home / skills / autumnsgrove / groveengine / api-integration

api-integration skill

/.claude/skills/api-integration

This skill helps you integrate external REST APIs securely with authentication, rate limiting, error handling, and caching to improve reliability.

npx playbooks add skill autumnsgrove/groveengine --skill api-integration

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: api-integration
description: Integrate external REST APIs with proper authentication, rate limiting, error handling, and caching patterns. Use when working with external APIs, building API clients, or fetching data from third-party services.
---

# API Integration Skill

## When to Activate

Activate this skill when:
- Integrating external APIs
- Building API clients or wrappers
- Handling API authentication
- Implementing rate limiting
- Caching API responses

## Core Principles

1. **Respect rate limits** - APIs are shared resources
2. **Secure authentication** - Keys in secrets.json, never in code
3. **Handle errors gracefully** - Implement retries and backoff
4. **Cache responses** - Reduce redundant requests

## Authentication Setup

### secrets.json

```json
{
  "github_token": "ghp_your_token_here",
  "openweather_api_key": "your_key_here",
  "comment": "Never commit this file"
}
```

### Python Loading

```python
import os
import json
from pathlib import Path

def load_secrets():
    secrets_path = Path(__file__).parent / "secrets.json"
    try:
        with open(secrets_path) as f:
            return json.load(f)
    except (FileNotFoundError, json.JSONDecodeError):
        return {}

secrets = load_secrets()
API_KEY = secrets.get("github_token", os.getenv("GITHUB_TOKEN", ""))

if not API_KEY:
    raise ValueError("No API key found")
```

## Request Patterns

### Basic GET (Python)

```python
import requests

def api_request(url: str, api_key: str) -> dict:
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Accept": "application/json"
    }
    response = requests.get(url, headers=headers, timeout=10)
    response.raise_for_status()
    return response.json()
```

### With Retry and Backoff

```python
import time
from typing import Optional

def api_request_with_retry(
    url: str,
    api_key: str,
    max_retries: int = 3
) -> Optional[dict]:
    headers = {"Authorization": f"Bearer {api_key}"}
    wait_time = 1

    for attempt in range(max_retries):
        try:
            response = requests.get(url, headers=headers, timeout=10)

            if response.status_code == 200:
                return response.json()
            elif response.status_code == 429:
                print(f"Rate limited. Waiting {wait_time}s...")
                time.sleep(wait_time)
                wait_time *= 2
            else:
                print(f"Error: HTTP {response.status_code}")
                return None
        except requests.exceptions.RequestException as e:
            print(f"Request failed: {e}")
            time.sleep(wait_time)
            wait_time *= 2

    return None
```

### Bash Request

```bash
#!/bin/bash
API_KEY=$(python3 -c "import json; print(json.load(open('secrets.json'))['github_token'])")

curl -s -H "Authorization: Bearer $API_KEY" \
  -H "Accept: application/json" \
  "https://api.github.com/user" | jq '.'
```

## Error Handling

```python
try:
    response = requests.get(url, headers=headers, timeout=10)
    response.raise_for_status()
    data = response.json()
except requests.exceptions.HTTPError as e:
    if e.response.status_code == 429:
        print("Rate limited - waiting")
    elif e.response.status_code == 401:
        print("Unauthorized - check API key")
    else:
        print(f"HTTP error: {e}")
except requests.exceptions.ConnectionError:
    print("Connection error")
except requests.exceptions.Timeout:
    print("Request timeout")
```

## HTTP Status Codes

| Code | Meaning | Action |
|------|---------|--------|
| 200 | Success | Process response |
| 401 | Unauthorized | Check API key |
| 403 | Forbidden | Check permissions |
| 404 | Not found | Verify endpoint |
| 429 | Rate limited | Wait and retry |
| 5xx | Server error | Retry with backoff |

## Caching

```python
import time

cache = {}
CACHE_TTL = 3600  # 1 hour

def cached_request(url: str, api_key: str) -> dict:
    now = time.time()

    if url in cache:
        data, timestamp = cache[url]
        if now - timestamp < CACHE_TTL:
            return data

    data = api_request(url, api_key)
    cache[url] = (data, now)
    return data
```

## Rate Limiting

### Check Headers

```bash
curl -I -H "Authorization: Bearer $API_KEY" "https://api.github.com/user" | grep -i rate
# x-ratelimit-limit: 5000
# x-ratelimit-remaining: 4999
```

### Implement Delays

```python
import time

def bulk_requests(urls: list, api_key: str, delay: float = 1.0):
    results = []
    for url in urls:
        result = api_request(url, api_key)
        results.append(result)
        time.sleep(delay)
    return results
```

## Pagination

```python
def fetch_all_pages(base_url: str, api_key: str) -> list:
    all_items = []
    page = 1

    while True:
        url = f"{base_url}?page={page}&per_page=100"
        data = api_request(url, api_key)

        if not data:
            break

        all_items.extend(data)
        page += 1
        time.sleep(1)  # Respect rate limits

    return all_items
```

## Best Practices

### DO ✅
- Store keys in secrets.json
- Implement retry with exponential backoff
- Cache responses when appropriate
- Respect rate limits
- Handle errors gracefully
- Log requests (without sensitive data)

### DON'T ❌
- Hardcode API keys
- Ignore rate limits
- Skip error handling
- Make requests in tight loops
- Log API keys

## API Etiquette Checklist

- [ ] Read API documentation and ToS
- [ ] Check rate limits
- [ ] Store keys securely
- [ ] Implement rate limiting
- [ ] Add error handling
- [ ] Cache appropriately
- [ ] Monitor usage

## Related Resources

See `AgentUsage/api_usage.md` for complete documentation including:
- Bash request patterns
- Conditional requests (ETags)
- Advanced caching strategies
- Specific API examples (GitHub, OpenWeather)

Overview

This skill helps integrate external REST APIs into a multi-tenant TypeScript blog platform with secure authentication, rate limiting, error handling, and caching patterns. It focuses on building resilient API clients, protecting secrets, and reducing latency and costs through smart retries and caching. Use it to standardize third-party data access across tenants and features.

How this skill works

The skill provides patterns for loading secrets safely, composing authenticated HTTP requests, and wrapping calls with retry, exponential backoff, and rate-limit awareness. It includes caching strategies (TTL-based), pagination handling, and common error-handling flows for HTTP status codes and network failures. Implementations are language-agnostic but mapped to TypeScript best practices for client wrappers and middleware.

When to use it

  • Fetching third-party data for posts, analytics, or enrichments in a multi-tenant blog
  • Building reusable API client modules or wrappers for external services
  • Protecting and rotating API keys and other credentials
  • Preventing tenant spikes from triggering provider rate limits
  • Improving reliability with retries, backoff, and structured error handling

Best practices

  • Keep credentials out of source; load from environment or a secure secret store at runtime
  • Implement retries with exponential backoff and limit total retry time
  • Respect provider rate-limit headers and apply client-side throttling per tenant
  • Cache safe-to-cache responses with TTL and invalidate on updates
  • Log requests and errors without recording secrets or sensitive tenant data

Example use cases

  • Server-side job that enriches blog posts with metadata from an external content API while caching results per slug
  • Multi-tenant webhook consumer that validates signatures, queues processing, and rate-limits downstream API calls per tenant
  • Admin dashboard fetching paginated analytics from a provider with a client that transparently paginates and caches results
  • Background sync that retries transient failures with exponential backoff and records metrics for monitoring
  • Batch import that respects x-ratelimit headers and spaces requests to avoid 429 responses

FAQ

How should I store API keys for a multi-tenant app?

Store keys in a secrets manager or environment variables per deployment or tenant; never commit keys. For tenant-level keys, encrypt at rest and load them on demand.

How do I handle provider rate limits across tenants?

Track usage per provider and per tenant, honor rate-limit headers, and implement token-bucket or fixed-delay throttling. Queue or back off requests when limits approach to avoid global failures.