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create-client-tool

Scaffolds an AtlasTool for an already-approved in-app useAtlasChat UI. For EOS sidebar tools, use integrate-fusion-agent (createAgentAction) instead. Triggers: AtlasTool, useAtlasChat tool, in-app atlas client tool.

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name create-client-tool description Scaffolds an AtlasTool for an already-approved in-app useAtlasChat UI. For EOS sidebar tools, use integrate-fusion-agent (createAgentAction) instead. Triggers: AtlasTool, useAtlasChat tool, in-app atlas client tool. allowed-tools Read, Glob, Grep, Edit, Write metadata {"argument-hint":"[tool-name] [brief description of what it does]"} Create a Client Tool Scaffold an AtlasTool named $ARGUMENTS . If the app has no approved in-app useAtlasChat , implement a Fusion action via integrate-fusion-agent instead. Prerequisite: vendored src/atlas-agent/ and @sinclair/typebox from integrate-atlas-chat . Background Client tools let the Atlas Agent invoke browser-side logic — charts, local state, UI panels, navigation. The agent decides when to call; the app executes and returns a result. Agent responds with a clientTool action TypeBox validates the arguments execute() runs in the browser and returns { output, details } output (string) is sent back to the agent details is available on message.toolCalls for the UI to render Step 1 — Understand the codebase Before writing anything, read: The file where useAtlasChat is called (often src/App.tsx or a chat hook) to find where tools is passed — imports are typically from ./atlas-agent/react after integrate-atlas-chat Any existing tool definitions to match the file/naming conventions Step 2 — Define the tool Use Type from @sinclair/typebox for the parameters schema (compile-time types + runtime validation). import { Type } from "@sinclair/typebox" ; import type { AtlasTool } from "./atlas-agent/types" ; export const myTool : AtlasTool = { name : "my_tool" , // snake_case — this is what the agent uses to invoke it description : "One sentence describing what this tool does and when the agent should call it." , parameters : Type . Object ({ exampleParam : Type . String ({ description : "What this param is for" }), optionalNum : Type . Optional ( Type . Number ({ description : "..." })), }), execute : async (args) => { return { output : "Plain text summary sent back to the agent" , details : { // Any structured data you want available in the UI via message.toolCalls }, }; }, }; Adjust the ./atlas-agent/... path if the tool file is not directly under src/ next to the atlas-agent folder (for example ../atlas-agent/types from src/tools/ ). TypeBox quick reference Schema Usage Type.String() string Type.Number() number Type.Boolean() boolean Type.Literal("foo") exact value Type.Union([Type.Literal("a"), Type.Literal("b")]) enum Type.Array(Type.String()) string[] Type.Object({ ... }) object Type.Optional(...) mark any field optional Always add a description on the tool and on each parameter — the agent uses those strings. Step 3 — Wire into useAtlasChat Find the useAtlasChat call and add the tool to the tools array: const { messages, send, ... } = useAtlasChat ({ client : isLoading ? null : sdk, agentExternalId : AGENT_EXTERNAL_ID , tools : [myTool], // add here }); Step 4 — Render tool results (if needed) If the tool returns structured details , render them in the message list. message.toolCalls is a ToolCall[] — one entry per tool call (client-side and server-side) in call order. {msg. toolCalls ?. map ( ( tc, i ) => ( // tc.name — tool name // tc.output — the string sent back to the agent // tc.details — your structured data (cast to your known shape) < MyToolOutput key = {i} data = {tc.details as MyToolDetails } /> ))}
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Field Description
formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
The same skill can be exported in different platform formats.
.skill Standard format with system_prompt and model_config, ready for any agent framework Download
.skillpro Enhanced format with scripts, tools, dependencies and hooks Download
.json Plain JSON export with system_prompt and model parameters only Download
Coze Markdown with frontmatter, for Coze platform import Download
Dify Dify DSL, import directly after creating an app Download

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