Skip to main content

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.

Informations de source

Dépôt
elevenlabs/plugin
Dernière activité de la source
7 septembre 2026 à 10:00
Langue détectée de SKILL.md
anglais
Étoiles
3
Forks
4

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

Affichage de SKILL.md

SKILL.md
Instructions source · Aperçu en lecture seule
name
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.
Voir sur GitHub