I typed one message: “Research TypeScript 5.5 release notes, summarize the key features, and save the summary to my notes folder.” Three separate skills ran. The agent searched the web, took those results to summarize them, and then saved the final text to a file. It figured out the order itself. I didn’t tell it which tools to use or in what sequence. This is skill chaining. It is the exact point where an AI agent stops feeling like a simple chatbot. It starts feeling like a capable, autonomous assistant.
What is skill chaining and why should I care?
Chaining isn’t something you hardcode. It happens naturally when your skills have clear descriptions. The model reasons about the sequence based on what you need.
The Scenario: You’re a developer trying to keep up with a fast-moving framework. Every Tuesday, you manually search for release notes. You read through a 2,000-word changelog. You copy the important parts into a “Tech Debt” document. With skill chaining, you just say “Check for updates.” The agent does all three steps while you make coffee.
Your job is to make each skill’s description clear. The model will handle the rest.
How do I give my agent “eyes” on the live web?
We’ll use DuckDuckGo’s Lite endpoint. It’s free and returns structured results without an API key.
export async function web_search({ query, maxResults = 5 }) {
const response = await fetch(
`https://html.duckduckgo.com/html/?q=${encodeURIComponent(query)}`
);
const html = await response.text();
// ... extract results with regex
return { query, results, count: results.length };
}Can an AI use another AI to save me money?
Sometimes a skill needs to call another model internally. This is a smart pattern for processing data.
The Scenario: You’re summarizing a massive 50-page legal document. If you use your “smartest” model for the whole task, it’ll cost you $2 in API fees. Instead, you build a skill where your main agent sends chunks of text to a faster, cheaper model like Claude Haiku. You get the same summary for $0.05.
export async function summarize_text({ text, maxLength = 300 }) {
const response = await client.messages.create({
model: "claude-haiku-4-5",
max_tokens: maxLength,
messages: [{ role: "user", content: `Summarize this: ${text}` }]
});
return { summary: response.content[0].text };
}How do I explain the “order of operations” to the LLM?
Be specific in your tool descriptions. Tell the AI when one tool’s output is perfect for another tool’s input.
{
name: "summarize_text",
description: "Summarize long text. Use this after web_search or reading a file when the content is too long for a chat response.",
// ... schema
}What does the “brain” of a multi-step agent look like?
The agent runs in a loop. It keeps calling tools until it has the final answer.
The Scenario: You ask for a “Deep dive into Node.js 22.” The agent first searches. It realizes the search result is a 10,000-word blog post. It then calls the summarizer. Finally, it sees your request to “save it” and calls the file writer. The loop handles the entire handoff between tools.
Can my agent do two things at once?
If two skills don’t depend on each other, the model might fire them both at once. This is parallel chaining.
The Scenario: You ask your agent to “Check the weather in Mumbai and the stock price for Apple.” The agent doesn’t need the weather to find the stock price. It sends both tool calls in a single response. You get both answers twice as fast.
What happens when the first link in the chain breaks?
If the search fails, the summary will fail too. You need to tell the model how to handle errors mid-chain.
"Do NOT summarize error messages. If the search fails, report the error to the user instead of trying to summarize it."What should I build next?
- File an issue: Build a GitHub Issue Creator Skill for Your AI Agent
- Unify your API: Vercel AI SDK Tools: One API for Claude and OpenAI Skills
- Fix the errors: Handling Errors in Agent Skills: Retries and Fallbacks
