How do I install the Architect Generate Agent skill?

Run npx skillmds@latest add elevenlabs/architect-generate-agent in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Architect Generate Agent safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Architect Generate Agent?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Architect Generate Agent free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Architect Generate Agent?

ElevenLabs (@elevenlabs) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Architect Generate Agent by elevenlabs

Name: Architect Generate Agent
Author: ElevenLabs

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.

ElevenLabs 4fa8103 2.5 KB Updated Sep 17, 2026 363 repo stars

File contents

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.

---name: architect-generate-agentdescription: 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 questionsBefore 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 confirmSummarize 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 agentOnce 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: refineIterate 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 stepsTell 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.

Frequently asked questions