image-to-code-skill for image → analyze → code website workflows.
Use redesign-skill to improve an existing codebase instead of greenfield styling.
Add soft-skill, minimalist-skill, or brutalist-skill when the visual direction is already chosen.
Add output-skill if the agent keeps truncating output.
Use imagegen-frontend-web, imagegen-frontend-mobile, or brandkit when the deliverable is images (comps, flows, identity boards), then pass results to your coding agent.

Image-first tip

For image-to-code-skill, state the pipeline in the prompt, e.g.: follow the skill: generate images, then analyze, then code.

ChatGPT Images and Codex

Attach or paste imagegen-frontend-web, imagegen-frontend-mobile, or brandkit and ask for the frames you need, then feed the renders to Codex, Cursor, or Claude Code. Use image-to-code-skill when you want one workflow that both generates references and implements the site in code.

Settings (taste-skill o

(Preview — first 8 000 chars. View full README ↗)

hardikpandya / stop-slop

A skill file for removing AI tells from prose

⭐ 6,183 🍴 454 ⭐ 755 stars today

📖 README

Stop Slop

A skill for removing AI tells from prose.

G-Yg4RVbIAAhVxW

What this is

AI writing has patterns. Predictable phrases, structures, rhythms. This skill teaches Claude (or any LLM) to catch and remove them.

Skill Structure

stop-slop/
├── SKILL.md              # Core instructions
├── references/
│   ├── phrases.md        # Phrases to remove
│   ├── structures.md     # Structural patterns to avoid
│   └── examples.md       # Before/after transformations
├── README.md
└── LICENSE

Quick start

Claude Code: Add this folder as a skill.

Claude Projects: Upload SKILL.md and reference files to project knowledge.

Custom instructions: Copy core rules from SKILL.md.

API calls: Include SKILL.md in your system prompt. Reference files load on demand.

What it catches

Banned phrases - Throat-clearing openers, emphasis crutches, business jargon, all adverbs, vague declaratives, meta-commentary. See references/phrases.md.

Structural clichés - Binary contrasts, negative listings, dramatic fragmentation, rhetorical setups, false agency, narrator-from-a-distance voice, passive voice. See references/structures.md.

Sentence-level rules - No Wh- sentence starters, no em dashes, no staccato fragmentation, no lazy extremes, active voice required.

Scoring

Rate 1-10 on each dimension:

Dimension	Question
Directness	Statements or announcements?
Rhythm	Varied or metronomic?
Trust	Respects reader intelligence?
Authenticity	Sounds human?
Density	Anything cuttable?

Below 35/50: revise.

Author

Hardik Pandya

License

MIT. Use freely, share widely.

(Preview — first 8 000 chars. View full README ↗)

twentyhq / twenty

The open alternative to Salesforce, designed for AI.

TypeScript ⭐ 47,655 🍴 6,769 ⭐ 495 stars today

📖 README

The #1 Open-Source CRM

Website · Documentation · Roadmap · Discord · Figma

Why Twenty

Twenty gives technical teams the building blocks for a custom CRM that meets complex business needs and quickly adapts as the business evolves. Twenty is the CRM you build, ship, and version like the rest of your stack.

Learn more about why we built Twenty

Installation

Cloud

The fastest way to get started. Sign up at twenty.com and spin up a workspace in under a minute, with no infrastructure to manage and always up to date.

Build an app

Scaffold a new app with the Twenty CLI:

npx create-twenty-app my-app

Define objects, fields, and views as code:

import { defineObject, FieldType } from 'twenty-sdk/define';

export default defineObject({
  nameSingular: 'deal',
  namePlural: 'deals',
  labelSingular: 'Deal',
  labelPlural: 'Deals',
  fields: [
    { name: 'name', label: 'Name', type: FieldType.TEXT },
    { name: 'amount', label: 'Amount', type: FieldType.CURRENCY },
    { name: 'closeDate', label: 'Close Date', type: FieldType.DATE_TIME },
  ],
});

Then ship it to your workspace:

npx twenty app:publish --private

See the app development guide for objects, views, agents, and logic functions.

Self-hosting

Run Twenty on your own infrastructure with Docker Compose, or contribute locally via the local setup guide.

Everything you need

Twenty gives you the building blocks of a modern CRM (objects, views, workflows, and agents) and lets you extend them as code. Here's a tour of what's in the box.

Want to go deeper? Read the User Guide for product walkthroughs, or the Documentation for developer reference.