castai-rate-limits
Handle CAST AI API rate limits with backoff and request queuing. Use when hitting 429 errors, optimizing API call patterns, or implementing rate-aware batch operations. Trigger with phrases like "cast ai rate limit", "cast ai 429", "cast ai throttle", "cast ai API limits".
Best use case
castai-rate-limits is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Handle CAST AI API rate limits with backoff and request queuing. Use when hitting 429 errors, optimizing API call patterns, or implementing rate-aware batch operations. Trigger with phrases like "cast ai rate limit", "cast ai 429", "cast ai throttle", "cast ai API limits".
Teams using castai-rate-limits should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/castai-rate-limits/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How castai-rate-limits Compares
| Feature / Agent | castai-rate-limits | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Handle CAST AI API rate limits with backoff and request queuing. Use when hitting 429 errors, optimizing API call patterns, or implementing rate-aware batch operations. Trigger with phrases like "cast ai rate limit", "cast ai 429", "cast ai throttle", "cast ai API limits".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# CAST AI Rate Limits
## Overview
The CAST AI REST API enforces rate limits per API key. The autoscaler agent communicates cluster state at 15-second intervals. For custom API integrations, implement exponential backoff and request queuing to avoid hitting limits.
## Prerequisites
- CAST AI API key configured
- Understanding of the API endpoints you call
## Rate Limit Behavior
| Aspect | Value |
|--------|-------|
| Rate limit scope | Per API key |
| Response on limit | HTTP 429 with `Retry-After` header |
| Agent sync interval | Every 15 seconds |
| Recommended polling | No more than once per 30 seconds |
## Instructions
### Step 1: Detect Rate Limits from Response Headers
```typescript
async function castaiRequest(path: string): Promise<Response> {
const response = await fetch(`https://api.cast.ai${path}`, {
headers: { "X-API-Key": process.env.CASTAI_API_KEY! },
});
// Log rate limit headers for monitoring
const remaining = response.headers.get("X-RateLimit-Remaining");
const reset = response.headers.get("X-RateLimit-Reset");
if (remaining) {
console.log(`Rate limit remaining: ${remaining}, resets: ${reset}`);
}
if (response.status === 429) {
const retryAfter = parseInt(response.headers.get("Retry-After") ?? "5");
throw new RateLimitError(retryAfter);
}
return response;
}
class RateLimitError extends Error {
constructor(public retryAfterSeconds: number) {
super(`Rate limited. Retry after ${retryAfterSeconds}s`);
}
}
```
### Step 2: Exponential Backoff with Jitter
```typescript
async function withBackoff<T>(
fn: () => Promise<T>,
maxRetries = 5
): Promise<T> {
for (let attempt = 0; attempt <= maxRetries; attempt++) {
try {
return await fn();
} catch (err) {
if (attempt === maxRetries) throw err;
let delayMs: number;
if (err instanceof RateLimitError) {
delayMs = err.retryAfterSeconds * 1000;
} else {
delayMs = Math.min(1000 * Math.pow(2, attempt), 30000);
}
// Add jitter to prevent thundering herd
delayMs += Math.random() * 1000;
console.log(`Retry ${attempt + 1}/${maxRetries} in ${delayMs}ms`);
await new Promise((r) => setTimeout(r, delayMs));
}
}
throw new Error("Unreachable");
}
```
### Step 3: Request Queue for Batch Operations
```typescript
import PQueue from "p-queue";
// Limit concurrent requests and enforce interval
const castaiQueue = new PQueue({
concurrency: 3,
interval: 1000,
intervalCap: 5, // Max 5 requests per second
});
async function queuedCastAIRequest<T>(fn: () => Promise<T>): Promise<T> {
return castaiQueue.add(() => withBackoff(fn));
}
// Batch process multiple clusters
const clusterIds = ["id1", "id2", "id3", "id4", "id5"];
const savings = await Promise.all(
clusterIds.map((id) =>
queuedCastAIRequest(() =>
fetch(`https://api.cast.ai/v1/kubernetes/clusters/${id}/savings`, {
headers: { "X-API-Key": process.env.CASTAI_API_KEY! },
}).then((r) => r.json())
)
)
);
```
### Step 4: Polling Best Practice
```typescript
// Do NOT poll faster than 30 seconds for cluster state
// The agent syncs every 15s; polling faster adds no value
async function pollClusterStatus(
clusterId: string,
intervalMs = 30000
): Promise<void> {
const timer = setInterval(async () => {
try {
const status = await queuedCastAIRequest(() =>
fetch(
`https://api.cast.ai/v1/kubernetes/external-clusters/${clusterId}`,
{ headers: { "X-API-Key": process.env.CASTAI_API_KEY! } }
).then((r) => r.json())
);
console.log(`Cluster ${clusterId}: ${status.agentStatus}`);
} catch (err) {
console.error("Poll failed:", err);
}
}, intervalMs);
}
```
## Error Handling
| Scenario | Detection | Response |
|----------|-----------|----------|
| 429 with Retry-After | Check header | Wait exact duration |
| 429 without header | Status code only | Exponential backoff from 1s |
| 5xx errors | Status >= 500 | Retry up to 3 times |
| Connection timeout | Fetch throws | Retry with longer timeout |
## Resources
- [CAST AI API Reference](https://api.cast.ai/v1/spec/openapi.json)
- [p-queue](https://github.com/sindresorhus/p-queue) -- concurrent request queue
## Next Steps
For security configuration, see `castai-security-basics`.Related Skills
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