03-27 00:04 · 科技
-
Research trending topics and generate ready-to-post social media content — as a Hermes/OpenClaw skill.
trendingcontent is a two-phase AI agent skill:
- Research phase — scrapes Reddit, X/Twitter, YouTube, TikTok, HackerNews, Bluesky and Brave Search to surface the most engaging content about your topic over a configurable time window (7, 15, 30, or up to 90 days).
- Content generation phase — uses the research findings to generate platform-specific social media copy: Twitter/X threads, LinkedIn posts, and Instagram captions.
Built on top of last30days by @mvanhorn, extended with:
- Variable time windows (not just 30 days)
- Multi-platform content generation (Twitter, LinkedIn, Instagram)
- Tone control (professional, casual, educational, viral)
- Multilingual output (English, Spanish, Portuguese)
- Hermes/OpenClaw skill standard compatibility
git clone https://github.com/gabogabucho/trendingcontent-agent.git
cd trendingcontent-agent
pip install -r requirements.txt
export SCRAPECREATORS_API_KEY="your_key" # required
export BRAVE_API_KEY="your_key" # optional but recommended
Or add them to your .env
file.
Get a ScrapeCreators API key at scrapecreators.com. Get a Brave Search API key at brave.com/search/api.
python scripts/trendingcontent.py "artificial intelligence" --days=7 --platform=all
python scripts/trendingcontent.py <topic> [options]
Options:
--days=N Time window: 7-90 days (default: 30)
--platform twitter | linkedin | instagram | all (default: all)
--tone professional | casual | educational | viral (default: professional)
--lang en | es | pt (default: en)
--sources=S1,S2 Only use these sources (reddit,twitter,youtube,tiktok,bluesky,hackernews,brave)
--disable=S1,S2 Disable specific sources
--list-sources Show all available sources
--quick Fewer sources, faster
--deep More sources, thorough
--research-only Output research only, skip content generation
# AI trends last 7 days → all platforms
python scripts/trendingcontent.py "artificial intelligence" --days=7
# Web3 → LinkedIn post in Spanish, professional tone
python scripts/trendingcontent.py "web3" --days=15 --platform=linkedin --lang=es
# Climate tech → viral Twitter thread
python scripts/trendingcontent.py "climate tech" --days=30 --platform=twitter --tone=viral
# Niche topic — only Reddit and YouTube (no HackerNews)
python scripts/trendingcontent.py "sourdough bread" --sources=reddit,youtube,brave
# Disable sources that don't fit your topic
python scripts/trendingcontent.py "fintech" --disable=hackernews,tiktok
# See all available sources
python scripts/trendingcontent.py --list-sources
# Research only, no content
python scripts/trendingcontent.py "LLMs" --days=14 --research-only
This repository follows the Hermes skill standard. To install as a skill:
# Copy to your Hermes skills directory
cp -r . /path/to/hermes-agent/skills/social-media/trendingcontent/
# Add API keys to your Hermes .env
echo "SCRAPECREATORS_API_KEY=your_key" >> ~/.hermes/.env
echo "BRAVE_API_KEY=your_key" >> ~/.hermes/.env
The agent will automatically discover the skill via SKILL.md
.
═══════════════════════════════════════════════
TRENDINGCONTENT — CONTENT GENERATION BRIEF
═══════════════════════════════════════════════
Topic: artificial intelligence
Period: Last 7 days
Language: English
Tone: Professional
Platforms: Twitter, LinkedIn, Instagram
───────────────────────────────────────────────
RESEARCH SUMMARY
───────────────────────────────────────────────
[ranked trending content with titles, scores, URLs]
───────────────────────────────────────────────
CONTENT INSTRUCTIONS
───────────────────────────────────────────────
[platform-specific format specs + tone guidance]
- Python 3.9+
yt-dlp
— YouTube transcript extractionrequests
— HTTP calls- ScrapeCreators API key (required)
- Brave Search API key (optional)
- Original research engine: last30days-skill by @mvanhorn — MIT License
- Social media generation layer & Hermes skill packaging: @gabogabucho
MIT License — see LICENSE for details.
▸ 展开全文
03-27 00:04 · 科技
This project presents a time-series analysis and forecast of Amazon's stock prices using historical data provided in AMZN.csv. The goal is to understand trends in stock prices and provide a reliable short-term (30-day) forecast using SARIMA modeling.
Stock market analysis plays a crucial role in financial decision-making. This project focuses on analyzing and forecasting Amazon’s stock prices using historical market data. By applying time-series analysis techniques, the project aims to identify trends, understand price movements, and generate a reliable short-term stock price forecast.
The Stock Prediction project performs a comprehensive time-series analysis on Amazon’s historical stock price data (AMZN.csv
). The workflow includes data cleaning, preprocessing, exploratory data analysis, visualization, and forecasting using the SARIMA (Seasonal AutoRegressive Integrated Moving Average) model.
The final output provides a 30-day stock price forecast along with confidence intervals, helping users gain insights into potential future price movements.
- Historical stock data analysis
- Data cleaning and preprocessing
- Time-series visualization of stock trends
- Daily price range and trading volume analysis
- Monthly average closing price analysis
- SARIMA-based 30-day stock price forecasting
- Confidence interval visualization
- Clear and interpretable plots for analysis
- Python 3.7+
- Pandas – data manipulation and analysis
- Matplotlib – data visualization
- Statsmodels – SARIMA forecasting model
- Streamlit (optional) – interactive dashboard
- Git & GitHub – version control
stock-prediction/ │ ├── data_cleaning/ │ └── clean_preprocess.py │ ├── images/ │ ├── daily-price-range.jpg │ ├── monthly-avg.jpg │ ├── trading-volume.jpg │ └── closing-price.jpg │ ├── AMZN.csv ├── README.md └── requirements.txt
- Load historical stock data (
AMZN.csv
) - Convert date column and set it as index
- Perform data validation and preprocessing
- Visualize trends and volatility
- Train SARIMA model on closing price data
- Generate 30-day forecast with confidence intervals
This project successfully demonstrates the application of time-series analysis for stock price forecasting. By leveraging SARIMA modeling, it provides meaningful insights into Amazon’s stock price behavior and delivers a reliable short-term forecast. The visualizations and structured workflow make this project a practical tool for financial analysis and a strong foundation for further enhancements such as real-time data integration and advanced predictive models.
Sanjey S
GitHub: https://github.com/SANJEY2106
▸ 展开全文
03-27 00:04 · 科技
Codex/CLaude/Gemini compatible skill: collect AI trends, write WeChat Official Account articles, generate images, publish to drafts
自动采集 AI 热点，撰写公众号文章，生成配图并发布到微信公众号草稿箱。
本仓库已移除所有硬编码的密钥/Token/本地路径；你需要通过环境变量自行配置（见下文）。
把整个目录放到你的 Skills 目录下（任选其一）：
~/.codex/skills/wechat-ai-publisher/
$CODEX_HOME/skills/wechat-ai-publisher/
~/.claude/skills/wechat-ai-publisher/
（Claude Code / Cloud Code）~/.gemini/skills/wechat-ai-publisher/
（Gemini：建议约定此路径，具体以你的工具为准）~/.openclaw/workspace/skills/wechat-ai-publisher/
（OpenClaw 默认 workspace）~/.openclaw/skills/wechat-ai-publisher/
（OpenClaw 全局 skills）
- 复制配置模板：
cp .env.example .env
- 在
.env
填入以下变量：
WECHAT_APPID
/WECHAT_SECRET
：公众号后台获取（用于换取access_token
）。REPLICATE_API_KEY
：用于生成配图（Replicate）。WECHAT_AUTHOR
（可选）：文章作者字段（默认：田威 AI
）。DEFAULT_COVER_URL
（可选）：图片生成失败时的兜底封面 URL。
- 让环境变量生效（示例）：
set -a
source .env
set +a
辅助脚本 scripts.sh
需要以下命令可用：
bash
,curl
,jq
Skill 运行环境（采集内容）依赖你已配置好对应工具/API：
mcp__tavily__tavily-search
/mcp__tavily__tavily-extract
mcp__exa__web_search_exa
本仓库不包含这些服务的 API Key，你需要在自己的 Agent/IDE/CLI 工具里按其官方方式配置。
注意：
scripts.sh
只是便捷脚本；Skill 本体逻辑在SKILL.md
。
示例文章（科普/护理/科技风格）：examples/nursing-tech-article.md
加载脚本：
source ./scripts.sh
常用函数：
get_wechat_token
generate_image "<prompt>" ["16:9"]
upload_to_r2 <local_path> <remote_path>
publish_article "<title>" "<content_html>" "<cover_prompt>" "<digest>"
参数建议：content_html
尽量从文件读取，避免 shell 引号/换行转义问题：
content_html=$(cat ./content.html)
publish_article "标题" "$content_html" "封面图 prompt" "摘要"
- 不要把
.env
提交到仓库；本仓库已通过.gitignore
忽略它。 - 任何线上发布前，建议在公众号后台/接口调用白名单中配置好服务器 IP（如有要求）。
OpenClaw 自带 Cron（需要你的 OpenClaw Gateway 常驻运行）。
推荐做法：把「默认配置」固定在 Skill 里，Cron 只负责每天触发一次；当你想改主题/风格/口吻时，再在触发消息里追加说明即可。
默认配置（可在 SKILL.md
里改）：
- 主题：AI工具
- 风格：github
- 配图：封面 + 3 张
- 作者：田威 AI
示例：每天 09:00（上海时区）触发一次发布（使用默认配置）：
openclaw cron add \
--name "wechat-ai-daily" \
--cron "0 9 * * *" \
--tz "Asia/Shanghai" \
--session isolated \
--message "发布AI热点；不要提问，直接执行"
临时覆盖默认配置（只要把需求“说出来”即可）：
openclaw cron add \
--name "wechat-ai-daily" \
--cron "0 9 * * *" \
--tz "Asia/Shanghai" \
--session isolated \
--message "发布AI热点；主题=护理；风格=purple；配图=仅封面；写得更科普一些；不要提问，直接执行"
查看/管理：
openclaw cron list
openclaw cron delete --name "wechat-ai-daily"
▸ 展开全文
03-27 00:04 · 科技
OpenClaw AI agent skill for automated TikTok and Instagram carousel growth. Pass any website URL to analyze brand, competitors, colors, value proposition. Generates 6 visually coherent slides and auto-publishes with trending music via upload-post API. Built-in analytics and learning loop. Free tier, no credit card. Larry alternative.
Generate viral TikTok/Instagram carousel slides from any website automatically.
Post 1 carousel per day. Learn. Iterate. Grow.
Day 1: Post → Analyze → Learn
Day 7: 7 data points, patterns emerging
Day 30: Refined strategy, proven hooks, optimal timing
The skill accumulates insights across ALL your posts in learnings.json
.
- 🔍 Full Business Research - Analyzes brand, features, competitors from any URL
- 🎨 AI Image Generation - Creates 6 visually coherent slides with Gemini (free plan available, no credit card)
- 📱 Direct Publishing - Posts straight to your TikTok feed with auto trending music + Instagram simultaneously. No drafts, no manual steps
- 💰 100% Free to Start - Both APIs (Gemini and upload-post.com) have free plans, no credit card required
- 📊 Analytics Tracking - Monitors performance across all posts
- 🔄 Learning Loop - Accumulates insights: best hooks, times, days, styles
# 1. Analyze website
node scripts/analyze-web.js https://your-product.com
# 2. Generate slides
GEMINI_API_KEY="..." bash scripts/generate-slides.sh
# 3. Publish
UPLOADPOST_TOKEN="..." bash scripts/publish-carousel.sh
# 4. Check analytics
UPLOADPOST_TOKEN="..." bash scripts/check-analytics.sh 7
- Node.js 18+
- Playwright (
npm install playwright && npx playwright install chromium
) - uv (Python package runner)
- jq
- Hook - Attention-grabbing question/problem
- Problem - Agitate the pain point
- Agitation - Show competition advancing
- Solution - Reveal your product
- Feature - Key benefit
- CTA - Call to action (Link in bio)
- ✅ All credentials are read from environment variables, not stored in files
- ✅ No API keys are hardcoded in any scripts
- ✅ Website analysis only reads public web pages (no authentication scraping)
- ✅ All generated data stays local in
/tmp/carousel/
- Website URL → Analyzed by Playwright (local browser) →
analysis.json
- Analysis → Sent to Gemini API → Returns generated images
- Images + Caption → Sent to Upload-Post API → Published to TikTok/Instagram
- Analytics → Fetched from Upload-Post API → Stored locally in
learnings.json
No sensitive user data is transmitted. Only the content you're publishing.
Most carousel automation skills post to drafts and require manual publishing. Viraloop is different:
See SKILL.md for full documentation.
MIT
▸ 展开全文
03-27 00:04 · 科技
Automated AI news aggregation that feeds directly into your Obsidian vault. Stay current on the latest LLM research, trending repos, and breakthroughs — curated daily
Your morning briefing, hand-picked from the places that matter.
⭐ If this saves you time, give it a star. It helps others find it.
Researchers · ML engineers · Obsidian users · Anyone who wants to stay current without the morning rabbit hole
Latent Feed is a little tool that scours your favourite corners of the internet—papers, repos, threads, and discussions—then bundles the good stuff into one tidy digest. No more tab-hopping. Just set it up once, let it run daily, and wake up to a fresh batch of what’s worth reading.
You choose where it lands: your Obsidian vault, your inbox, or both.
Features: Run once a day · Clean digest by topic · Write to Obsidian or email
→ Examples — Email · Obsidian daily note
Click to expand — quick sample
## 🏢 Frontier Labs
**[Claude 4.5 Released](https://anthropic.com/...)** · anthropic
> New model with improved reasoning.
## 📄 Research
**[Transformers Are Bayesian Networks](https://arxiv.org/...)** · arxiv
> Connections between attention and probabilistic inference.
## 🔓 Open Source Repos
**[vLLM v0.6.0: Prefix Caching + LoRA](https://github.com/...)** · github
> 5x faster serving.
We pull from six places. Toggle any on or off in your config.
Add your own RSS feeds in config.yaml
under blogs.feeds
.
Items are auto-tagged into 10 categories. Edit processor/categorizer.py
to adjust keywords.
Latent Feed doesn’t fetch these, but they’re worth following:
YouTube — Andrej Karpathy, Stanford Online (CS25), Anthropic, Yannic Kilcher, DeepLearning.AI, Dwarkesh Patel
Courses — Karpathy: Zero to Hero, Stanford CS25: Transformers
pip install -r requirements.txt
cp config/config.example.yaml config/config.yaml
Open config/config.yaml
and at least set your vault path:
vault_path: "/path/to/your/obsidian-vault"
daily_notes_subfolder: "LatentFeed"
If you want short summaries for each item, set your API key:
export ANTHROPIC_API_KEY="your-api-key"
And in config.yaml
:
use_summarization: true
llm_provider: "claude"
Add this to config.yaml
:
email:
enabled: true
subscribers:
- "you@example.com"
smtp:
host: "smtp.gmail.com"
port: 587
from_addr: "your-bot@gmail.com"
subject: "Latent Feed — {date}"
Then set your SMTP credentials (prefer env vars):
export SMTP_USER="your-bot@gmail.com"
export SMTP_PASSWORD="your-app-password"
💡 Gmail users: use an App Password, not your usual login.
python main.py
Try it in 30 seconds — preview the digest with no config:
git clone https://github.com/<you>/latent-feed && cd latent-feed
pip install -r requirements.txt
python main.py --dry-run
Add a cron job so it runs at 8 AM:
crontab -e
# Add this line:
0 8 * * * cd /path/to/latent-feed && /path/to/python main.py
If your vault lives in a Git repo, you can run Latent Feed on a schedule:
-
Add these Secrets in your repo settings:
ANTHROPIC_API_KEY
(if you use summaries)VAULT_PATH
(path to your vault)SMTP_USER
,SMTP_PASSWORD
(if you use email)
-
If the vault is in another repo, clone it in the workflow first, then pass
VAULT_PATH
.
⚠️ Most local vaults can’t be reached from GitHub. For email-only, use--email-only
with your SMTP secrets.
These files are in .gitignore
and must never be committed to GitHub:
- Copy
config/config.example.yaml
toconfig/config.yaml
- Copy
.env.example
to.env
and fill in your real values - Never commit
config.yaml
or.env
— they hold private data
latent-feed/
├── main.py # Entry point
├── sources/ # News sources
├── processor/ # Filter, dedup, categorize, summarize
├── output/ # Markdown, Obsidian, email
├── models/ # Data models
├── config/ # Configuration
└── .github/workflows/ # GitHub Actions
- CONTRIBUTING.md — How to contribute
Found this useful? Star the repo, share it with a colleague, or post about it. Every bit helps.
MIT
▸ 展开全文
03-27 00:04 · 科技
GitHub 话题趋势追踪工具 - 按主题监控热门项目，AI 智能分析，每日邮件报告，GitHub Pages 可视化
追踪 GitHub 话题下的热门项目，AI 智能分析，每日趋势报告邮件
GitHub Topics Trending 是一个自动化仓库趋势追踪系统。它每天通过 GitHub API 获取指定话题下的热门仓库，使用 Claude AI 对仓库进行智能分析和分类，计算星标变化趋势，并通过 Resend 发送专业的 HTML 邮件报告，同时生成 GitHub Pages 静态网站。
- 开发者视角 - 快速了解特定话题下的热门项目
- 趋势洞察 - 捕捉新兴项目和工具的崛起
- 智能总结 - AI 帮你理解每个项目解决什么问题
- 自动化 - 无需手动查看网站，每天自动推送
GitHub Topics Daily - 2026-01-27
├── Top 20 经典榜单（含 AI 总结）
│ ├── 仓库名称（可点击跳转）、排名、星标数
│ ├── AI 一句话摘要
│ ├── 详细说明
│ └── 解决的问题标签
├── 星标增长 Top 5
├── 新晋项目
├── 活跃项目
└── 趋势统计
┌─────────────────────────────────────────────────────────────────┐
│ GitHub Topics Trending │
└─────────────────────────────────────────────────────────────────┘
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ GitHub │ │ GitHub │ │ Claude │
│ Actions │ ──▶ │ Fetcher │ ──▶ │ Summarizer │
│ (Cron Daily)│ │ (API) │ │ AI │
└──────────────┘ └──────┬───────┘ └──────┬───────┘
│ │
▼ │
┌──────────────┐ │
│ README │ │
│ Fetcher │ │
└──────┬───────┘ │
│ │
└───────┬───────────────┘
│
▼
┌──────────────┐
│ Database │
│ (SQLite) │
└──────┬───────┘
│
▼
┌──────────────┐
│ Trend │
│ Analyzer │
└──────┬───────┘
│
┌─────────────────┴─────────────────┐
▼ ▼
┌──────────────┐ ┌──────────────┐
│ Email │ │ Web │
│ Reporter │ │ Generator │
└──────┬───────┘ └──────┬───────┘
│ │
▼ ▼
┌──────────────┐ ┌──────────────┐
│ Resend │ │ GitHub │
│ Sender │ │ Pages │
└──────┬───────┘ └──────────────┘
│
▼
──────► 您的邮箱
- Python 3.11+
- GitHub Personal Access Token
- Claude API Key（支持智谱代理）
- Resend API Key
# 克隆仓库
git clone https://github.com/geekjourneyx/github-topics-trending.git
cd github-topics-trending
# 安装依赖
pip install -r requirements.txt
# 复制环境变量模板
cp .env.example .env
# 编辑 .env 文件，填入你的 API Keys
nano .env
# 设置环境变量
export GH_TOKEN="your_github_token"
export ZHIPU_API_KEY="your_api_key"
export RESEND_API_KEY="your_resend_key"
export EMAIL_TO="your_email@example.com"
# 运行（完整流程）
python -m src.main
# 仅获取数据，不发送邮件
python -m src.main --fetch-only
注意: 环境变量已配置为使用 GH_TOKEN
和 TOPIC
，避免以 GITHUB_
开头的 Secret 名称。
# 设置环境变量
export GH_TOKEN="your_github_token"
export TOPIC="claude-code"
---
## 配置说明
### 环境变量
| 变量 | 必需 | 说明 | 默认值 |
|-----|------|------|--------|
| `GH_TOKEN` | Yes | GitHub Personal Access Token | - |
| `TOPIC` | No | 要追踪的 GitHub Topic | `claude-code` |
| `ZHIPU_API_KEY` | Yes | Claude API Key（智谱代理） | - |
| `ANTHROPIC_BASE_URL` | No | Claude API 地址 | `https://open.bigmodel.cn/api/anthropic` |
| `RESEND_API_KEY` | Yes | Resend API Key | - |
| `EMAIL_TO` | Yes | 收件人邮箱 | - |
| `RESEND_FROM_EMAIL` | No | 发件人邮箱 | `onboarding@resend.dev` |
| `DB_PATH` | No | 数据库路径 | `data/github-trending.db` |
| `DB_RETENTION_DAYS` | No | 数据保留天数 | `90` |
| `SURGE_THRESHOLD` | No | 暴涨阈值（比例） | `0.3` |
### GitHub Token 配置
#### 申请 Personal Access Token (PAT)
1. 登录 GitHub
2. 点击头像 → **Settings**
3. 左侧菜单最下方 → **Developer settings**
4. **Personal access tokens** → **Tokens (classic)**
5. **Generate new token** → **Generate new token (classic)**
6. 配置 Token：
- **Note**: 输入 `GitHub Topics Bot`
- **Expiration**: 选择有效期（建议 `90 days` 或 `No expiration`）
- **勾选权限**:
- ☑️ `public_repo` (访问公共仓库)
- 点击 **Generate token**
7. 复制 token（只显示一次！请妥善保存）
#### 在 GitHub Actions 中配置
1. 进入 GitHub 仓库页面
2. **Settings** → **Secrets and variables** → **Actions**
3. **New repository secret**
4. 配置:
- **Name**: `GITHUB_TOKEN`
- **Value**: 粘贴你的 token
5. 点击 **Add secret**
### Resend 配置
1. 注册 [Resend](https://resend.com)
2. 创建 API Key
3. 配置发件人域名（或使用默认的 `onboarding@resend.dev`）
---
## 使用方法
### 命令行运行
```bash
# 完整流程（获取数据 + AI 分析 + 发送邮件 + 生成网站）
python -m src.main
# 仅获取数据
python -m src.main --fetch-only
# 查看最新数据日期
sqlite3 data/github-trending.db "SELECT date FROM repos_daily ORDER BY date DESC LIMIT 1;"
# 查看今日排行榜 Top 10
sqlite3 data/github-trending.db "SELECT rank, repo_name, stars FROM repos_daily WHERE date = '2026-01-27' ORDER BY rank LIMIT 10;"
# 查看仓库详情
sqlite3 data/github-trending.db "SELECT repo_name, summary, category FROM repos_details WHERE repo_name = 'anthropics/claude-code';"
-
进入仓库 Settings → Pages
-
Build and deployment 配置：
- Source: Deploy from a branch
- Branch: gh-pages 分支
- Folder: / (root)
- 点击 Save
- 仓库 Settings → General
- 滚动到底部 Danger Zone
- Change repository visibility → 选择 Public
- 确认更改
https://geekjourneyx.github.io/github-topics-trending/
-
Settings → Secrets and variables → Actions → New repository secret
-
添加以下 Secrets：
- Settings → Actions → General
- Workflow permissions → 选择 Read and write permissions
- 点击 Save
- 点击 Actions 标签
- 选择 GitHub Topics Trending workflow
- 点击 Run workflow → Run workflow
默认每天 UTC 02:00（北京时间 10:00）自动运行。
github-topics-trending/
├── .github/workflows/
│ └── github-trending.yml # GitHub Actions 配置
├── src/
│ ├── config.py # 配置管理
│ ├── database.py # SQLite 操作
│ ├── github_fetcher.py # GitHub API 采集
│ ├── readme_fetcher.py # README 获取
│ ├── claude_summarizer.py # AI 分析
│ ├── trend_analyzer.py # 趋势计算
│ ├── email_reporter.py # 邮件生成
│ ├── web_generator.py # 网站生成
│ ├── resend_sender.py # 邮件发送
│ └── main.py # 主入口
├── plugins/
│ └── github-topics/ # Claude Code Skill
├── docs/ # GitHub Pages 输出
├── data/
│ └── github-tre
▸ 展开全文
03-27 00:04 · 科技
Crypto AI Analytics is a sophisticated desktop tool designed for data-driven crypto market analysis and intelligent trend detection.
📈 Crypto AI Analytics
Crypto AI Analytics is a sophisticated desktop tool designed for data-driven crypto market analysis and intelligent trend detection. Built for serious researchers and developers, it utilizes AI models to parse complex market datasets, identify patterns, and provide insightful visualizations for your trading strategies. With an intuitive dashboard and secure data handling, it allows you to optimize your research workflow without compromising your personal API credentials.
Whether you are performing technical analysis, testing historical scenarios, or exploring machine learning-based price predictions, Crypto AI Analytics provides the powerful workspace you need to analyze the market smarter. 🚀
👥 Who Is Crypto AI Analytics For?
Crypto AI Analytics is ideal for users interested in quantitative finance and market research:
📊 Market Researchers studying volatility, sentiment, and historical patterns
💡 Quantitative Analysts developing new data-backed trading logic
🖥️ Developers building custom connectors for cryptocurrency exchange data
🧑🏫 Technical Traders seeking advanced visual indicators beyond standard charting
🧪 Backtesting Enthusiasts simulating and analyzing various financial scenarios
🎯 What Can You Use It For?
Crypto AI Analytics helps you turn raw exchange data into clear insights:
🔍 Trend Analysis – leverage neural networks to detect shifts in market momentum
📑 Automated Reporting – track your custom assets with AI-generated summary reports
🧪 Historical Simulation – simulate strategy outcomes based on past market data
🌐 Exchange Aggregation – view and analyze data across multiple providers
🤖 Strategy Prototyping – fine-tune your logic using real-time market inputs
✨ Key Features
Crypto AI Analytics includes a professional-grade toolset for market research:
🛡️ Zero-Credential Policy – secure integration using your own read-only API keys stored locally
⚡ Optimized Data Pipeline – high-performance architecture for processing live data streams
📊 Visual Dashboard – clear, professional-grade charts for identifying anomalies
🔄 Adaptive Learning – utilizes ML algorithms that evolve with incoming market inputs
🧩 Custom Plugins – support for user-defined Python or JS strategies and custom indicators
📂 Offline Export – save your entire research session, data exports, and logs for review
⚙️ Installation
To install Crypto AI Analytics, simply go to the Releases section of this repository and download the latest available version for your operating system. After downloading, extract the archive if necessary and run the installer file. Follow the installation steps to complete the setup.
👉 Download the latest version here:
💻 System Requirements
🖥️ OS: Windows 10 / 11, macOS, or Linux
🧠 RAM: 8 GB minimum (16 GB recommended for historical backtesting)
🌐 Connection: High-speed internet required for live market data
💾 Storage: At least 5 GB of free space for database and logs
⭐ Support the Project
If you find this project useful, consider giving the repository a Star ⭐ on GitHub.
Your support helps the project grow and improve!
(Disclaimer: This tool is designed for educational and analytical purposes. Financial market activity involves significant risk; this software does not guarantee results. Users are responsible for their own trades and data management.)
▸ 展开全文
03-27 00:04 · 科技
ShopIQ is an AI-powered retail intelligence platform built for Praxis 2.0 that analyzes shopping data to predict trends, discover shopper behavior patterns, segment customers, and uncover product affinities using machine learning and an interactive AI analytics assistant.
Production-Ready Retail Analytics Platform with AI-powered insights, forecasting, and shopper behavior analysis.
✨ Recently Updated: Enhanced responsive design, error handling, and production deployment configuration.
A comprehensive data analytics platform with forecasting, shopper behavior analysis, and interactive visualizations built with Next.js and TypeScript.
- Interactive Dashboard: View and analyze your data with rich visualizations
- Revenue Forecasting: Predictive analytics using machine learning models
- Shopper Behavior Analysis: Advanced customer segmentation using RFM analysis and clustering
- Market Basket Analysis: Discover product affinities and purchasing patterns
- Data Upload & Processing: Upload CSV files and automatically process them
- 🆕 Custom Local AI Assistant: Free, privacy-focused AI that answers questions about your data - no API costs!
- Responsive Design: Works seamlessly across desktop and tablet devices
- Framework: Next.js 16.1.6
- Language: TypeScript
- Styling: Tailwind CSS with custom components
- UI Components: Radix UI primitives
- Charts: Recharts
- State Management: Zustand
- Icons: Lucide React
- AI Integration: @ai-sdk/react for AI assistant functionality
The application provides multiple perspectives for data analysis:
- Dashboard: Overview of key metrics and visualizations
- Forecast: Revenue predictions and model performance metrics
- Comparison: Side-by-side data comparison capabilities
- Behavior: Detailed shopper behavior and segmentation analysis
- Upload: Data import and preprocessing workflow
- Settings: Application configuration options
# 1. Install dependencies
npm install
# 2. Set up environment variables
cp .env.example .env.local
# Edit .env.local and add your OPENAI_API_KEY
# 3. Run development server
npm run dev
# Push to Git
git add . && git commit -m "Update" && git push
# Render will auto-deploy
- The application loads with prebuilt sample data for immediate exploration
- Navigate between different views using the sidebar
- Interact with charts and visualizations to explore data insights
- Go to the Upload view from the sidebar
- Select a CSV file containing your transaction data
- Map your columns to the required fields (category, purchase amount, date, customer ID)
- The application will process your data and update all visualizations
- RFM Analysis: Recency, Frequency, Monetary analysis for customer segmentation
- Clustering: Unsupervised learning to identify customer groups
- Affinity Rules: Market basket analysis to discover product relationships
- Predictive Models: Revenue forecasting with confidence intervals
├── app/ # Next.js app router pages
├── components/ # Reusable UI components
│ ├── ui/ # Base UI components (buttons, cards, etc.)
│ └── views/ # Page-level components
│ ├── error-boundary.tsx # Error handling
│ └── theme-provider.tsx # Theme management
├── lib/ # Shared utilities and business logic
│ ├── store.ts # Zustand store for state management
│ ├── types.ts # Type definitions
│ ├── behavior-analysis.ts # Shopper behavior algorithms
│ └── data-processing.ts # Data processing utilities
├── public/ # Static assets
├── styles/ # Global styles
├── .env.example # Environment variables template
├── render.yaml # Render deployment config
└── DEPLOYMENT.md # Deployment guide
graph TD
A[User Interface] --> B[Next.js App Router]
B --> C[Zustand Store]
C --> D[Data Processing Layer]
D --> E[Business Logic Components]
E --> F[UI Components]
G[CSV Upload] --> H[Data Validation]
H --> I[Data Cleaning]
I --> J[Feature Engineering]
J --> K[Analysis Engine]
K --> L[RFM Analysis]
K --> M[Clustering Engine]
K --> N[Affinity Analysis]
K --> O[Forecasting Model]
L --> P[Visualization Layer]
M --> P
N --> P
O --> P
P --> Q[Dashboard Views]
Q --> R[User Interface]
S[AI Assistant] --> T[Chat API]
T --> U[LLM Integration]
U --> V[Response Generation]
V --> R
W[Settings Management] --> X[Configuration Store]
X --> C
flowchart LR
A[User Input] --> B[Frontend Components]
B --> C[Zustand State Management]
D[CSV File Upload] --> E[File Validation]
E --> F[Data Parsing]
F --> G[Column Mapping]
G --> H[Data Cleaning]
H --> I[Feature Extraction]
I --> J[Data Transformation]
J --> K[Processed Dataset]
K --> L[RFM Calculation]
K --> M[Clustering Analysis]
K --> N[Affinity Rules]
K --> O[Time Series Forecasting]
L --> P[Segmentation Results]
M --> Q[Cluster Assignments]
N --> R[Association Rules]
O --> S[Forecast Predictions]
P --> T[Visualization Engine]
Q --> T
R --> T
S --> T
T --> U[Dashboard Components]
U --> V[User Interface]
W[AI Chat Requests] --> X[API Processing]
X --> Y[LLM Integration]
Y --> Z[Response Generation]
Z --> V
graph TD
A[User Authentication] --> B[Session Management]
B --> C[Route Protection]
D[Input Validation] --> E[Data Sanitization]
E --> F[API Security Layer]
F --> G[Server-Side Validation]
G --> H[Response Sanitization]
I[Environment Security] --> J[Secrets Management]
J --> K[Configuration Validation]
L[Client-Side Security] --> M
▸ 展开全文
03-27 00:04 · 科技
Polymarket arbitrage bot -Arbitrage Between Up and Down token in 15m Markets - Rust Based Trading Bot appearing maximum speed for placing market orders - Locking and Increasing Positions according to Trending || $0.99 Locking Comparison || polymarket arbitrage bot || polymarket trading bot || polymarket bot || Rust Bot ||
Automate your soccer market execution on Polymarket with a battle-tested momentum workflow designed for speed, discipline, and repeatable trade management.
Check my profile to get inspiration - @manaboot
Private bot for serious traders who want a professional edge without wasting time on manual market monitoring.
- Live soccer market scanning across selected competitions and tags.
- Momentum-based entries with configurable trigger thresholds.
- Smart limit execution for better fill reliability.
- Automated take-profit exits with minimum hold safeguards.
- Built-in risk filters to avoid low-quality order book conditions.
- Fast configuration tuning for conservative or aggressive styles.
- Traders active on Polymarket who want to scale execution quality.
- Users who already understand soccer market dynamics and timing.
- Operators who prefer clear rules over emotional decision-making.
- Buyers looking for a private, practical trading system instead of public scripts.
The bot continuously monitors live soccer events and tracks bid/ask behavior in real time.
When momentum criteria are met, it executes rule-based entries and manages exits automatically.
- Fetch and refresh eligible soccer markets.
- Track live book conditions for each target event.
- Detect valid bid-momentum setups.
- Place controlled limit entries with safety checks.
- Exit at configured take-profit levels when conditions confirm.
- Reset and continue monitoring for the next setup.
This is designed for consistency, discipline, and speed of reaction during live market movement.
- Private bot package (compiled/runtime format based on agreement).
- Setup guidance for API credentials and environment.
- Base strategy profile with configurable parameters.
- Quick-start instructions for simulation and live mode.
- Optional support window for onboarding (if offered by seller).
If you want access to this bot, contact:
- Telegram:
@crellios
- Discord:
@0x_crellos
- Email:
piter.jb0817@gmail.com
Please include:
- Your trading experience level
- Preferred risk style (conservative / balanced / aggressive)
- Expected usage frequency (daily / weekends / major leagues only)
No. This repository is a public showcase for the private selling version.
No. Market conditions vary and losses are possible. Proper risk control is essential.
Basic trading knowledge is strongly recommended before using automation with real funds.
Yes, simulation/testing workflow is available before switching to live execution.
Trading prediction markets involves substantial risk. This bot is a tool, not financial advice.
You are fully responsible for execution decisions, local compliance, exchange terms, and capital risk.
▸ 展开全文
03-27 00:04 · 科技
自动追踪 skills.sh 技能排行榜，AI 智能分析，每日趋势报告邮件
自动追踪 skills.sh 技能排行榜，AI 智能分析，每日趋势报告邮件
Skills Trending Daily 是一个自动化技能趋势追踪系统。它每天从 skills.sh/trending 获取最新的技能排行榜，使用 Claude AI 对热门技能进行智能分析和分类，计算排名变化趋势，并通过 Resend 发送专业的 HTML 邮件报告。
- 开发者视角 - 快速了解哪些技能值得学习
- 趋势洞察 - 捕捉新兴技术框架和工具的崛起
- 智能总结 - AI 帮你理解每个技能解决什么问题
- 自动化 - 无需手动查看网站，每天自动推送
Skills Trending Daily - 2026-01-24
├── Top 20 Leaderboard（含 AI 总结）
│ ├── 技能名称（可点击跳转）、排名、安装量
│ ├── AI 一句话摘要
│ ├── 详细说明
│ └── 解决的问题标签
├── Rising Skills（上升幅度 Top 5）
├── Declining Skills（下降幅度 Top 5）
├── New & Dropped（新晋/掉榜）
└── Trending Up（安装量暴涨告警）
┌─────────────────────────────────────────────────────────────────┐
│ Skills Trending 系统架构 │
└─────────────────────────────────────────────────────────────────┘
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ GitHub │ │ Playwright │ │ Claude │
│ Actions │ ──▶ │ Skills │ ──▶ │ Summarizer │
│ (Cron Daily)│ │ Fetcher │ │ AI │
└──────────────┘ └──────┬───────┘ └──────┬───────┘
│ │
▼ │
┌──────────────┐ │
│ Detail │ │
│ Fetcher │ │
│ (Top N) │ │
└──────┬───────┘ │
│ │
└───────┬───────────────┘
│
▼
┌──────────────┐
│ Database │
│ (SQLite) │
└──────┬───────┘
│
▼
┌──────────────┐
│ Trend │
│ Analyzer │
└──────┬───────┘
│
▼
┌──────────────┐
│ HTML │
│ Reporter │
└──────┬───────┘
│
▼
┌──────────────┐
│ Resend │
│ Sender │
└──────┬───────┘
│
▼
──────► 您的邮箱
- Python 3.11+
- Claude API Key（支持智谱代理）
- Resend API Key
# 克隆仓库
git clone https://github.com/geekjourneyx/trending-skills.git
cd trending-skills
# 安装依赖
pip install -r requirements.txt
# 安装 Playwright 浏览器
playwright install chromium
# 复制环境变量模板
cp .env.example .env
# 编辑 .env 文件，填入你的 API Keys
nano .env
# 设置环境变量
export ZHIPU_API_KEY="your_api_key"
export RESEND_API_KEY="your_resend_key"
export EMAIL_TO="your_email@example.com"
# 运行
python src/main_trending.py
- 注册 Resend
- 创建 API Key
- 配置发件人域名（或使用默认的
onboarding@resend.dev
）
# 完整流程
python src/main_trending.py
# 查看最新数据日期
sqlite3 data/trends.db "SELECT date FROM skills_daily ORDER BY date DESC LIMIT 1;"
# 查看今日排行榜 Top 10
sqlite3 data/trends.db "SELECT rank, name, installs FROM skills_daily WHERE date = '2026-01-24' ORDER BY rank LIMIT 10;"
# 查看技能详情
sqlite3 data/trends.db "SELECT name, summary, category FROM skills_details WHERE name = 'remotion-best-practices';"
- Fork 本仓库
- 在 GitHub Settings > Secrets and variables > Actions 中添加：
ZHIPU_API_KEY
RESEND_API_KEY
EMAIL_TO
（可选）
- 启用 Actions
默认每天 UTC 02:00（北京时间 10:00）自动运行。
修改时间：编辑 .github/workflows/skills-trending.yml
中的 cron
表达式。
在 GitHub Actions 页面点击 "Run workflow" 按钮手动执行。
skills-trending/
├── .github/workflows/
│ └── skills-trending.yml # GitHub Actions 配置
├── src/
│ ├── config.py # 配置管理
│ ├── database.py # SQLite 操作
│ ├── skills_fetcher.py # 榜单抓取（Playwright）
│ ├── detail_fetcher.py # 详情抓取
│ ├── claude_summarizer.py # AI 分析
│ ├── trend_analyzer.py # 趋势计算
│ ├── html_reporter.py # 邮件生成
│ ├── resend_sender.py # 邮件发送
│ └── main_trending.py # 主入口
├── plugins/
│ └── trending-skills/ # Claude Code Skill
├── data/
│ └── trends.db # 数据库（运行时生成）
├── requirements.txt
├── .env.example
├── CHANGELOG.md
└── README.md
新增数据源
# 修改 skills_fetcher.py
class SkillsFetcher:
def __init__(self, timeout: int = 30000):
self.trending_url = "your_custom_url"
新增分析维度
# 修改 trend_analyzer.py
def calculate_trends(self, today_skills, today, ai_summary_map):
# 添加新的分析逻辑
pass
自定义邮件样式
# 修改 html_reporter.py
def _get_header(self, date: str) -> str:
# 修改样式和布局
pass
- 检查 Resend API Key 是否正确
- 确认收件人邮箱地址
- 查看垃圾邮件箱
- 检查 GitHub Actions 日志
# 重新安装
playwright install chromium --with-deps
默认位置：data/trends.db
sqlite3 data/trends.db
.tables
SELECT * FROM skills_daily ORDER BY date DESC LIMIT 10;
编辑 .github/workflows/skills-trending.yml
：
schedule:
- cron: '0 2 * * *' # UTC 时间，每天 02:00
如果该项目帮助了您，请作者喝杯咖啡吧
- 作者:
geekjourneyx
- X (Twitter): https://x.com/seekjourney
- 公众号: 极客杰尼
关注公众号，获取更多 AI 编程、AI 工具与 AI 出海建站的实战分享：
- skills.sh - 技能数据来源
- Anthropic - Claude AI
- Resend - 邮件服务
- Playwright - 浏览器自动化
▸ 展开全文