Optimize Lindy AI agent performance and response times. Use when improving latency, optimizing throughput, or reducing response times. Trigger with phrases like "lindy performance", "lindy slow", "optimize lindy", "lindy latency".
/plugin marketplace add jeremylongshore/claude-code-plugins-plus-skills/plugin install lindy-pack@claude-code-plugins-plusThis skill is limited to using the following tools:
Optimize Lindy AI agent performance for faster response times and higher throughput.
import { Lindy } from '@lindy-ai/sdk';
interface PerformanceMetrics {
avgLatency: number;
p95Latency: number;
p99Latency: number;
throughput: number;
errorRate: number;
}
async function measureBaseline(agentId: string, iterations = 100): Promise<PerformanceMetrics> {
const lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });
const latencies: number[] = [];
let errors = 0;
const start = Date.now();
for (let i = 0; i < iterations; i++) {
const runStart = Date.now();
try {
await lindy.agents.run(agentId, { input: 'Benchmark test' });
latencies.push(Date.now() - runStart);
} catch (e) {
errors++;
}
}
const totalTime = (Date.now() - start) / 1000;
latencies.sort((a, b) => a - b);
return {
avgLatency: latencies.reduce((a, b) => a + b, 0) / latencies.length,
p95Latency: latencies[Math.floor(latencies.length * 0.95)],
p99Latency: latencies[Math.floor(latencies.length * 0.99)],
throughput: iterations / totalTime,
errorRate: errors / iterations,
};
}
// BEFORE: Verbose instructions (slow)
const slowAgent = {
instructions: `
You are a helpful assistant that should carefully consider
each request and provide detailed, comprehensive responses.
Think step by step about each query. Consider all possibilities.
Provide examples and explanations for everything.
`,
};
// AFTER: Concise instructions (fast)
const fastAgent = {
instructions: `
Be concise. Answer directly. Skip pleasantries.
Format: [Answer] (1-2 sentences)
`,
config: {
maxTokens: 100, // Limit response length
},
};
// Non-streaming (waits for full response)
const result = await lindy.agents.run(agentId, { input });
console.log(result.output); // Logs after full response
// Streaming (immediate partial responses)
const stream = await lindy.agents.runStream(agentId, { input });
for await (const chunk of stream) {
process.stdout.write(chunk.delta); // Real-time output
}
import NodeCache from 'node-cache';
const cache = new NodeCache({ stdTTL: 300 }); // 5 minute TTL
async function runWithCache(agentId: string, input: string) {
const cacheKey = `${agentId}:${crypto.createHash('md5').update(input).digest('hex')}`;
// Check cache
const cached = cache.get(cacheKey);
if (cached) {
return cached;
}
// Run agent
const lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });
const result = await lindy.agents.run(agentId, { input });
// Cache result
cache.set(cacheKey, result);
return result;
}
// Poor: Sequential execution
for (const input of inputs) {
await lindy.agents.run(agentId, { input }); // Slow!
}
// Better: Parallel with controlled concurrency
import pLimit from 'p-limit';
const limit = pLimit(5); // Max 5 concurrent
const results = await Promise.all(
inputs.map(input =>
limit(() => lindy.agents.run(agentId, { input }))
)
);
[ ] Baseline metrics captured
[ ] Instructions are concise
[ ] Max tokens configured appropriately
[ ] Streaming enabled for long responses
[ ] Caching implemented for repeated queries
[ ] Concurrency optimized
[ ] Connection pooling enabled
[ ] Timeout values tuned
| Issue | Cause | Solution |
|---|---|---|
| High latency | Verbose instructions | Simplify prompts |
| Timeouts | Response too long | Add max tokens |
| Throttling | Too concurrent | Limit parallelism |
class PerformantLindyClient {
private lindy: Lindy;
private cache: NodeCache;
private limiter: any;
constructor() {
this.lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });
this.cache = new NodeCache({ stdTTL: 300 });
this.limiter = pLimit(5);
}
async run(agentId: string, input: string) {
const cacheKey = `${agentId}:${input}`;
const cached = this.cache.get(cacheKey);
if (cached) return cached;
const result = await this.limiter(() =>
this.lindy.agents.run(agentId, { input })
);
this.cache.set(cacheKey, result);
return result;
}
}
Proceed to lindy-cost-tuning for cost optimization.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.