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adynato-cloudflare阿迪纳托 Cloudflare

Agent Skill

adynato-cloudflare 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

245

周安装

10

GitHub Stars

1

下载量

78
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:adynato-cloudflare(阿迪纳托 Cloudflare)
来源仓库:https://github.com/adynato/skills
仓库路径:skills/adynato-cloudflare
安装命令:
npx skills add https://github.com/adynato/skills --skill adynato-cloudflare
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/adynato/skills --skill adynato-cloudflare

简介

用于部署和管理 Cloudflare Workers 或 Pages 项目,支持日志调试。

  • 提供 wrangler CLI 集成,可读取实时日志并按状态、IP 或搜索词过滤。
  • 通过 GitHub 安装,需配置 CLOUDFLARE_API_TOKEN 或使用交互式登录。
  • 建议在 CI/CD 环境中使用 API token,并检查 .vercel/project.json 获取项目 ID。
  • adynato-cloudflare 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Cloudflare Skill

Use this skill when deploying Adynato projects to Cloudflare Workers or Pages.

Wrangler CLI

Installation

npm install -g wrangler

# Or use npx
npx wrangler <command>

Authentication

# Interactive login (opens browser)
wrangler login

# Check auth status
wrangler whoami

# Use API token (CI/CD)
export CLOUDFLARE_API_TOKEN="your-token"

Reading Logs for Debugging

Tail Live Logs

# Stream logs from production
wrangler tail

# Stream logs from specific environment
wrangler tail --env staging

# Filter by status
wrangler tail --status error

# Filter by search term
wrangler tail --search "user-123"

# Filter by IP
wrangler tail --ip 192.168.1.1

# JSON output for parsing
wrangler tail --format json

Log Output Format

GET https://example.com/api/users - Ok @ 1/17/2026, 10:30:00 AM
  (log) Processing request for user-123
  (error) Database connection failed

Adding Console Logs

// Workers log to wrangler tail
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    console.log('Request received:', request.url)
    console.log('Headers:', Object.fromEntries(request.headers))

    try {
      const result = await doSomething()
      console.log('Result:', JSON.stringify(result))
      return Response.json(result)
    } catch (error) {
      console.error('Error:', error.message, error.stack)
      return Response.json({ error: 'Internal error' }, { status: 500 })
    }
  }
}

Debugging Tips

  1. Always log request context first ` console.log([${request.method}] ${new URL(request.url).pathname}) `
  2. Log before and after async operations console.log('Fetching from KV...') const value = await env.MY_KV.get(key) console.log('KV result:', value? 'found': 'not found')
  3. Use structured logging console.log(JSON.stringify({type: 'request', path: url.pathname, method: request.method, timestamp: Date.now()}))

Deployment

Deploy Worker

# Deploy to production
wrangler deploy

# Deploy to specific environment
wrangler deploy --env staging

# Dry run (see what would be deployed)
wrangler deploy --dry-run

# Deploy specific script
wrangler deploy src/worker.ts

Deploy Pages

# Deploy to Pages
wrangler pages deploy ./dist

# Deploy with specific project
wrangler pages deploy ./dist --project-name=my-site

# Deploy to specific branch
wrangler pages deploy ./dist --branch=preview

Configuration

wrangler.toml

name = "my-worker"
main = "src/index.ts"
compatibility_date = "2026-01-17"

# Environment variables (non-secret)
[vars]
API_URL = "https://api.example.com"
NODE_ENV = "production"

# KV Namespaces
[[kv_namespaces]]
binding = "MY_KV"
id = "abc123"

# D1 Databases
[[d1_databases]]
binding = "DB"
database_name = "my-database"
database_id = "def456"

# R2 Buckets
[[r2_buckets]]
binding = "BUCKET"
bucket_name = "my-bucket"

# Durable Objects
[[durable_objects.bindings]]
name = "MY_DO"
class_name = "MyDurableObject"

# Staging environment
[env.staging]
name = "my-worker-staging"
vars = { API_URL = "https://staging-api.example.com" }

[[env.staging.kv_namespaces]]
binding = "MY_KV"
id = "staging-kv-id"

Secrets

# Add secret (interactive)
wrangler secret put MY_SECRET

# Add secret from stdin
echo "secret-value" | wrangler secret put MY_SECRET

# Add to specific environment
wrangler secret put MY_SECRET --env staging

# List secrets
wrangler secret list

# Delete secret
wrangler secret delete MY_SECRET

Common Errors

"No account id found"

Error: No account id found, quitting...

Fix: Add account_id to wrangler.toml or login:

wrangler login
# or
wrangler whoami  # to verify auth
# wrangler.toml
account_id = "your-account-id"

"Worker not found"

Error: worker not found

Fix: Check worker name matches wrangler.toml:

# List all workers
wrangler deployments list

# Check wrangler.toml name field

"KV namespace not found"

Error: namespace not found

Fix: Create the namespace first:

# Create KV namespace
wrangler kv:namespace create MY_KV

# Use the returned id in wrangler.toml

"Script too large"

Error: Script startup exceeded CPU time limit

Fix:

  • Bundle size limit is 10MB (25MB on paid)
  • Check for large dependencies
  • Use dynamic imports for rarely-used code
# Check bundle size
wrangler deploy --dry-run --outdir=./dist
ls -la ./dist

"Binding not found"

Error: Cannot find binding "MY_KV"

Fix: Ensure binding is in wrangler.toml and matches code:

// Code expects env.MY_KV
interface Env {
  MY_KV: KVNamespace  // Must match wrangler.toml binding
}

KV Storage

# Create namespace
wrangler kv:namespace create MY_KV

# List namespaces
wrangler kv:namespace list

# Put value
wrangler kv:key put --binding=MY_KV "my-key" "my-value"

# Get value
wrangler kv:key get --binding=MY_KV "my-key"

# List keys
wrangler kv:key list --binding=MY_KV

# Delete key
wrangler kv:key delete --binding=MY_KV "my-key"

# Bulk upload
wrangler kv:bulk put --binding=MY_KV data.json

D1 Database

# Create database
wrangler d1 create my-database

# Execute SQL
wrangler d1 execute my-database --command="SELECT * FROM users"

# Execute SQL file
wrangler d1 execute my-database --file=./schema.sql

# Export database
wrangler d1 export my-database --output=backup.sql

# List databases
wrangler d1 list

R2 Storage

# Create bucket
wrangler r2 bucket create my-bucket

# List buckets
wrangler r2 bucket list

# Upload file
wrangler r2 object put my-bucket/path/file.txt --file=./local-file.txt

# Download file
wrangler r2 object get my-bucket/path/file.txt

# Delete file
wrangler r2 object delete my-bucket/path/file.txt

Local Development

# Start local dev server
wrangler dev

# Dev with specific port
wrangler dev --port 8787

# Dev with local mode (no network to Cloudflare)
wrangler dev --local

# Dev with specific environment
wrangler dev --env staging

# Dev with live reload
wrangler dev --live-reload

CI/CD with GitHub Actions

name: Deploy to Cloudflare

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Setup Node
        uses: actions/setup-node@v4
        with:
          node-version: '20'

      - name: Install dependencies
        run: npm ci

      - name: Deploy Worker
        run: npx wrangler deploy
        env:
          CLOUDFLARE_API_TOKEN: ${{ secrets.CLOUDFLARE_API_TOKEN }}

Required Secrets

SecretDescription
CLOUDFLARE_API_TOKENAPI token with Workers edit permission
CLOUDFLARE_ACCOUNT_IDOptional, can be in wrangler.toml

Debugging Checklist

When a worker fails:

  1. Check live logs wrangler tail --status error
  2. Check recent deployments wrangler deployments list
  3. Rollback if needed wrangler rollback
  4. Test locally wrangler dev
  5. Check bindings wrangler kv:namespace list wrangler d1 list wrangler r2 bucket list
  6. Verify secrets wrangler secret list

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

29.3%
按下载量换算23

Antigravity

19.38%
按下载量换算15

windsurf

18.03%
按下载量换算14

Codex

13.14%
按下载量换算10

OpenCode

7.92%
按下载量换算6

Gemini CLI

3.62%
按下载量换算3

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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