CAT
/Skills
SkillsMCPMarketplacesDigestToolsAdvertise

This week in Claude

Every Monday: Claude Code, Agent SDK, MCP, and the Anthropic platform moves worth your time.

Skills by Category
Frontend DevelopmentBackend & APIsTesting & QASecurityDevOps & CI/CDGit & Pull RequestsDocumentationCode Review & QualityAI & Agent BuildingSkill Development
MCP Servers by Category
Sales & MarketingWeb & Browser AutomationDatabasesAI & LLM ToolsCloud & InfrastructureCommunication & MessagingDeveloper ToolsDesign & CreativeDocuments & KnowledgeSearch & Web Crawling
Marketplaces by Category
AI Agents & OrchestrationLLM IntegrationDevelopment ToolsFrontend & UIBackend & APIsDatabasesTesting & Code QualityDevOps & CloudSecurity & ComplianceGit & Version Control

Cross AI Tools

Discover Claude Code plugins, extensions, and tools. Automatically updated directory of Anthropic Claude AI marketplaces with development tools, productivity plugins, and integrations.

Resources

  • Browse Skills
  • Browse MCP Servers
  • Browse Marketplaces
  • Plugins Reference

Community

  • About
  • Tools
  • Feedback
  • Privacy Policy
  • Advertise

Built for the Claude Code community with Claude Code by @mertduzgun

Independent project, not affiliated with Anthropic

Can

zhinkgit/embeddedskills
178 installs314 stars
Summary

A full-featured CAN/CAN-FD debugging tool that wraps interface discovery, real-time monitoring, frame transmission, logging, and DBC decoding into a single workflow. It auto-scans USB-CAN adapters (PCAN, Vector, slcan), writes working configs to your project so you don't re-specify channels every time, and handles both arbitration and data bitrates for FD frames. The send command goes straight through once configured, no confirmation prompts, which is efficient but means you should double-check your IDs first. Output is structured JSON or JSON Lines for streaming, and it prioritizes CLI args over project config over defaults, so you can override on the fly. If you're doing embedded CAN work and tired of switching between vendor tools, this consolidates the repetitive stuff.

Install to Claude Code

npx -y skills add zhinkgit/embeddedskills --skill can --agent claude-code

Installs into .claude/skills of the current project.

CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
Files
SKILL.mdView on GitHub

CAN — 嵌入式 CAN / CAN-FD 调试工具

统一封装接口发现、实时监控、报文发送、日志记录、数据库文件解码和统计分析能力。

配置

环境级配置 (skill/config.json)

仅保留 slcan 相关的环境级配置:

{
  "slcan_serial_port": "",
  "slcan_serial_baudrate": 115200
}
字段说明默认值
slcan_serial_portslcan 场景的串口""
slcan_serial_baudrateslcan 场景的串口速率115200

工程级配置 (.embeddedskills/config.json)

工作区下的 .embeddedskills/config.json 存放工程级 CAN 配置:

{
  "can": {
    "interface": "",
    "channel": "",
    "bitrate": 500000,
    "data_bitrate": 2000000,
    "log_dir": ".embeddedskills/logs/can"
  }
}
字段说明默认值
interfaceCAN 后端,如 pcan / vector / slcan""
channel通道名,如 PCAN_USBBUS1""
bitrate仲裁域比特率500000
data_bitrateCAN-FD 数据域比特率2000000
log_dir日志输出目录.embeddedskills/logs/can

参数解析优先级

  1. CLI 参数 (--interface, --channel, --bitrate 等) - 最高优先级
  2. 工程级配置 (.embeddedskills/config.json 中的 can 部分)
  3. 状态文件 (.embeddedskills/state.json 中的历史记录)
  4. 默认值 - 最低优先级

自动扫描行为

当未指定 interface 和 channel 时,脚本会自动扫描系统 CAN 接口,按以下步骤处理:

  1. 扫描系统中所有可用 CAN 接口
  2. 若只找到一个接口 → 自动使用并写入工程配置
  3. 若找到多个接口 → 返回候选列表,等待用户选择
  4. 若未找到接口 → 提示错误,停止执行

子命令

子命令用途风险
scan扫描可用 CAN 接口与 USB-CAN 设备低
monitor实时监控总线报文低
send发送标准帧 / 扩展帧 / 远程帧 / CAN-FD 帧高
log记录总线报文到 ASC / BLF / CSV 文件低
decode用 DBC 等数据库文件解码报文或日志低
stats统计总线负载、ID 分布和帧率低

执行流程

  1. 检查 python-can 是否可用,未安装时提示 pip install python-can
  2. 按优先级解析参数:CLI > 工程级配置 > 状态文件 > 默认值
  3. 无子命令时默认执行 scan
  4. monitor / send / log / stats 使用解析后的连接参数
  5. decode 先确认数据库文件和输入源存在
  6. 若未指定 interface/channel,自动扫描系统 CAN 接口:
    • 唯一候选:自动使用并写入工程配置
    • 多候选:返回列表让用户选择
  7. 成功执行后,将确认的参数写回工程配置
  8. send 只要配置可连接就直接执行,不二次确认
  9. 运行对应脚本并输出结构化结果
  10. 失败时优先反馈接口、驱动、比特率和过滤条件问题

脚本调用

所有脚本位于 skill 目录的 scripts/ 下,通过 python 直接调用。 脚本会按优先级从 CLI 参数、工程级配置、状态文件中读取参数。

# 扫描接口
python scripts/can_scan.py [--json]

# 实时监控
python scripts/can_monitor.py [--interface <接口>] [--channel <通道>] [--bitrate <速率>] [--fd] [--filter-id <ID列表>] [--exclude-id <ID列表>] [--dbc <DBC文件>] [--timeout <秒>] [--json]

# 发送报文
python scripts/can_send.py [--interface <接口>] [--channel <通道>] [--bitrate <速率>] <id> <data> [--extended] [--remote] [--fd] [--repeat <次>] [--interval <秒>] [--periodic <毫秒>] [--listen] [--json]

# 日志记录
python scripts/can_log.py [--interface <接口>] [--channel <通道>] [--bitrate <速率>] [--output <文件>] [--duration <秒>] [--max-count <数量>] [--filter-id <ID列表>] [--console] [--json]

# 数据库解码
python scripts/can_decode.py <db_file> [--db-format <auto|dbc|arxml|kcd|sym|cdd>] [--id <CAN_ID>] [--data <HEX数据>] [--log <日志文件>] [--signal <信号名>] [--list] [--json]

# 总线统计
python scripts/can_stats.py [--interface <接口>] [--channel <通道>] [--bitrate <速率>] [--duration <秒>] [--top <数量>] [--watch <ID列表>] [--json]

输出格式

单次命令返回标准 JSON:

{
  "status": "ok",
  "action": "scan",
  "summary": "发现 2 个 CAN 接口",
  "details": { ... }
}

持续命令(monitor --json、send --listen --json)输出 JSON Lines,结束摘要写入 stderr。

错误输出:

{
  "status": "error",
  "action": "send",
  "error": { "code": "interface_open_failed", "message": "无法打开指定 CAN 接口" }
}

核心规则

  • 不自动猜测 interface、channel、bitrate,多接口时不自动选择
  • 参数解析优先级:CLI > 工程级配置 > 状态文件 > 默认值;自动扫描结果仅在未提供 CLI 参数时生效
  • 未指定 interface/channel 时自动扫描,唯一候选自动写入配置,多候选需用户选择
  • 成功执行后,确认的参数自动写回 .embeddedskills/config.json
  • 未明确说明用途时不主动发送任何报文
  • --json 输出的持续流使用 JSON Lines,摘要写 stderr 不污染数据流
  • DBC 解码失败不应导致监控中断
  • 找不到帧定义时返回明确错误,不静默吞掉

参考

  • references/common_interfaces.json:常见 USB-CAN 设备信息
Featured
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
First SeenJun 3, 2026
View on GitHub

Recommended

caveman

juliusbrussee/caveman

Ultra-compressed communication mode cutting token usage ~75% while preserving technical accuracy.
203.4k
67.8k
grill-me

mattpocock/skills

Relentless interviewing skill that stress-tests plans and designs through systematic questioning.
250.9k
114.5k
improve

shadcn/improve

Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for other models/agents to execute.
10
205
systematic-debugging

obra/superpowers

Structured debugging methodology that mandates root cause investigation before attempting any fixes.
124.6k
215.9k
karpathy-guidelines

forrestchang/andrej-karpathy-skills

Behavioral guidelines to reduce common LLM coding mistakes through explicit assumptions, simplicity, and verifiable success criteria.
13.9k
165.4k
find-skills

vercel-labs/skills

Discover and install specialized agent skills from the open ecosystem when users need extended capabilities.
1.8M
21.1k