Why Use This This skill provides specialized capabilities for jeremylongshore's codebase.
Use Cases Developing new features in the jeremylongshore repository Refactoring existing code to follow jeremylongshore standards Understanding and working with jeremylongshore's codebase structure
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Updated At Jan 9, 2026, 12:57 AM
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SKILL.md 147 Lines
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License MIT
---
name: lindy-core-workflow-a
description: |
Core Lindy workflow for creating and configuring AI agents.
Use when building new agents, defining agent behaviors,
or setting up agent capabilities.
Trigger with phrases like "create lindy agent", "build lindy agent",
"lindy agent workflow", "configure lindy agent".
allowed-tools: Read, Write, Edit
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected] >
---
# Lindy Core Workflow A: Agent Creation
## Overview
Complete workflow for creating, configuring, and deploying Lindy AI agents.
## Prerequisites
- Completed `lindy-install-auth` setup
- Understanding of agent use case
- Clear instructions/persona defined
## Instructions
### Step 1: Define Agent Specification
```typescript
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
```typescript
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
```typescript
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
```typescript
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
```typescript
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
- [Lindy Agent Creation](https://docs.lindy.ai/agents/create)
- [Available Tools](https://docs.lindy.ai/tools)
- [Model Options](https://docs.lindy.ai/models)
## Next Steps
Proceed to `lindy-core-workflow-b` for task automation workflows.