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Copy file name to clipboardExpand all lines: docs/content/docs/framework/frontend.en.mdx
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@@ -187,71 +187,27 @@ After selecting a sub-agent, the `/` menu shows skills mounted on that target. C
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The frontend sends structured `veadkInvocation` metadata instead of inferring invocation intent from the message string. The backend plugin directs ADK to call the skill tool or invoke `transfer_to_agent` one tree edge at a time until it reaches the target. The same metadata is written to the first Google GenAI `Part`, so reopening session history restores the `/skill` and `@agent` chips.
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### Skill creation mode
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The new-chat **Skill creation** mode runs
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`doubao-seed-2-0-pro-260215` and `deepseek-v4-flash-260425` in parallel through
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Ark Responses. Each candidate gets an independent AgentKit CodeEnv session.
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The server validates the generated directory name, `SKILL.md` frontmatter,
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file count, size, and safe paths before packaging a ZIP. AgentKit credential
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configuration resolves the Ark API key with the deployer's AK/SK and writes it
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directly to each dedicated CodeEnv Tool. Per-job Session variables cannot
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override the Tool's model credential. Model credentials are never returned to
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the browser, and the Skill creation APIs require the Studio developer or admin
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role.
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After submission, both candidate conversations appear immediately and
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independently render public reasoning summaries, tool calls, and assistant
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messages returned by the generator. Private chain-of-thought and credentials
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never enter the page. Completed candidates still support preview, ZIP download,
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and AgentKit publishing. Once the real job ID is available, the frontend resumes
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polling directly from Sandbox state. An interrupted creation stream or a brief
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AgentKit control-plane delay no longer turns an intermediate state into an
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expired job.
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Before starting a local server, configure separate `Ready` AgentKit Tools for
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Codex, OpenClaw, Hermes, and Skill creation:
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### Skill Center
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Studio developers and admins create and optimize Skills from the Skill Center.
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Each candidate runs in an isolated DevEnv session on the shared Dev Sandbox
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Tool, streams public activity, and validates the generated files. Valid results
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can be previewed, downloaded, or published to AgentKit. Model credentials remain
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on the Tool and are never returned to the browser.
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Local Studio reads the DevEnv Tool ID from `SANDBOX_DEV`:
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