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architect-generate-agent

Use when the user asks to generate, build, or create a new agent from a description ("create an agent for X", "build a chatbot that does Y"). There is no one-shot generate endpoint over REST; ask clarifying questions, then build via create and refine via patch.

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elevenlabs/plugin
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2026년 9월 7일 10:00
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기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.

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설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.

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SKILL.md
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architect-generate-agent
description
Use when the user asks to generate, build, or create a new agent from a description ("create an agent for X", "build a chatbot that does Y"). There is no one-shot generate endpoint over REST; ask clarifying questions, then build via create and refine via patch.
# Generate a new agent (there is no one-shot generate endpoint) The web Architect has a `generate_agent` action. Over the ConvAI REST API there is NO one-shot generate endpoint. Do not pretend there is. Instead, gather requirements, create a starter agent with `POST /v1/convai/agents/create`, then refine it with `PATCH` (host `https://api.elevenlabs.io`, header `xi-api-key: $API_KEY`). ## Step 1: pause and ask clarifying questions Before creating anything, understand what the user actually needs. Ask targeted questions to gather: - Use case and context: what specific problem does this agent solve? Who are the end users? - Scope and capabilities: what should the agent do, and what should it NOT do? - Tone and personality: how should it sound (formal, friendly, technical)? - Integration needs: does it need to call external APIs, access a knowledge base, or trigger workflows? - Success criteria: how will the user know it is working well? - Constraints: any compliance, language, or channel requirements? ## Step 2: synthesize and confirm Summarize your understanding back to the user in 2-3 sentences and ask "Is this what you're looking for?" This prevents building the wrong agent. ## Step 3: create the agent Once aligned, create the agent with `POST /v1/convai/agents/create`. Set the name and an initial `conversation_config` built from the synthesized requirements: a system prompt capturing the use case, scope, tone, and constraints, a suitable first message, and an LLM chosen for the task (see the `architect-llm-selection` skill for region/compliance/latency tradeoffs). The response returns the new `agent_id`. ## Step 4: refine Iterate with `PATCH /v1/convai/agents/{agent_id}?branch_id={b}` partial-merge bodies to tighten the prompt, add tools, or adjust config. For step-by-step behavior, add procedures (see the `architect-manage-procedures` skill). For knowledge, attach knowledge base documents via `POST /v1/convai/knowledge-base/text` or `.../url`. For post-call extraction and grading, see the `architect-post-call-data` skill. ## Step 5: review and next steps Tell the user the agent is created and offer next steps: review the system prompt, set up procedures, test with a simulation, secure it for production, or customize the workflow.
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