# Clickup Performance Tuning

> Optimize ClickUp API v2 performance with caching, pagination, connection pooling, and request batching patterns. Trigger: "clickup performance", "optimize clickup", "clickup latency", "clickup caching", "clickup slow", "clickup batch requests", "clickup pagination".

- Skill: `gabrielmoreira/clickup-performance-tuning` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/clickup-performance-tuning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/clickup-performance-tuning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- License: MIT
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/clickup-performance-tuning

---

# ClickUp Performance Tuning

## Overview

Optimize ClickUp API v2 throughput and latency. Key strategies: cache hierarchy data, paginate efficiently, pool connections, and batch where possible.

## Baseline Latency (ClickUp API v2)

| Endpoint | Typical P50 | Typical P95 |
|----------|-------------|-------------|
| `GET /user` | 80ms | 200ms |
| `GET /team` | 100ms | 300ms |
| `GET /list/{id}/task` | 150ms | 500ms |
| `POST /list/{id}/task` | 200ms | 600ms |
| `PUT /task/{id}` | 150ms | 400ms |
| `GET /task/{id}` (with custom fields) | 200ms | 700ms |

## 1. Cache Hierarchy Data

Workspaces, spaces, folders, and lists change infrequently. Cache them.

```typescript
import { LRUCache } from 'lru-cache';

const clickupCache = new LRUCache<string, any>({
  max: 1000,
  ttl: 300_000, // 5 min for structural data
});

async function cachedRequest<T>(path: string, ttl?: number): Promise<T> {
  const cached = clickupCache.get(path);
  if (cached) return cached as T;

  const data = await clickupRequest(path);
  clickupCache.set(path, data, ttl ? { ttl } : undefined);
  return data as T;
}

// Hierarchy data: 5 min cache (default)
const spaces = await cachedRequest(`/team/${teamId}/space?archived=false`);

// Task data: 30 sec cache (changes more often)
const task = await cachedRequest(`/task/${taskId}`, 30_000);
```

## 2. Efficient Pagination

Get Tasks returns max 100 tasks per page. Use async generators for memory efficiency.

```typescript
async function* paginateTasks(listId: string, filters: Record<string, string> = {}) {
  let page = 0;
  let hasMore = true;

  while (hasMore) {
    const params = new URLSearchParams({
      page: String(page),
      archived: 'false',
      subtasks: 'true',
      ...filters,
    });

    const data = await clickupRequest(`/list/${listId}/task?${params}`);
    const tasks = data.tasks;

    for (const task of tasks) {
      yield task;
    }

    // ClickUp returns fewer than 100 tasks on last page
    hasMore = tasks.length === 100;
    page++;
  }
}

// Process tasks without loading all into memory
let count = 0;
for await (const task of paginateTasks('900100200300', { 'statuses[]': 'in progress' })) {
  await processTask(task);
  count++;
}
console.log(`Processed ${count} tasks`);
```

## 3. Connection Pooling

```typescript
import { Agent } from 'node:https';

const keepAliveAgent = new Agent({
  keepAlive: true,
  maxSockets: 10,
  maxFreeSockets: 5,
  timeout: 30_000,
  scheduling: 'lifo',
});

// Use with undici or node-fetch that supports custom agents
// Native fetch in Node 18+ uses keep-alive by default
```

## 4. Parallel with Rate Awareness

```typescript
import PQueue from 'p-queue';

// Respect 100 req/min on Free/Unlimited/Business
const clickupQueue = new PQueue({
  concurrency: 5,
  interval: 60_000,
  intervalCap: 90, // 90% of 100 limit
});

async function parallelTaskFetch(taskIds: string[]) {
  const results = await Promise.all(
    taskIds.map(id =>
      clickupQueue.add(() => clickupRequest(`/task/${id}`))
    )
  );
  return results;
}
```

## 5. Webhook-Based Cache Invalidation

```typescript
// Instead of polling or short TTLs, invalidate cache on webhook events
app.post('/webhooks/clickup', (req, res) => {
  res.status(200).json({ received: true });

  const { event, task_id } = req.body;

  switch (event) {
    case 'taskUpdated':
    case 'taskDeleted':
      clickupCache.delete(`/task/${task_id}`);
      break;
    case 'listUpdated':
    case 'listDeleted':
      // Invalidate all list-related caches
      for (const key of clickupCache.keys()) {
        if (key.includes('/list/')) clickupCache.delete(key);
      }
      break;
  }
});
```

## 6. Reduce Payload Size

```typescript
// Use custom_fields and include_closed parameters to minimize response size
const params = new URLSearchParams({
  archived: 'false',
  include_closed: 'false',
  subtasks: 'false',        // Skip subtask expansion if not needed
  page: '0',
});

// Note: ClickUp v2 doesn't support field selection (no ?fields= parameter)
// Minimize response by filtering client-side
const { tasks } = await clickupRequest(`/list/${listId}/task?${params}`);
const slim = tasks.map((t: any) => ({
  id: t.id, name: t.name, status: t.status.status, priority: t.priority?.priority,
}));
```

## Performance Monitoring

```typescript
async function measuredRequest<T>(name: string, fn: () => Promise<T>): Promise<T> {
  const start = performance.now();
  try {
    const result = await fn();
    const ms = (performance.now() - start).toFixed(1);
    console.log(`[clickup] ${name}: ${ms}ms`);
    return result;
  } catch (error) {
    const ms = (performance.now() - start).toFixed(1);
    console.error(`[clickup] ${name}: FAILED after ${ms}ms`);
    throw error;
  }
}
```

## Error Handling

| Issue | Cause | Solution |
|-------|-------|----------|
| Stale cache | No invalidation | Use webhooks for invalidation |
| Memory growth | Unbounded cache | Set `max` entries on LRU cache |
| Pagination loop | API returns 100 forever | Add max page safety limit |
| Queue backlog | Burst of requests | Increase concurrency or plan tier |

## Prerequisites

- Baseline latency, throughput, freshness, and rate-limit measurements
- Defined SLO, data-consistency constraints, and change/rollback owner
- Staging workload that represents target list/task volume without private data

## Instructions

Tune one bounded variable at a time—pagination, cache policy, concurrency, or
payload handling—then compare observed behavior to the baseline. Keep retries
and cache invalidation correct before raising concurrency; performance gains
that create stale tasks, duplicate writes, or rate-limit debt are regressions.

## Output

Produce a tuning record with baseline, proposed setting, measured impact,
rate-limit and error behavior, data-freshness result, owner approval, and
rollback threshold. Keep raw task content and credentials out of measurements.

## Examples

In staging, cap a task-list pagination job, add an LRU limit, and compare p95
latency and 429 rate to baseline. If cached task status becomes stale or queue
depth rises, restore the prior setting and correct invalidation before tuning
another parameter.

## Resources

- [ClickUp Get Tasks](https://developer.clickup.com/reference/gettasks)
- [ClickUp Rate Limits](https://developer.clickup.com/docs/rate-limits)
- [lru-cache](https://github.com/isaacs/node-lru-cache)
- [p-queue](https://github.com/sindresorhus/p-queue)

## Next Steps

For cost optimization, see `clickup-cost-tuning`.