castai-sdk-patterns
Production-ready CAST AI REST API wrapper patterns in TypeScript and Python. Use when building reusable CAST AI clients, implementing retry logic, or wrapping the CAST AI API for team use. Trigger with phrases like "cast ai API patterns", "cast ai client wrapper", "cast ai TypeScript", "cast ai Python client".
Best use case
castai-sdk-patterns is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Production-ready CAST AI REST API wrapper patterns in TypeScript and Python. Use when building reusable CAST AI clients, implementing retry logic, or wrapping the CAST AI API for team use. Trigger with phrases like "cast ai API patterns", "cast ai client wrapper", "cast ai TypeScript", "cast ai Python client".
Teams using castai-sdk-patterns 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-sdk-patterns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How castai-sdk-patterns Compares
| Feature / Agent | castai-sdk-patterns | 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?
Production-ready CAST AI REST API wrapper patterns in TypeScript and Python. Use when building reusable CAST AI clients, implementing retry logic, or wrapping the CAST AI API for team use. Trigger with phrases like "cast ai API patterns", "cast ai client wrapper", "cast ai TypeScript", "cast ai Python client".
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.
SKILL.md Source
# CAST AI SDK Patterns
## Overview
CAST AI uses a REST API with `X-API-Key` header authentication. There is no official SDK -- build typed wrappers around `fetch` or `requests`. These patterns cover singleton clients, typed responses, retry with backoff, and multi-cluster management.
## Prerequisites
- Completed `castai-install-auth` setup
- TypeScript 5+ or Python 3.10+
- Familiarity with async/await patterns
## Instructions
### Step 1: TypeScript API Client
```typescript
// src/castai/client.ts
interface CastAIConfig {
apiKey: string;
baseUrl?: string;
timeoutMs?: number;
}
interface CastAICluster {
id: string;
name: string;
status: string;
providerType: "eks" | "gke" | "aks";
agentStatus: string;
createdAt: string;
}
interface CastAISavings {
monthlySavings: number;
savingsPercentage: number;
currentMonthlyCost: number;
optimizedMonthlyCost: number;
}
interface CastAINode {
name: string;
instanceType: string;
lifecycle: "on-demand" | "spot";
allocatableCpu: string;
allocatableMemory: string;
zone: string;
}
class CastAIClient {
private apiKey: string;
private baseUrl: string;
private timeoutMs: number;
constructor(config: CastAIConfig) {
this.apiKey = config.apiKey;
this.baseUrl = config.baseUrl ?? "https://api.cast.ai";
this.timeoutMs = config.timeoutMs ?? 30000;
}
private async request<T>(path: string, options?: RequestInit): Promise<T> {
const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(), this.timeoutMs);
try {
const response = await fetch(`${this.baseUrl}${path}`, {
...options,
headers: {
"X-API-Key": this.apiKey,
"Content-Type": "application/json",
...options?.headers,
},
signal: controller.signal,
});
if (!response.ok) {
const body = await response.text();
throw new CastAIError(response.status, body, path);
}
return response.json();
} finally {
clearTimeout(timeout);
}
}
async listClusters(): Promise<CastAICluster[]> {
const data = await this.request<{ items: CastAICluster[] }>(
"/v1/kubernetes/external-clusters"
);
return data.items;
}
async getSavings(clusterId: string): Promise<CastAISavings> {
return this.request(`/v1/kubernetes/clusters/${clusterId}/savings`);
}
async listNodes(clusterId: string): Promise<CastAINode[]> {
const data = await this.request<{ items: CastAINode[] }>(
`/v1/kubernetes/external-clusters/${clusterId}/nodes`
);
return data.items;
}
async updatePolicies(clusterId: string, policies: Record<string, unknown>): Promise<void> {
await this.request(`/v1/kubernetes/clusters/${clusterId}/policies`, {
method: "PUT",
body: JSON.stringify(policies),
});
}
}
class CastAIError extends Error {
constructor(
public readonly status: number,
public readonly body: string,
public readonly path: string
) {
super(`CAST AI ${status} on ${path}: ${body}`);
this.name = "CastAIError";
}
get retryable(): boolean {
return this.status === 429 || this.status >= 500;
}
}
```
### Step 2: Singleton with Retry
```typescript
// src/castai/index.ts
let instance: CastAIClient | null = null;
export function getCastAIClient(): CastAIClient {
if (!instance) {
if (!process.env.CASTAI_API_KEY) {
throw new Error("CASTAI_API_KEY environment variable required");
}
instance = new CastAIClient({ apiKey: process.env.CASTAI_API_KEY });
}
return instance;
}
export async function withRetry<T>(
fn: () => Promise<T>,
maxRetries = 3
): Promise<T> {
for (let attempt = 0; attempt <= maxRetries; attempt++) {
try {
return await fn();
} catch (err) {
if (attempt === maxRetries) throw err;
if (err instanceof CastAIError && !err.retryable) throw err;
const delay = 1000 * Math.pow(2, attempt) + Math.random() * 500;
await new Promise((r) => setTimeout(r, delay));
}
}
throw new Error("Unreachable");
}
```
### Step 3: Python Client
```python
# castai_client.py
import os
import time
import requests
from dataclasses import dataclass
from typing import Optional
@dataclass
class CastAIConfig:
api_key: str
base_url: str = "https://api.cast.ai"
timeout: int = 30
class CastAIClient:
def __init__(self, config: Optional[CastAIConfig] = None):
self.config = config or CastAIConfig(
api_key=os.environ["CASTAI_API_KEY"]
)
self.session = requests.Session()
self.session.headers.update({
"X-API-Key": self.config.api_key,
"Content-Type": "application/json",
})
def _get(self, path: str) -> dict:
resp = self.session.get(
f"{self.config.base_url}{path}",
timeout=self.config.timeout,
)
resp.raise_for_status()
return resp.json()
def list_clusters(self) -> list[dict]:
return self._get("/v1/kubernetes/external-clusters")["items"]
def get_savings(self, cluster_id: str) -> dict:
return self._get(f"/v1/kubernetes/clusters/{cluster_id}/savings")
def list_nodes(self, cluster_id: str) -> list[dict]:
return self._get(
f"/v1/kubernetes/external-clusters/{cluster_id}/nodes"
)["items"]
def get_policies(self, cluster_id: str) -> dict:
return self._get(f"/v1/kubernetes/clusters/{cluster_id}/policies")
```
## Error Handling
| Status | Meaning | Action |
|--------|---------|--------|
| 401 | Invalid API key | Rotate key at console.cast.ai |
| 403 | Insufficient permissions | Use Full Access key |
| 404 | Cluster not found | Verify cluster ID |
| 429 | Rate limited | Backoff and retry |
| 5xx | Server error | Retry with exponential backoff |
## Resources
- [CAST AI OpenAPI Spec](https://api.cast.ai/v1/spec/openapi.json)
- [CAST AI Terraform Provider Source](https://github.com/castai/terraform-provider-castai)
## Next Steps
Apply these patterns in `castai-core-workflow-a` to manage cluster optimization.Related Skills
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