I've recently streamlined a practical AI agent pipeline, clarifying the roles of Hermes and Claude Code, and I'd like to share some insights.
In my day-to-day automation tasks, I found that relying solely on a large model often isn't efficient enough. **Hermes is my pipeline brain**—it handles task scheduling, working memory, cron timers, and message distribution, like a butler who knows my habits. It keeps task context across dates, executes repetitive work on a schedule, and pushes results to Telegram, email, or Feishu. No manual triggers required; the system runs on the clock.
Meanwhile, **Claude Code is my coding agent**. For one-off complex coding, code refactoring, or security audits, I switch over to it. It works directly in the IDE environment, can see the entire repository structure, and handles 10+ files at once without back-and-forth hassle. Refactoring a large module takes just 5 minutes. The coding complexity that Hermes can't handle gets passed to Claude Code.
Both sides' core advantage is the **shared skills and tool library**. The workflow scripts, API integrations, and data processing logic I've accumulated in Hermes can also be directly accessed by Claude Code. This makes switching costs extremely low—there's no need to rewrite adaptation code, and a problem can be seamlessly tackled on both sides.
For everyday conversations, I keep costs low with Haiku, but for complex tasks requiring long context or reasoning, I directly escalate to Sonnet/Opus. In practice, **the agent is the brain of the pipeline, and skills are the hands of the pipeline**. The brain is responsible for decision-making and coordination, while the hands handle specific repetitive actions. People don’t have to do these tasks every day; the system runs automatically.
The key is to distill manual tasks into clear skills and regularly update strategies. This way, even if personnel or tools change, the process can be reused. I've automated about 60% of my daily work now, with the remaining time used for new experiments.
$BTC #AI #Agent
In my day-to-day automation tasks, I found that relying solely on a large model often isn't efficient enough. **Hermes is my pipeline brain**—it handles task scheduling, working memory, cron timers, and message distribution, like a butler who knows my habits. It keeps task context across dates, executes repetitive work on a schedule, and pushes results to Telegram, email, or Feishu. No manual triggers required; the system runs on the clock.
Meanwhile, **Claude Code is my coding agent**. For one-off complex coding, code refactoring, or security audits, I switch over to it. It works directly in the IDE environment, can see the entire repository structure, and handles 10+ files at once without back-and-forth hassle. Refactoring a large module takes just 5 minutes. The coding complexity that Hermes can't handle gets passed to Claude Code.
Both sides' core advantage is the **shared skills and tool library**. The workflow scripts, API integrations, and data processing logic I've accumulated in Hermes can also be directly accessed by Claude Code. This makes switching costs extremely low—there's no need to rewrite adaptation code, and a problem can be seamlessly tackled on both sides.
For everyday conversations, I keep costs low with Haiku, but for complex tasks requiring long context or reasoning, I directly escalate to Sonnet/Opus. In practice, **the agent is the brain of the pipeline, and skills are the hands of the pipeline**. The brain is responsible for decision-making and coordination, while the hands handle specific repetitive actions. People don’t have to do these tasks every day; the system runs automatically.
The key is to distill manual tasks into clear skills and regularly update strategies. This way, even if personnel or tools change, the process can be reused. I've automated about 60% of my daily work now, with the remaining time used for new experiments.
$BTC #AI #Agent