retellai-core-workflow-a
Builds and configures AI-driven voice agents using Retell AI.
Install
mkdir -p .claude/skills/retellai-core-workflow-a && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7945" && unzip -o skill.zip -d .claude/skills/retellai-core-workflow-a && rm skill.zipInstalls to .claude/skills/retellai-core-workflow-a
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Retell AI core workflow a \u2014 AI voice agent and phone call automation.\n\Key capabilities
- →Create voice agents with custom LLM prompts
- →Configure function calling for telephony actions
- →Set call behavior parameters like silence timeouts
- →Enable backchannel responses
- →Update agent voice and configuration
How it works
It uses the Retell SDK to define LLM models, prompts, and function schemas, which are then attached to an agent instance to handle real-time voice interactions.
Inputs & outputs
When to use retellai-core-workflow-a
- →Create a custom voice assistant
- →Implement function calling for bookings
- →Configure telephony agent prompts
- →Build voice-enabled business workflows
About this skill
Retell AI Core Workflow A
Overview
Build and configure voice agents with custom prompts, function calling, and call flow logic.
Prerequisites
- Completed
retellai-hello-world
Instructions
Step 1: Agent with Function Calling
const llm = await retell.llm.create({
model: 'gpt-4o',
general_prompt: `You are a booking assistant for Dr. Smith's office.
- Help callers book, reschedule, or cancel appointments
- Collect: name, phone, preferred date/time
- Confirm all details before booking`,
functions: [
{
name: 'book_appointment',
description: 'Book a new appointment',
parameters: {
type: 'object',
properties: {
patient_name: { type: 'string' },
phone: { type: 'string' },
date: { type: 'string', description: 'YYYY-MM-DD format' },
time: { type: 'string', description: 'HH:MM format' },
},
required: ['patient_name', 'phone', 'date', 'time'],
},
url: 'https://your-api.com/appointments',
speak_during_execution: true,
speak_after_execution: true,
},
],
});
Step 2: Configure Voice and Behavior
const agent = await retell.agent.create({
response_engine: { type: 'retell-llm', llm_id: llm.llm_id },
voice_id: '11labs-Rachel',
agent_name: 'Dr. Smith Booking Agent',
language: 'en-US',
opt_out_sensitive_data_storage: false,
end_call_after_silence_ms: 10000, // End call after 10s silence
max_call_duration_ms: 300000, // 5-minute max
enable_backchannel: true, // "mhm", "yeah" responses
boosted_keywords: ['appointment', 'schedule', 'Dr. Smith'],
});
Step 3: Update Agent Configuration
await retell.agent.update(agent.agent_id, {
voice_id: '11labs-Dorothy', // Change voice
end_call_after_silence_ms: 15000,
});
Output
- Agent with custom LLM prompt and function calling
- Voice and behavior configuration
- Real-time function execution during calls
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Function not triggering | Prompt doesn't guide to function | Include function use in prompt |
| Voice quality issues | Wrong voice selection | Test different voices |
| Call ending too early | Short silence timeout | Increase end_call_after_silence_ms |
Resources
Next Steps
Phone call management: retellai-core-workflow-b
When not to use it
- →When the agent prompt does not explicitly guide the model to use functions
Prerequisites
Limitations
- →Function not triggering if prompt is insufficient
How it compares
This provides a structured workflow for linking backend API logic directly to voice agent function calls, bypassing manual telephony integration.
Compared to similar skills
retellai-core-workflow-a side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| retellai-core-workflow-a (this skill) | 0 | 27d | Review | Intermediate |
| llama-cpp | 21 | 8mo | Review | Intermediate |
| mcp-builder | 136 | 3mo | Review | Advanced |
| skill-creator | 128 | 3mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by jeremylongshore
View all by jeremylongshore →You might also like
llama-cpp
zechenzhangAGI
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
mcp-builder
anthropics
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
skill-creator
anthropics
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
opencode-cli
SpillwaveSolutions
This skill should be used when configuring or using the OpenCode CLI for headless LLM automation. Use when the user asks to "configure opencode", "use opencode cli", "set up opencode", "opencode run command", "opencode model selection", "opencode providers", "opencode vertex ai", "opencode mcp servers", "opencode ollama", "opencode local models", "opencode deepseek", "opencode kimi", "opencode mistral", "fallback cli tool", or "headless llm cli". Covers command syntax, provider configuration, Vertex AI setup, MCP servers, local models, cloud providers, and subprocess integration patterns.
claude-automation-recommender
anthropics
Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.
langchain
zechenzhangAGI
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.