04-26 16:01 · 科技,GitHub
Self-hosted Plex companion: Smart Discovery (recommendations from watch history and taste), add movies and shows from the app via Radarr/Sonarr, Kometa config builder (no YAML), Plex collections, Overseerr requests, Tautulli trending. One dashboard, less scrolling, more watching.
Saved you a scroll or two? If this app saves you from endless scrolling, a star for the project helps a lot.
Self-hosted Plex companion: Smart Discovery from your watch history, add movies/shows via Radarr/Sonarr, Kometa builder, Plex collections, Tautulli. One dashboard -less scrolling, more watching.
SeekAndWatch Cloud (beta) - Hosted option so friends and family can request from your Plex server without needing access to your apps. You approve or deny; requests sync to Radarr or Sonarr. Zero port forwarding.
Documentation: Wiki - install, Smart Discovery, Plex Collections, Radarr, Sonarr, Kometa builder, troubleshooting.
Important:
- SeekAndWatch Cloud linking now uses the local app payload plus One-Click Pair with Cloud in Requests Settings. Existing cloud users may need to re-link after updating.
- The main app uses the TMDB API Read Access Token. Kometa uses its own separate TMDB API Key field.
- Public registration is first-run only by default. After the first account is created, registration stays closed unless you explicitly re-enable it.
/config
must be writable so SeekAndWatch can persist its secret key, or you must provideSECRET_KEY
yourself.
SeekAndWatch is a self-hosted Plex companion that turns your library into a smart “what should we watch?” hub. It connects Plex, Tautulli, TMDB, Radarr, and Sonarr in one dashboard so you can discover, decide, and request without switching tabs.
Goal: spend less time browsing, more time watching. It uses your watch history and owned libraries (Plex, Radarr, Sonarr) to surface stuff you don’t have yet and gives you tools to build collections without editing YAML.
- Uses your last 5,000 plays to build a taste profile and recommend titles you don’t own or haven’t watched.
- Seed-based recommendations (pick movies/shows you like; get similar stuff) plus I’m Feeling Lucky for random picks.
- Filters: genre, year, rating, Certified Fresh (Rotten Tomatoes), future releases only, international & obscure.
- Owned items hidden - Plex library plus optional Radarr/Sonarr scanner so recommendations exclude what you already have.
- Randomized results each run; load more without regenerating.
- Instant trailers in the app; optional OMDB for Rotten Tomatoes/critic scores.
- Add movies/shows from the app - Request from Smart Discovery or elsewhere; opens in Radarr/Sonarr with quality profile and root folder.
- Requested (Media page) - View and manage requested items in one place. Adding to Radarr/Sonarr is done from Smart Discovery or the request flow, not from a Radarr/Sonarr library view in the app.
- Radarr & Sonarr Scanner (optional) - Background scan of your Radarr/Sonarr libraries so those items are treated as "owned" and excluded from Smart Discovery (in addition to Plex).
- Visual builder for Kometa overlays and collections with toggles.
- Uses a Kometa-only TMDB v3 API key inside the builder. The rest of SeekAndWatch uses the TMDB API Read Access Token.
- Live preview for overlays, ratings, codecs, content badges.
- Library templates, undo/redo, comparison (current vs saved), performance estimates.
- Import configs (paste or URL); generates clean configs you can refine later.
- 130+ preset collections - decades, genres, studios, themes, international, awards. Categories are collapsible so you can focus on what you use.
- Visibility - Home, Library recommended, and Friends checkboxes on every collection; changes apply to Plex right away (no need to run the collection again).
- Library Browser - Live view of all collections on your Plex server with the same visibility toggles. In Plex you can reorder and change options under Settings -> Manage -> Libraries -> Manage Recommendations.
- Auto-update (daily/weekly/manual), sync strict or append-only.
- Multi-library support - sync collections across multiple Plex libraries.
- System Health Bar - Compact status indicators on the dashboard for Plex, Radarr, Sonarr, and Cloud connections.
- One-Click Plex Linking - No more manual token entry! Link your account via official Plex OAuth and the app automatically discovers your server.
- One-Click Cloud Pairing - In Requests Settings, import the local app payload from the cloud website's Pairing Bootstrap Secret section, then run one-click pairing.
- Quick Tunnels - Enable instant cloud notifications (webhooks) without opening ports or setting up a reverse proxy. If instant delivery is unavailable, the app falls back to polling.
- Background Alias Discovery to reduce duplicate recommendations; blocklist for titles you never want to see.
- Ignore specific Plex users in recommendation history.
- Radarr/Sonarr for direct add; track past requests across both.
- Tautulli integration for trending on server.
- Backup/restore (including import); one-click updates for manual Docker installs (Unraid App Store installs update via App Store only).
- System logs and health for scans and scheduled jobs; multi-user accounts with admin controls; security safeguards for logins, forms, and file
▸ 展开全文
04-26 16:01 · 财经,财经
【摩尔线程：一季度营收同比增长155.35% 净利润扭亏为盈】摩尔线程（688795.SH）披露2026年一季报与2025年报。根据报告，2026年一季度，摩尔线程实现营收7.38亿元，同比增长155.35%；归母净利润0.29亿元，同比增加1.42亿元；归母扣非净利润亏损0.54亿元，亏损同比收窄60.10%。
摩尔线程（688795.SH）披露2026年一季报与2025年报。根据报告，2026年一季度，摩尔线程实现营收7.38亿元，同比增长155.35%；归母净利润0.29亿元，同比增加1.42亿元；归母扣非净利润亏损0.54亿元，亏损同比收窄60.10%。
摩尔线程2025年实现营收15.05亿元，同比增长243.37%；毛利总额达到9.87亿元，较上年同期增长218.43%；归母净利润、归母扣非净利润分别较上年同期亏损收窄38.16%、33.38%。扣除股份支付影响因素后，2025年净利润亏损6.48亿元，较上年同期收窄8.47亿元，收窄比例达56.65%。同时，摩尔线程保持了高强度的研发投入，全年研发费用13.05亿元，占比为86.68%。
随着AI算力需求激增，作为国产GPU龙头，摩尔线程商业化进程不断提速，2026年一季度斩获6.6亿元夸娥智算集群大单。同时，摩尔线程表示，公司是市场中为数不多的真正实现千卡级、万卡级大规模集群商业化应用落地的GPU供应商，公司的智算卡已在多家智算中心及云服务平台实现部署。基于新一代“花港”架构，公司正推进十万卡超大规模智算集群建设和超节点等核心技术研发。
（文章来源：科创板日报）
▸ 展开全文
04-26 16:01 · 科技,GitHub
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
04-26 16:01 · 科技,GitHub
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
04-26 16:00 · 科技,GitHub
Local-first dashboard for Codex sessions with trends, token usage, tools, and word cloud | Codex 本地数据分析仪表盘
Codex Viz is a local-first dashboard for exploring your Codex CLI sessions. It indexes your local JSONL history and turns it into clear trends, token usage, tool insights, and a focused word cloud.
If you want fast answers to “what did I do”, “how much did it cost”, and “where did my time go”, this is the smallest tool that feels big.
Codex Viz 是一个本地优先的 Codex CLI 会话可视化面板。它只读取你本机的 JSONL 历史，快速生成趋势、Token 使用、工具洞察与输入词云。
你想知道“我最近都在做什么”“消耗了多少 Token”“哪些工具最常用”，这里一眼就能看到。
- 本地索引：SQLite 缓存，快速查询
- 趋势看板：会话、消息、工具、Token 趋势
- Token 统计：基于
event_msg.token_count
的total_token_usage
增量累计，首条/重置时用last_token_usage
兜底- Prompt(含缓存) =
input_tokens
；cached_input_tokens
为其中命中缓存的子集
- Prompt(含缓存) =
- 词云：对 user 输入做轻量分词（英文词 + 中文 2/3-gram）
- 工具排行：Top 工具一眼可见
pnpm i
pnpm dev
打开 http://localhost:3000
CODEX_SESSIONS_DIR
：默认~/.codex/sessions
CODEX_VIZ_CACHE_DIR
：默认~/.codex-viz/cache
- 想追踪个人/团队在 Codex 上的投入与产出
- 需要快速复盘近期任务与工具使用偏好
- 希望用更直观的方式理解 Token 消耗
MIT License，详见 LICENSE
。
Codex Viz is a local-first dashboard for Codex CLI sessions. It reads your JSONL history on your machine and turns it into trends, token usage, tool insights, and a focused user-input word cloud.
If you want to quickly answer “what I worked on”, “how many tokens I used”, and “which tools I rely on”, this gives you that at a glance.
- Local indexing with SQLite for fast queries
- Trend chart for sessions, messages, tools, and tokens
- Token accounting from
event_msg.token_count
using incrementaltotal_token_usage
- Prompt (incl. cache) =
input_tokens
;cached_input_tokens
is a subset of it
- Prompt (incl. cache) =
- Lightweight word cloud from user input (EN tokens + ZH 2/3-gram)
- Top tools leaderboard
pnpm i
pnpm dev
Open http://localhost:3000
CODEX_SESSIONS_DIR
(default~/.codex/sessions
)CODEX_VIZ_CACHE_DIR
(default~/.codex-viz/cache
)
- Track personal/team Codex usage and trends
- Review recent work and tool preferences
- Make token usage more tangible
MIT License. See LICENSE
.
Partnership with https://llmapis.com - Discover more AI tools and resources
▸ 展开全文
04-26 16:00 · 科技,GitHub
Kakobuy Spreadsheet features 3,000+ trending products from Weidian, Taobao, and 1688, with affordable new arrivals added daily. Explore the latest finds from the past 7 days, plus shoes and clothing on MaisonLooks with QC photos and AI try-on. Order easily through your preferred agent.
All-in-One Spreadsheet for Kakobuy, ACbuy, Litbuy, OOPbuy, Sugargoo & Superbuy. Features 3000+ trending Chinese products from Weidian, Taobao & 1688 with daily updates. Browse premium finds on MaisonLooks with QC photos and AI try-on. Order easily via your preferred agent for the best shopping experience.
Discover over 3,000 trending Chinese products on our Master Spreadsheet, fully compatible with Kakobuy, ACbuy, Litbuy, OOPbuy, Sugargoo, and Superbuy. We feature daily affordable arrivals from Weidian, Taobao, and 1688, maintained with updates every 24 hours.
- Premium Selection: Browse the best finds and belts curated for all agents.
- Smart Shopping: Access high-quality QC photos and AI try-on technology.
- Daily Updates: See the latest additions from the past 7 days.
▸ 展开全文
04-26 16:00 · 科技,GitHub
Natively - Free open-source AI interview copilot & meeting assistant. The best Cluely alternative, Final Round AI alternative, and Interview Coder alternative. Real-time transcription, undetectable stealth mode, local RAG, BYOK. No subscriptions. No data breaches.
If you’re looking for a hosted desktop recording API, consider checking out Recall.ai, an API that records Zoom, Google Meet, Microsoft Teams, in-person meetings, and more.
The best free alternative to Cluely, Final Round AI, LockedIn AI, and Interview Coder.
Same UI as Cluely. More features. $0. Open source. No data breaches.
Competitors charge $20–$149/month, store your data on their servers, and one already breached 83,000 users. Natively costs $0, runs locally, and has never had a data breach. Your keys, your models, your machine.
Requires macOS 12+ (Apple Silicon & Intel) or Windows 10/11
👥 9,000+ Users · 🔥 700+ DAU · 💸 $0 vs $149/mo rivals · ⚡ <500ms latency · 🛡️ 0 data breaches
Natively started as a pixel-perfect recreation of Cluely's interface — then kept going. If you've used Cluely, you already know how to use Natively. Same overlay, same workflow, same shortcuts. Except it's free, open-source, runs locally, supports any LLM, and has never breached a single user's data.
Looking for a free Cluely alternative? A Cluely open-source clone? You found it.
"This is a fantastic piece of software and you should definitely keep up the great work! This is exactly what I was looking for. I started out trying the open-source version, and because it worked so well, I decided to go ahead and buy the full premium license."
— Oskar Krzak (⭐⭐⭐⭐⭐ via Gumroad)
"Natively is significantly faster than Cluely when it comes to response time and screen analysis. The latency is practically non-existent."
— Premium User
"Just wanted to say thanks! Natively helped me completely crack the first two rounds of my Software Engineering interviews. The responses were incredibly fast and accurate."
— Private Email Feedback
"Used the free version of Natively for my interviews and just landed a massive summer internship. It took all the stress out of the live coding and behavioral rounds!"
— Private Email Feedback
While other tools act as simple API wrappers, Natively is a complete, native intelligence system designed specifically for high-stakes meetings and interviews.
- Native Audio Capture (<500ms): Built with Rust and Zero-Copy ABI transfers, bypassing generic web-audio limitations for ultra-low latency.
- Dual-Channel Intelligence: Distinct pipelines for system audio (what they say) and your microphone (what you dictate) ensuring perfect transcription without room noise.
- Battle-Tested Stealth Mode: Completely undetectable. Hides from the dock, disables popups, and disguises the process during screen sharing.
- Rolling Context: We don't just transcribe; we maintain a "memory window" of the conversation for smarter answers.
- Local RAG Memory: We embed your meetings locally using SQLite vector search so you can ask, "What did John say about the API last week?"
- Custom Personas & Reference Docs: Switch between tailored AI roles (Tech, Sales, HR) and inject specific PDFs to give the AI your exact context.
- Rich Dashboard: A full UI to manage, search, and export your history—not just a floating window.
- Fully Offline Capable: Don't trust the cloud? Run Natively 100% offline using local Ollama models with limited anonymous telemetry.
- Cluely had a data breach in mid-2025 that exposed 83,000 users' personal info, transcripts, and screenshots — Natively stores everything locally by default with limited anonymous telemetry and has never had a breach.
- Final Round AI costs $149/month and its taskbar icon is visible to proctoring software — Natively is free, open-source, and has a battle-tested undetectable stealth mode.
- LockedIn AI charges $55–70/month and locks you into their cloud LLM with no local option — Natively lets you use any model (GPT, Claude, Gemini, Llama) or go fully offline with Ollama.
Every star pushes Natively higher in GitHub search, helping developers and job seekers find a free, private alternative instead of paying $149/month for tools that store their data on someone else's server.
This demo shows a complete live meeting scenario:
- Real-time transcription as the meeting happens
- Rolling context awareness across multiple speakers
- Screenshot analysis of shared slides
- Instant generation of what to say next
- Follow-up questions and concise responses
- All happening live, without recording or post-processing
Legend: ✅ Full support ·
⚠️ Partial or limited · ❌ Not available
The UI is intentionally familiar — if you've used Cluely, there's zero learning curve.
Cluely's mid-2025 data breach exposed personal information, full interview transcripts, and screenshots of 83,000 users. Every word spoken during an interview was stored on their servers — and then leaked. They charge $20/month for this privilege.
By default, Natively stores everything on your local machine, with only limited anonymous telemetry (basic GA4 install tracking, zero personal data). Your transcripts, API keys, and screenshots never leave your machine when using your own keys. The entire codebase is open-source (AGPL-3.0) and auditable. Zer
▸ 展开全文
04-26 16:00 · 科技,GitHub
Daily news and hot topics via the 6551 API. Supports news categories, hot news articles, and trending tweets by category.
Daily News MCP Server
News Categories · Hot News · Trending Tweets · Crypto Intelligence
claude mcp add daily-news \
-- uv --directory /path/to/daily-news run daily-news-mcp
Replace
/path/to/daily-news
with your local project path.
cp -r openclaw-skill/daily-news ~/.openclaw/skills/6551-daily-news
Not sure if this MCP is safe? Paste the prompt below to your AI assistant — it will review the source code first, then install if safe:
Copy this prompt and send it to your AI assistant (replace
<project-path>
with actual value):
Please review and install the daily-news-mcp MCP server for me. The project is at <project-path>.
Steps:
1. Review these files for security:
- src/daily_news_mcp/api_client.py — Confirm it only connects to ai.6551.io, no data sent elsewhere
- src/daily_news_mcp/config.py — Confirm no hardcoded secrets or leaks
- src/daily_news_mcp/tools.py — Confirm all tools only perform API queries, no file writes, command execution, or dangerous operations
- pyproject.toml — Confirm dependencies are only mcp, httpx, no suspicious packages
2. Tell me your conclusion: safe / risky / problematic, with specific reasons
3. If safe, run the install:
claude mcp add daily-news -- uv --directory <project-path> run daily-news-mcp
Once connected, just ask your AI assistant:
Also supports config.json
in the project root (env vars take precedence):
{
"api_base_url": "https://ai.6551.io",
"max_rows": 100
}
[
{
"key": "crypto",
"name": "Crypto",
"name_zh": "加密货币",
"description": "...",
"subcategories": [
{
"key": "defi",
"name": "DeFi",
"name_zh": "去中心化金融",
"description": "..."
}
]
}
]
{
"success": true,
"category": "crypto",
"subcategory": "defi",
"news": {
"success": true,
"count": 10,
"items": [
{
"id": 123,
"title": "...",
"source": "...",
"link": "https://...",
"score": 85,
"grade": "A",
"signal": "bullish",
"summary_zh": "...",
"summary_en": "...",
"coins": ["BTC", "ETH"],
"published_at": "2026-03-17T10:00:00Z"
}
]
},
"tweets": {
"success": true,
"count": 5,
"items": [
{
"author": "Vitalik Buterin",
"handle": "VitalikButerin",
"content": "...",
"url": "https://...",
"metrics": { "likes": 1000, "retweets": 200, "replies": 50 },
"posted_at": "2026-03-17T09:00:00Z",
"relevance": "high"
}
]
}
}
Other Clients — Manual Install (click to expand)
In all configs below, replace
/path/to/daily-news
with your actual local project path.
Edit config (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
, Windows: %APPDATA%\Claude\claude_desktop_config.json
):
{
"mcpServers": {
"daily-news": {
"command": "uv",
"args": ["--directory", "/path/to/daily-news", "run", "daily-news-mcp"]
}
}
}
~/.cursor/mcp.json
or Settings > MCP Servers:
{
"mcpServers": {
"daily-news": {
"command": "uv",
"args": ["--directory", "/path/to/daily-news", "run", "daily-news-mcp"]
}
}
}
~/.codeium/windsurf/mcp_config.json
:
{
"mcpServers": {
"daily-news": {
"command": "uv",
"args": ["--directory", "/path/to/daily-news", "run", "daily-news-mcp"]
}
}
}
cd /path/to/daily-news
uv sync
uv run daily-news-mcp
# MCP Inspector
npx @modelcontextprotocol/inspector uv --directory /path/to/daily-news run daily-news-mcp
├── README.md # English
├── docs/
│ ├── README_ZH.md # 中文
│ ├── README_JA.md # 日本語
│ └── README_KO.md # 한국어
├── openclaw-skill/daily-news/ # OpenClaw Skill
├── pyproject.toml
├── config.json
└── src/daily_news_mcp/
├── server.py # Entry point
├── app.py # FastMCP instance
├── config.py # Config loader
├── api_client.py # HTTP client
└── tools.py # 2 tools
MIT
▸ 展开全文
04-26 16:00 · 开源,加密货币,数据分析,桌面工具,趋势检测
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
04-26 16:00 · 开源项目,AI自动化,内容生成,微信生态,GitHub Trending
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