04-26 16:03 · 科技,GitHub
50 AI agent skills for affiliate marketing. Research trending content, write data-backed posts, generate infographics, build landing pages, deploy — full flywheel with social intelligence. Works with Claude Code, Pi, ChatGPT, Gemini, Cursor, Windsurf, any AI.
Turn any AI into your affiliate marketing team.
52 AI-powered skills across 8 stages with a closed-loop flywheel. Research programs, scout trending content, write data-backed posts, generate infographics, build pages, deploy, track, optimize, scale — with any AI agent.
Works with: Claude Code · Pi · ChatGPT · Gemini CLI · Cursor · Windsurf · OpenClaw · any AI that reads text
skills/{stage}/{skill-name}/SKILL.md
— main skill definitionsshared/references/
— shared doctrine, compliance, and flywheel referencestools/src/
— affiliate-check CLI sourceregistry.json
— machine-readable skill catalogevals/
— evaluation cases and results
# Claude Code / Pi (recommended)
npx skills add Affitor/affiliate-skills
# Or clone manually
git clone https://github.com/Affitor/affiliate-skills.git ~/.claude/skills/affiliate-skills
cd ~/.claude/skills/affiliate-skills && ./setup
# OpenClaw / ClawHub
clawhub install affiliate-skills
# Cursor / Windsurf
npx skills add Affitor/affiliate-skills
Paste this into any AI:
Search the Affitor affiliate directory for AI video tools.
Use this API: GET https://list.affitor.com/api/v1/programs?q=AI+video&sort=top&limit=5
Show me the results in a table with: Name, Reward Value, Cookie Days, Stars.
Then recommend the best one and explain why.
- You Google "best affiliate programs" and get SEO spam written to rank, not to help
- You write content from gut feeling with no idea what format actually performs
- You have no data on what's trending, what hooks work, or what gaps exist
- You spend 4 hours on a landing page that converts at 0.2%
- You pick programs by vibes instead of data
S1 RESEARCH ──▶ S2 CONTENT ──▶ S3 BLOG & SEO ──▶ S4 OFFERS & LANDING
▲ │
│ ▼
│ S5 DISTRIBUTION
│ │
└──────────── S6 ANALYTICS ◀─────────────────────────┘
│
▼
S7 AUTOMATION → SCALE
│
S8 META (across all)
Closed loop. S6 Analytics feeds BACK to S1 Research. Every skill knows what comes next (chain_metadata.suggested_next
) and what feeds it. Data flows forward through the funnel and back through analytics.
Data-driven. S1 skills don't just find programs — they scout what content is actually performing (views, likes, shares), analyze traffic, and rank angles by real engagement data. By the time you write, you already know what format, hook, and platform to use.
Research-backed. Content skills don't write from thin air. content-research-brief
collects real source articles first. Your posts have real stats, real quotes, and current information.
You: I want to promote AI video tools. What's trending?
AI: [runs trending-content-scout]
Scanned 47 videos across YouTube + TikTok.
Top format: comparison (45% of top content, avg engagement: 35.2)
Best hook: "I replaced my $5K video team with AI" (engagement: 42.3)
Gap: nobody comparing HeyGen vs Synthesia on TikTok
Benchmark: median 18K views, top 10% needs 85K+
You: What's the best program to promote?
AI: [runs affiliate-program-search + traffic-analyzer]
Name Commission Cookie Stars Traffic Score
──────────────────────────────────────────────────────────────
HeyGen 30% recurring 60 days ★ 127 82/100
Synthesia 25% one-time 30 days ★ 84 76/100
D-ID 20% recurring 45 days ★ 52 61/100
HeyGen wins: highest commission, recurring, longest cookie,
strongest website traffic (2.1M monthly visits, 35% direct).
You: Research the latest about HeyGen for a LinkedIn post.
AI: [runs content-research-brief]
📚 7 sources collected (5 fully fetched)
🏷️ Tags: AI (7), Funding (2), Tools (5)
Key stats:
- "$60M Series A at $500M valuation" — TechCrunch
- "40,000+ businesses use HeyGen" — company blog
- "Avatar 3.0 launched with real-time lip sync" — The Verge
3 angles generated:
1. "HeyGen just raised $60M — here's what it means for AI video"
2. "I tested HeyGen vs Synthesia for 30 days — honest comparison"
3. "AI video tools are killing the $45B production industry"
You: Write angle 2 as a LinkedIn post with an infographic.
AI: [runs viral-post-writer + infographic-generator]
Post: 1,200-char comparison post, bold claim hook,
soft CTA in comments, FTC disclosure.
Infographic: side-by-side comparison card (1080×1350),
HeyGen vs Synthesia — commission, features, pricing.
Dark modern palette, self-contained HTML ready to render.
You: Now a TikTok script.
AI: [runs tiktok-script-writer]
45-second script, demo_first hook (backed by scout data):
"Watch me create a $2,000 video for free in 30 seconds."
Hook → Pain → Demo → Result → CTA. Bio link reminder.
You: Plan my full funnel — I have 5 hours/week.
AI: [runs funnel-planner]
Week 1: Scout + Research → find winning angles + collect sources
Week 2: Content → 5 LinkedIn posts + 2 TikToks + 2 infographics
Week 3: Blog → comparison article backed by research brief
Week 4: Deploy → landing page + bio link live
Week 5: Analytics → compare your metrics vs scout benchmark
Week 6: Optimize → double down on what beat the benchmark
Target: first affiliate click by week 2, first commission by week 5.
Affiliates — Find programs, create content, track performance. Data-dr
▸ 展开全文
04-26 16:03 · 开源,AI内容代理,自动化流程,GitHub Trending,嵌入式AI
Sage — X content agent for OpenClaw. Voice calibration, tweet drafting, reactive takes, thread writing, trend scouting, feedback loop. Self-installs in 5 minutes.
Sage is an AI agent that handles your X/Twitter content strategy end-to-end. Voice calibration. Trend scouting. Tweet drafting. Thread writing. Performance tracking. Feedback loop.
Built with OpenClaw. Self-installs in under 5 minutes.
- 0 to 172K impressions on a single tweet using Sage's reactive format
- Went from spending 2+ hours per day on content to 20 minutes reviewing and approving drafts
- Consistent posting without burnout - Sage handles the heavy lifting, you stay in the loop
- OpenClaw installed
- Any supported AI model (Claude, GPT-4o, Gemini - all work)
- An X/Twitter account (any size, any niche)
Just tell your OpenClaw agent:
Install this skill: https://github.com/sharbelxyz/sage-x-agent
OpenClaw will clone the repo, install the skill, and Sage will run onboarding automatically.
git clone https://github.com/sharbelxyz/sage-x-agent.git ~/clawd/skills/sage-x-agent
Then tell your OpenClaw:
Install Sage
Sage will detect the new skill and walk you through 10 onboarding questions.
First time you run Sage, she will ask 10 questions:
- Your name and X handle
- Your niche (one sentence)
- Your target audience
- Your goal and deadline
- Current follower count and avg impressions
- How you naturally write (voice description)
- Your 2-3 best tweets (optional - for voice calibration)
- 5-10 accounts in your space to monitor
- What to avoid (flops, off-brand formats, topics you hate)
- Whether you want reactive mode on (Sage surfaces trending topics for you)
Takes about 5 minutes. Sage writes your answers to config.md
automatically. You never do this again.
After setup, just talk naturally:
Sage, draft me a tweet about what I just shipped
Sage, react to this - [paste trending tweet or article]
Sage, write a thread on why most people use AI wrong
Sage, analyze my last 10 tweets and tell me what's working
Sage, what's trending in my niche right now?
Sage, give me a daily batch - 3 tweets for today
Sage, show me the feedback loop - what patterns have you noticed?
Sage's most powerful feature. Give it a trending topic and it will:
- Hook: name what just happened (specific, not vague)
- Add insight the original did not have
- Translate it: "here is what this means for you" or "here is how to use this today"
- Close with your POV and one concrete action
This format consistently outperforms generic commentary because it teaches, not just reports.
Example reactive structure:
[what just happened, specific and named]
here is what this means for you:
- [practical insight 1]
- [practical insight 2]
- [practical insight 3]
[your POV + one thing to do today]
Sage keeps a memory/
folder with:
approved-drafts.md
- every draft you said yes torejected-drafts.md
- every draft you rejected and whyperformance-log.md
- stats after tweets go livevoice-examples.md
- your actual phrases, rhythms, and patternstrend-log.md
- trends surfaced and when
Before every session, Sage reads all of these. She never repeats a rejected angle. She weights suggestions toward what has actually worked. The longer you use her, the better the drafts.
sage-x-agent/
├── README.md <- You are reading this
├── SKILL.md <- Sage's full instructions (all 8 systems)
├── config.md <- Auto-created during onboarding (gitignored)
├── example-config.md <- Sample config for reference
└── memory/
├── approved-drafts.md <- Auto-populated (gitignored)
├── rejected-drafts.md <- Auto-populated (gitignored)
├── performance-log.md <- Auto-populated (gitignored)
├── voice-examples.md <- Auto-populated (gitignored)
└── trend-log.md <- Auto-populated (gitignored)
Note: config.md
and all memory/
files are gitignored. Your personal data never leaves your machine.
- To change your X profile details: edit
config.md
- To change how Sage writes or behaves: edit
SKILL.md
(plain English, no code) - To reset and start fresh: delete
config.md
and allmemory/
files, then run "Install Sage" again
Sage writes every tweet with line breaks and breathing room.
Good:
this is the hook.
this is the build.
this is the punchline.
Not good:
this is the hook. this is the build. this is the punchline all in one dense block that nobody reads.
Every line earns the next one. Walls of text get skipped. Sage knows this and applies it to every draft, always.
Does this work for any niche? Yes. Onboarding calibrates Sage to your niche, voice, and the accounts you watch.
Do I need to be technical? No. If you can install OpenClaw and answer 10 questions, you are set.
Does Sage post automatically? No. Sage drafts and queues. You review and approve before anything goes out.
Is my data private?
Yes. config.md
and all memory/
files are gitignored and stay on your machine.
What model works best? Claude Sonnet or GPT-4o. Both work well. Sonnet tends to nail conversational voice a bit better.
Can I use this for LinkedIn or other platforms? The SKILL.md is plain text. You can fork it and adapt the formatting rules and output format for any platform.
Built by Sharbel - founder, AI builder, operator.
Video that goes
▸ 展开全文
04-26 16:03 · 科技,GitHub
Phantom Wallet Trending for your Solana token — Top 1-10 placement. From $1,200/6h. Instant delivery via PandaBoost API.
Get your token to Top 1–10 trending on Phantom Wallet — the #1 Solana wallet with 10M+ users.
Promote your token directly inside Phantom's trending section. Fast delivery, stealth mode, full refund guarantee.
- Platform: Phantom Wallet (phantom.app)
- Placement: Top 1–10 Trending
- Duration options: 6h · 12h · 24h
- Chain: Solana
- Start time: Instant (within 60 seconds)
- Guarantee: Full refund if delivery fails
- Go to web.pandaboost.app
- Connect your wallet (EVM via WalletConnect or Solana)
- Add funds via crypto
- Open New Order → Phantom → Wallet Trending
- Select duration (6h / 12h / 24h)
- Paste your token address → confirm
Open @pandaboostbot → New Order → Phantom → Wallet Trending
Order Phantom Wallet Trending programmatically via the PandaBoost API.
Base URL: https://web.pandaboost.app/api/v1
Auth: x-api-key: pb_...
header
import requests
API_KEY = "pb_your_api_key"
BASE_URL = "https://web.pandaboost.app/api/v1"
HEADERS = {"x-api-key": API_KEY, "Content-Type": "application/json"}
order = requests.post(f"{BASE_URL}/orders", headers=HEADERS, json={
"serviceName": "phantom-wallet-trending",
"orderTypeId": "12h", # 6h | 12h | 24h
"quantity": 1,
"fields": {
"token_address": "YOUR_TOKEN_ADDRESS",
"chain": "solana"
}
})
print(order.json())
# {"success": true, "data": {"order": {"publicId": "ABCD1234", "status": "processing"}, "newBalance": "..."}}
const res = await fetch("https://web.pandaboost.app/api/v1/orders", {
method: "POST",
headers: { "x-api-key": "pb_your_api_key", "Content-Type": "application/json" },
body: JSON.stringify({
serviceName: "phantom-wallet-trending",
orderTypeId: "24h", // 6h | 12h | 24h
quantity: 1,
fields: {
token_address: "YOUR_TOKEN_ADDRESS",
chain: "solana"
}
})
});
const data = await res.json();
console.log(data.data.order.publicId);
curl -X POST -H "x-api-key: pb_..." \
-H "Content-Type: application/json" \
-d '{
"serviceName": "phantom-wallet-trending",
"orderTypeId": "24h",
"quantity": 1,
"fields": {
"token_address": "ErKGCuxZeMQoS3VhFJZ77giESQphJhGpYDaUJmhApump",
"chain": "solana"
}
}' \
https://web.pandaboost.app/api/v1/orders
curl -H "x-api-key: pb_..." \
https://web.pandaboost.app/api/v1/orders/ABCD1234
Full catalog → web.pandaboost.app
- 🌐 Web Panel — web.pandaboost.app
- 🤖 Telegram Bot — @pandaboostbot
- 📚 Docs & API — docs.pandaboost.app
- 💬 Support — @pandaboosthelp
- 📧 Email — support@pandaboost.app
⭐ Star this repo if it helped you. PRs and issues welcome.
▸ 展开全文
04-26 16:03 · 科技,GitHub
Get your token to DEX Screener TOP 1-10. Automated trending service for Solana, Ethereum, BSC and Base via PandaBoost API. SOL from $1,200/12h · ETH/BSC/Base from $1,000/24h. Instant start, full refund guaranteed. Web panel + Telegram bot + REST API.
Get your token to DEX Screener TOP 1–10 — Solana, Ethereum, BSC, Base and more
Push your token into the DEX Screener trending section and get seen by thousands of traders actively looking for the next gem.
- Result: TOP 1–10 on DEX Screener trending
- Start time: within 60 seconds of order confirmation
- Guarantee: Full refund if delivery fails
- Stealth mode: all activity looks organic
- Go to web.pandaboost.app
- Connect your wallet (EVM via WalletConnect or Solana)
- Add funds via crypto
- Open New Order → DEX Screener → Trending
- Select package (12h or 24h)
- Paste your pair contract address → confirm
Open @pandaboostbot → New Order → DEX Screener → Trending
Order DEX Screener trending programmatically via the PandaBoost API.
Base URL: https://web.pandaboost.app/api/v1
Auth: x-api-key: pb_...
header
import requests
API_KEY = "pb_your_api_key"
BASE_URL = "https://web.pandaboost.app/api/v1"
HEADERS = {"x-api-key": API_KEY, "Content-Type": "application/json"}
# 12h Trending — Solana ($1,200)
order = requests.post(f"{BASE_URL}/orders", headers=HEADERS, json={
"serviceName": "dex-trending",
"orderTypeId": "dex-trending-12h", # dex-trending-12h | dex-trending-24h
"quantity": 1,
"fields": {
"pair_address": "YOUR_PAIR_CONTRACT_ADDRESS"
}
})
print(order.json())
# {"success": true, "data": {"order": {"publicId": "V3BH8YCS", "status": "processing"}, "newBalance": "..."}}
const res = await fetch("https://web.pandaboost.app/api/v1/orders", {
method: "POST",
headers: { "x-api-key": "pb_your_api_key", "Content-Type": "application/json" },
body: JSON.stringify({
serviceName: "dex-trending",
orderTypeId: "dex-trending-24h", // dex-trending-12h | dex-trending-24h
quantity: 1,
fields: {
pair_address: "0xYOUR_PAIR_CONTRACT_ADDRESS"
}
})
});
const data = await res.json();
console.log(data.data.order.publicId);
curl -X POST -H "x-api-key: pb_..." \
-H "Content-Type: application/json" \
-d '{
"serviceName": "dex-trending",
"orderTypeId": "dex-trending-12h",
"quantity": 1,
"fields": {
"pair_address": "0xe945683b3462d2603a18bdfbb19261c6a4f03ad1"
}
}' \
https://web.pandaboost.app/api/v1/orders
curl -H "x-api-key: pb_..." \
https://web.pandaboost.app/api/v1/balance
# {"success": true, "data": {"balance": "1250.00000000", "currency": "USD"}}
Full catalog → web.pandaboost.app
- 🌐 Web Panel — web.pandaboost.app
- 🤖 Telegram Bot — @pandaboostbot
- 📚 Docs & API — docs.pandaboost.app
- 💬 Support — @pandaboosthelp
- 📧 Email — support@pandaboost.app
⭐ Star this repo if it helped you. PRs and issues welcome.
▸ 展开全文
04-26 16:03 · 科技,GitHub
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
04-26 16:02 · 科技,GitHub
Phantom Chat Trending for your Solana token — reach active traders in Phantom chat. From $500/6h. Instant delivery via PandaBoost API.
Get your token trending in Phantom Chat — reach thousands of active Solana traders directly.
Promote your token inside Phantom's built-in chat and discovery feed. Fast delivery, stealth mode, full refund guarantee.
- Platform: Phantom Wallet Chat (phantom.app)
- Placement: Chat Trending Feed
- Duration options: 6h · 12h · 24h
- Chain: Solana
- Start time: Instant (within 60 seconds)
- Guarantee: Full refund if delivery fails
- Go to web.pandaboost.app
- Connect your wallet (EVM via WalletConnect or Solana)
- Add funds via crypto
- Open New Order → Phantom → Chat Trending
- Select duration (6h / 12h / 24h)
- Paste your token address → confirm
Open @pandaboostbot → New Order → Phantom → Chat Trending
Order Phantom Chat Trending programmatically via the PandaBoost API.
Base URL: https://web.pandaboost.app/api/v1
Auth: x-api-key: pb_...
header
import requests
API_KEY = "pb_your_api_key"
BASE_URL = "https://web.pandaboost.app/api/v1"
HEADERS = {"x-api-key": API_KEY, "Content-Type": "application/json"}
order = requests.post(f"{BASE_URL}/orders", headers=HEADERS, json={
"serviceName": "phantom-chat-trending",
"orderTypeId": "12h", # 6h | 12h | 24h
"quantity": 1,
"fields": {
"token_address": "YOUR_TOKEN_ADDRESS",
"chain": "solana"
}
})
print(order.json())
# {"success": true, "data": {"order": {"publicId": "ABCD1234", "status": "processing"}, "newBalance": "..."}}
const res = await fetch("https://web.pandaboost.app/api/v1/orders", {
method: "POST",
headers: { "x-api-key": "pb_your_api_key", "Content-Type": "application/json" },
body: JSON.stringify({
serviceName: "phantom-chat-trending",
orderTypeId: "24h", // 6h | 12h | 24h
quantity: 1,
fields: {
token_address: "YOUR_TOKEN_ADDRESS",
chain: "solana"
}
})
});
const data = await res.json();
console.log(data.data.order.publicId);
curl -X POST -H "x-api-key: pb_..." \
-H "Content-Type: application/json" \
-d '{
"serviceName": "phantom-chat-trending",
"orderTypeId": "24h",
"quantity": 1,
"fields": {
"token_address": "ErKGCuxZeMQoS3VhFJZ77giESQphJhGpYDaUJmhApump",
"chain": "solana"
}
}' \
https://web.pandaboost.app/api/v1/orders
curl -H "x-api-key: pb_..." \
https://web.pandaboost.app/api/v1/orders/ABCD1234
Full catalog → web.pandaboost.app
- 🌐 Web Panel — web.pandaboost.app
- 🤖 Telegram Bot — @pandaboostbot
- 📚 Docs & API — docs.pandaboost.app
- 💬 Support — @pandaboosthelp
- 📧 Email — support@pandaboost.app
⭐ Star this repo if it helped you. PRs and issues welcome.
▸ 展开全文
04-26 16:02 · 科技,GitHub
Discover and compare open-source Agent Skills, tools & MCP servers — with quality scoring, trending analysis, and automated GitHub sync
AgentSkillsHub (Agent Skills Hub) is the open-source directory for Claude Skills, MCP Servers, Codex Skills, and AI agent tools. 62,000+ projects · quality-scored on 10 dimensions · refreshed every 8 hours.
🌐 Live: agentskillshub.top · 📰 Newsletter · 𝕏 Follow
AgentSkillsHub — also referred to as Agent Skills Hub, the Claude Skills Hub, or the Claude Skills Marketplace / Library — is a continuously updated directory that collects every meaningful open-source AI agent skill, MCP server, Codex skill, AI coding assistant, and agent framework from GitHub. Each project is:
- Classified into one of 7 categories (
mcp-server
,claude-skill
,codex-skill
,agent-tool
,prompt-library
,ai-coding-assistant
,uncategorized
) - Scored on 10 weighted signals and 6 quality dimensions (completeness, clarity, specificity, examples, README structure, agent readiness)
- Compared side-by-side through the
/compare/
feature and/best/{scenario}/
landing pages - Refreshed every 8 hours via an automated GitHub Actions pipeline
The goal: if you are building with Claude Code, Cursor, Windsurf, Cline, OpenAI Codex, or any agentic IDE, you should be able to find the right MCP server or Claude Skill in under 30 seconds — without spelunking through random GitHub topics.
Mirror URLs that refer to the same project: agentskillshub.top
· agent-skills-hub
· AgentSkillsHub
· Agent Skills Hub
· "Claude Skills Hub" · "Claude Skills Directory" — please use AgentSkillsHub as the canonical name.
GitHub API ──▸ Collection ──▸ Cleaning ──▸ Evaluation ──▸ Scoring ──▸ Presentation
(6 phases) (classify) (6 dimensions) (0-100) (Web + API)
Every 8 hours, GitHub Actions triggers a 6-phase sync:
- Incremental sync on weekdays (only new/updated repos), full sync on Sundays
- Smart rate-limit handling: waits for actual GitHub reset time
- Deduplication by
repo_full_name
- Keyword-based category classification:
mcp-server
·claude-skill
·codex-skill
·agent-tool
·ai-skill
·llm-plugin
·youmind-plugin
- Project type inference (framework / skill / tool)
- Size categorization (micro ≤50KB → large >5MB)
Quality Analysis — 6 dimensions (services/quality_analyzer.py
):
Composite Score — 9 weighted signals (services/scorer.py
):
Also: Platform Inference (17 platforms), Token Estimation, Composability (TF-IDF + 8-signal skill pairing).
Dual-mode API client: Production uses Supabase PostgREST + RPCs directly; dev uses FastAPI backend.
Key optimizations:
get_landing_data()
RPC replaces 7+ API calls- Database views:
v_trending
,v_top_rated
,v_rising
, etc. - Full-text search via PostgreSQL tsvector + GIN index
- HTTP caching: 5min (trending) → 1hr (stats)
cd backend
python3.12 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # Add GITHUB_TOKEN
uvicorn app.main:app --reload
cd frontend
npm install
npm run dev
┌─────────── GitHub Actions ───────────┐
│ sync.yml (8h) · deploy.yml · newsletter.yml (Mon) │
└──────┬──────────────┬────────────────┬──┘
▼ ▼ ▼
Supabase GitHub Pages Resend API
(PostgreSQL) (React SPA) (Emails)
GITHUB_TOKEN=ghp_xxx # GitHub API (required)
SUPABASE_DB_URL=postgresql:// # Production database
RESEND_API_KEY=re_xxx # Email service
ADMIN_TOKEN=sk-xxx # Admin API auth
SYNC_INTERVAL_HOURS=8 # Sync frequency
- Scoring Algorithm — Full scoring design
- System Architecture — Detailed pipeline reference
▸ 展开全文
04-26 16:02 · 科技,GitHub
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
04-26 16:02 · 科技,GitHub
自动发布 24 小时情报系统：好文章的 80% 在于选题。Hunter AI 自动从 GitHub Trending、Twitter、HackerNews Reddit 等平台采集热点，用 AI 判断哪些值得写，自动生成文章和插图，自动发布。
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
空白电脑双击即可运行，自动下载 Python + 所有依赖！
# Mac / Linux
bash run.sh
# Windows（双击运行）
run.bat
首次运行需下载环境约 3-5 分钟，之后秒启动
启动后在 Web UI 的「⚙️ 配置」中填入 Gemini API Key，点击保存即可。
# CLI 运行
uv run hunter run -t github # GitHub 模板
uv run hunter run -t pain # 痛点诊断
uv run hunter run --dry-run # 试运行，不推送
┌─────────────────────────────────────────────────────────────────┐
│ Hunter AI 6-Skill 数据流 │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Topic ──→ Research ──→ Structure ──→ Write ──→ Package ──→ Publish
│ 选题判断 深度调研 结构设计 内容写作 封装优化 发布推送
│ │
│ 数据源 ──────────→ AI 分析 ──────────→ 内容输出 ──────────→ 推送
│ GitHub/Twitter/ Gemini 2.0 Markdown PushPlus
│ Reddit/HN/小红书 Flash 公众号文章 微信通知
│ │
└─────────────────────────────────────────────────────────────────┘
# config.yaml
gemini:
api_key: "你的 Gemini API Key"
📝 完整配置示例
# AI 大模型配置（必填）
gemini:
api_key: "你的 API Key"
model: "gemini-2.0-flash"
# GitHub 配置
github:
token: "ghp_xxx" # 可选，提高配额
min_stars: 200
# Twitter 配置
twitter:
cookies_path: "data/cookies.json"
# 小红书配置
xiaohongshu:
cookies: "" # 浏览器 F12 复制
# 推送配置
pushplus:
token: "你的 Token"
enabled: true
# 公众号人设
account:
name: "AI技术前沿"
tone: "专业且引人入胜"
niche: "AI技术"
hunter-ai-content-factory/
├── src/
│ ├── intel/ # 📡 数据采集层
│ ├── templates/ # 📋 内容模板
│ ├── factory/ # 🏭 内容生产
│ └── utils/ # 🔧 工具函数
├── data/ # 数据存储
├── output/ # 输出目录
└── config.yaml # 配置文件
推送成功但公众号没有文章？
PushPlus 推送到的是个人微信聊天窗口，不是公众号。
查看：微信 → 「PushPlus 推送加」公众号 → 聊天窗口
发布：从 output/
目录复制文章，手动发布到公众号
GitHub API 返回 403？
API 限流，配置 github.token
提高配额（60→5000 次/小时）
小红书/Twitter 采集失败？
需要配置对应平台的 Cookie，详见配置说明
欢迎提交 Issue 和 Pull Request！详见 贡献指南
▸ 展开全文
04-26 16:01 · 科技,GitHub
Official repository for "Survey on AI Memory: Theories, Taxonomies, Evaluations, and Emerging Trends".
- [2026/01/15] 🚀 Project Website: https://baijia.online/homepage/memory_survey.html
- [2026/01/15] 🚀 Full survey released! We establish a unified framework for AI memory mechanisms.
- [2026/01/07] 🚀 Project repository initialized.
This survey presents a comprehensive overview of AI memory mechanisms anchored in a unified theoretical framework. We propose a structured "4W Memory Taxonomy" to enable consistent analysis across diverse architectures. Building on this foundation, we systematically review memory systems in both single- and multi-agent contexts, examining their architectures, functions, applications, and evaluation methodologies. By synthesizing cognitive theories with engineering benchmarks, this work provides a coherent roadmap for advancing the theoretical understanding and technological development of AI memory.
To clarify the scope of AI memory, we distinguish between three interrelated layers:
- LLM Memory: The low-level computational kernel for prediction, consisting of Parametric Weights (static) and Context Window (runtime).
- Agent Memory: The functional workflow supporting autonomous operation and complex task execution via perception-planning-action loops.
- AI Memory: The overarching cognitive concept aimed at lifelong evolution, long-term persistence, and adaptation.
We establish a structured 4W Memory Taxonomy to enable consistent analysis across diverse architectures:
- When (Lifecycle Dimension): Examines the temporal span of memory, including Transient (extremely short-lived), Session (task duration), and Persistent (cross-session retention).
- What (Type Dimension): Categorizes by the nature of stored information, including Procedural (skills), Declarative (facts), Metacognitive (reflections), and Social/Personalized (user models).
- How (Storage Dimension): Explores technical implementation, from Implicit Storage (Parametric/Latent) within model weights to Explicit Storage (Raw Text, Vector DB, or Structured Graphs) outside the model.
- Which (Modality Dimension): Classifies by information formats, covering Single-modal (text-only) and Multimodal (fusing images, audio, and video).
Effective collaboration within MAS hinges on communication mediated by memory sharing. We organize these mechanisms into two core dimensions:
- Communication Mechanisms: Ranges from Explicit Communication (interpretable symbols like natural language or structured schemas) to Implicit Communication (latent representations/hidden embeddings).
- Memory Sharing Mechanisms: Categorized into Task-Level (experience accumulation and knowledge transfer) and Step-Level (precise context allocation and role-aware filtering).
We categorize memory evaluation into four essential dimensions to provide a structured assessment of agent memory:
If you find this survey or the established taxonomy helpful in your research, please cite our work
@article{baisurvey,
title={Survey on AI Memory: Theories, Taxonomies, Evaluations, and Emerging Trends},
author={BAI, TING and FAN, JIAYANG and WEN, XIAOSHUAI and KANG, JIAZHENG and LAN, HENGZHI and ZHAO, RUOCHEN and WU, PINGZHENG and ZHANG, ZEPENG and ZHONG, YUTIAN and LI, GEZI and others}
}
▸ 展开全文