Claude-skill-registry lindy-core-workflow-a
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/lindy-core-workflow-a" ~/.claude/skills/majiayu000-claude-skill-registry-lindy-core-workflow-a && rm -rf "$T"
manifest:
skills/data/lindy-core-workflow-a/SKILL.mdsource content
Lindy Core Workflow A: Agent Creation
Overview
Complete workflow for creating, configuring, and deploying Lindy AI agents.
Prerequisites
- Completed
setuplindy-install-auth - Understanding of agent use case
- Clear instructions/persona defined
Instructions
Step 1: Define Agent Specification
interface AgentSpec { name: string; description: string; instructions: string; tools: string[]; model?: string; temperature?: number; } const agentSpec: AgentSpec = { name: 'Customer Support Agent', description: 'Handles customer inquiries and support tickets', instructions: ` You are a helpful customer support agent. - Be polite and professional - Ask clarifying questions when needed - Escalate complex issues to human support - Always confirm resolution with the customer `, tools: ['email', 'calendar', 'knowledge-base'], model: 'gpt-4', temperature: 0.7, };
Step 2: Create the Agent
import { Lindy } from '@lindy-ai/sdk'; const lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY }); async function createAgent(spec: AgentSpec) { const agent = await lindy.agents.create({ name: spec.name, description: spec.description, instructions: spec.instructions, tools: spec.tools, config: { model: spec.model || 'gpt-4', temperature: spec.temperature || 0.7, }, }); console.log(`Created agent: ${agent.id}`); return agent; }
Step 3: Configure Agent Tools
async function configureTools(agentId: string, tools: string[]) { for (const tool of tools) { await lindy.agents.addTool(agentId, { name: tool, enabled: true, }); } console.log(`Configured ${tools.length} tools`); }
Step 4: Test the Agent
async function testAgent(agentId: string) { const testCases = [ 'Hello, I need help with my order', 'Can you check my subscription status?', 'I want to cancel my account', ]; for (const input of testCases) { const result = await lindy.agents.run(agentId, { input }); console.log(`Input: ${input}`); console.log(`Output: ${result.output}\n`); } }
Output
- Fully configured AI agent
- Connected tools and integrations
- Tested agent responses
- Ready for deployment
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Tool not found | Invalid tool name | Check available tools list |
| Instructions too long | Exceeds limit | Summarize or split instructions |
| Model unavailable | Unsupported model | Use default gpt-4 |
Examples
Complete Agent Creation Flow
async function main() { // Create agent const agent = await createAgent(agentSpec); // Configure tools await configureTools(agent.id, agentSpec.tools); // Test agent await testAgent(agent.id); console.log(`Agent ${agent.id} is ready!`); } main().catch(console.error);
Resources
Next Steps
Proceed to
lindy-core-workflow-b for task automation workflows.