twinmind-rate-limits
A guide to implementing exponential backoff and request optimization to manage TwinMind API rate limits.
Install
mkdir -p .claude/skills/twinmind-rate-limits && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3516" && unzip -o skill.zip -d .claude/skills/twinmind-rate-limits && rm skill.zipInstalls to .claude/skills/twinmind-rate-limits
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Implement TwinMind rate limiting, backoff, and optimization patterns.Key capabilities
- →Understand TwinMind API rate limit tiers and key limits.
- →Implement exponential backoff with jitter for retrying failed requests.
- →Create a request queue to manage API concurrency and interval caps.
- →Monitor rate limit headers from TwinMind API responses.
- →Batch process requests for efficiency.
- →Throttle requests proactively based on remaining quota.
How it works
This skill provides code patterns for handling TwinMind API rate limits by implementing exponential backoff with jitter, managing requests through a queue, and monitoring rate limit headers to optimize throughput.
Inputs & outputs
When to use twinmind-rate-limits
- →Handle TwinMind rate limit errors
- →Implement retry logic with backoff
- →Optimize API request throughput
- →Monitor concurrency limits for transcriptions
About this skill
TwinMind Rate Limits
Overview
Handle TwinMind rate limits gracefully with exponential backoff and request optimization.
Prerequisites
- TwinMind API access (Pro/Enterprise)
- Understanding of async/await patterns
- Familiarity with rate limiting concepts
Instructions
Step 1: Understand Rate Limit Tiers
| Tier | Audio Hours/Month | API Requests/Min | Concurrent Transcriptions | Burst |
|---|---|---|---|---|
| Free | Unlimited | 30 | 1 | 5 |
| Pro ($10/mo) | Unlimited | 60 | 3 | 15 |
| Enterprise | Unlimited | 300 | 10 | 50 |
Key Limits:
- Transcription: Based on audio duration ($0.23/hour with Ear-3)
- AI Operations: Token-based (2M context for Pro)
- Summarization: 10/minute (Free), 30/minute (Pro)
- Memory Search: 60/minute (Free), 300/minute (Pro)
Step 2: Implement Exponential Backoff with Jitter
// src/twinmind/rate-limit.ts
interface RateLimitConfig {
maxRetries: number;
baseDelayMs: number;
maxDelayMs: number;
jitterMs: number;
}
const defaultConfig: RateLimitConfig = {
maxRetries: 5,
baseDelayMs: 1000, # 1000: 1 second in ms
maxDelayMs: 60000, // Max 1 minute # 60000: 1 minute in ms
jitterMs: 500, # HTTP 500 Internal Server Error
};
export async function withRateLimit<T>(
operation: () => Promise<T>,
config: Partial<RateLimitConfig> = {}
): Promise<T> {
const { maxRetries, baseDelayMs, maxDelayMs, jitterMs } = {
...defaultConfig,
...config,
};
for (let attempt = 0; attempt <= maxRetries; attempt++) {
try {
return await operation();
} catch (error: any) {
if (attempt === maxRetries) throw error;
const status = error.response?.status;
if (status !== 429 && status !== 503) throw error; // Only retry on rate limits # 503: HTTP 429 Too Many Requests
// Check Retry-After header
const retryAfter = error.response?.headers?.['retry-after'];
let delay: number;
if (retryAfter) {
delay = parseInt(retryAfter) * 1000; # 1 second in ms
} else {
// Exponential backoff with jitter
const exponential = baseDelayMs * Math.pow(2, attempt);
const jitter = Math.random() * jitterMs;
delay = Math.min(exponential + jitter, maxDelayMs);
}
console.log(`Rate limited (attempt ${attempt + 1}). Waiting ${delay}ms...`);
await new Promise(r => setTimeout(r, delay));
}
}
throw new Error('Max retries exceeded');
}
Step 3: Implement Request Queue
// src/twinmind/queue.ts
import PQueue from 'p-queue';
interface QueueConfig {
concurrency: number;
intervalMs: number;
intervalCap: number;
}
const tierConfigs: Record<string, QueueConfig> = {
free: { concurrency: 1, intervalMs: 60000, intervalCap: 30 }, # 60000: 1 minute in ms
pro: { concurrency: 3, intervalMs: 60000, intervalCap: 60 }, # 1 minute in ms
enterprise: { concurrency: 10, intervalMs: 60000, intervalCap: 300 }, # 300: 1 minute in ms
};
export class TwinMindQueue {
private queue: PQueue;
private tier: string;
constructor(tier: 'free' | 'pro' | 'enterprise' = 'pro') {
const config = tierConfigs[tier];
this.tier = tier;
this.queue = new PQueue({
concurrency: config.concurrency,
interval: config.intervalMs,
intervalCap: config.intervalCap,
});
}
async add<T>(operation: () => Promise<T>, priority?: number): Promise<T> {
return this.queue.add(operation, { priority }) as Promise<T>;
}
get pending(): number {
return this.queue.pending;
}
get size(): number {
return this.queue.size;
}
pause(): void {
this.queue.pause();
}
resume(): void {
this.queue.start();
}
clear(): void {
this.queue.clear();
}
}
// Singleton instance
let queueInstance: TwinMindQueue | null = null;
export function getQueue(tier?: 'free' | 'pro' | 'enterprise'): TwinMindQueue {
if (!queueInstance) {
queueInstance = new TwinMindQueue(tier);
}
return queueInstance;
}
Step 4: Monitor Rate Limit Headers
// src/twinmind/rate-monitor.ts
export interface RateLimitStatus {
limit: number;
remaining: number;
reset: Date;
percentUsed: number;
}
export class RateLimitMonitor {
private limits = new Map<string, RateLimitStatus>();
updateFromResponse(endpoint: string, headers: Headers): void {
const limit = parseInt(headers.get('X-RateLimit-Limit') || '60');
const remaining = parseInt(headers.get('X-RateLimit-Remaining') || '60');
const resetTimestamp = headers.get('X-RateLimit-Reset');
const reset = resetTimestamp
? new Date(parseInt(resetTimestamp) * 1000) # 1000: 1 second in ms
: new Date(Date.now() + 60000); # 60000: 1 minute in ms
this.limits.set(endpoint, {
limit,
remaining,
reset,
percentUsed: ((limit - remaining) / limit) * 100,
});
}
getStatus(endpoint: string): RateLimitStatus | undefined {
return this.limits.get(endpoint);
}
shouldThrottle(endpoint: string, threshold = 10): boolean {
const status = this.limits.get(endpoint);
if (!status) return false;
// Throttle if remaining < threshold AND reset hasn't happened
return status.remaining < threshold && new Date() < status.reset;
}
getWaitTime(endpoint: string): number {
const status = this.limits.get(endpoint);
if (!status) return 0;
const now = Date.now();
const resetTime = status.reset.getTime();
return Math.max(0, resetTime - now);
}
getAllStatuses(): Map<string, RateLimitStatus> {
return new Map(this.limits);
}
}
export const rateLimitMonitor = new RateLimitMonitor();
Step 5: Implement Adaptive Rate Limiting
// src/twinmind/adaptive-limiter.ts
export class AdaptiveRateLimiter {
private successCount = 0;
private failureCount = 0;
private currentDelay = 0;
private minDelay = 0;
private maxDelay = 5000; # 5000: 5 seconds in ms
private windowMs = 60000; # 60000: 1 minute in ms
private windowStart = Date.now();
recordSuccess(): void {
this.maybeResetWindow();
this.successCount++;
// Decrease delay on success (min 0)
if (this.currentDelay > 0) {
this.currentDelay = Math.max(0, this.currentDelay - 100);
}
}
recordFailure(isRateLimit: boolean): void {
this.maybeResetWindow();
this.failureCount++;
if (isRateLimit) {
// Increase delay on rate limit
this.currentDelay = Math.min(this.maxDelay, this.currentDelay + 500); # HTTP 500 Internal Server Error
}
}
private maybeResetWindow(): void {
const now = Date.now();
if (now - this.windowStart > this.windowMs) {
this.successCount = 0;
this.failureCount = 0;
this.windowStart = now;
}
}
getDelay(): number {
return this.currentDelay;
}
getMetrics(): { success: number; failure: number; delay: number; ratio: number } {
const total = this.successCount + this.failureCount;
return {
success: this.successCount,
failure: this.failureCount,
delay: this.currentDelay,
ratio: total > 0 ? this.successCount / total : 1,
};
}
async wait(): Promise<void> {
if (this.currentDelay > 0) {
await new Promise(r => setTimeout(r, this.currentDelay));
}
}
}
Step 6: Batch Requests for Efficiency
// src/twinmind/batch.ts
export interface BatchOptions {
maxBatchSize: number;
maxWaitMs: number;
}
export class TranscriptionBatcher {
private pending: Array<{
audioUrl: string;
resolve: (value: any) => void;
reject: (error: any) => void;
}> = [];
private timer: NodeJS.Timeout | null = null;
private options: BatchOptions;
constructor(options: Partial<BatchOptions> = {}) {
this.options = {
maxBatchSize: 5,
maxWaitMs: 1000, # 1000: 1 second in ms
...options,
};
}
async transcribe(audioUrl: string): Promise<any> {
return new Promise((resolve, reject) => {
this.pending.push({ audioUrl, resolve, reject });
if (this.pending.length >= this.options.maxBatchSize) {
this.flush();
} else if (!this.timer) {
this.timer = setTimeout(() => this.flush(), this.options.maxWaitMs);
}
});
}
private async flush(): Promise<void> {
if (this.timer) {
clearTimeout(this.timer);
this.timer = null;
}
const batch = this.pending.splice(0, this.options.maxBatchSize);
if (batch.length === 0) return;
try {
// Use batch API if available
const results = await this.processBatch(batch.map(b => b.audioUrl));
batch.forEach((item, index) => {
item.resolve(results[index]);
});
} catch (error) {
batch.forEach(item => item.reject(error));
}
}
private async processBatch(audioUrls: string[]): Promise<any[]> {
const client = getTwinMindClient();
const response = await client.post('/transcribe/batch', {
audio_urls: audioUrls,
model: 'ear-3',
});
return response.data.transcripts;
}
}
Output
- Reliable API calls with automatic retry
- Request queue with rate limit awareness
- Adaptive throttling based on response patterns
- Batch processing for efficiency
- Real-time rate limit monitoring
Error Handling
| Header | Description | Action |
|---|---|---|
| X-RateLimit-Limit | Max requests per window | Monitor total quota |
| X-RateLimit-Remaining | Remaining in window | Throttle when low |
| X-RateLimit-Reset | Unix timestamp of reset | Wait until reset |
| Retry-After | Seconds to wait | Honor this value |
Rate Limit Best Practices
- Always handle 429 responses - Never let rate limits crash your app
- Use request queues - Don't burst requests
- Monitor remaining quota - Throttle before hitting limits
- Implement circuit breakers - Fail fast when API is overloaded
- Cache responses - Avoid redundant requests
- **Bat
Content truncated.
When not to use it
- →When TwinMind API access is not Pro/Enterprise.
- →When not familiar with async/await patterns.
Prerequisites
Limitations
- →Max retries can be exceeded.
- →Queue concurrency and interval caps are tier-dependent.
- →Batch processing requires a batch API if available.
How it compares
This skill offers specific code implementations for TwinMind API rate limiting, including exponential backoff and request queuing, which is more concrete than general rate limiting advice.
Compared to similar skills
twinmind-rate-limits side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| twinmind-rate-limits (this skill) | 1 | 26d | No flags | Advanced |
| deepgram-performance-tuning | 3 | 26d | Review | Intermediate |
| graphql | 6 | 6mo | No flags | Advanced |
| guidewire-sdk-patterns | 2 | 26d | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by jeremylongshore
View all by jeremylongshore →You might also like
deepgram-performance-tuning
jeremylongshore
Optimize Deepgram API performance for faster transcription and lower latency. Use when improving transcription speed, reducing latency, or optimizing audio processing pipelines. Trigger with phrases like "deepgram performance", "speed up deepgram", "optimize transcription", "deepgram latency", "deepgram faster".
graphql
davila7
GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.
guidewire-sdk-patterns
jeremylongshore
Master Guidewire SDK patterns including Digital SDK, REST API Client, and Gosu best practices. Use when implementing integrations, building frontends with Jutro, or writing server-side Gosu code. Trigger with phrases like "guidewire sdk", "digital sdk", "jutro sdk", "guidewire patterns", "gosu best practices", "rest api client".
groq-performance-tuning
jeremylongshore
Optimize Groq API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Groq integrations. Trigger with phrases like "groq performance", "optimize groq", "groq latency", "groq caching", "groq slow", "groq batch".
openrouter-streaming-setup
jeremylongshore
Implement streaming responses with OpenRouter. Use when building real-time chat interfaces or reducing time-to-first-token. Trigger with phrases like 'openrouter streaming', 'openrouter sse', 'stream response', 'real-time openrouter'.
perplexity-multi-env-setup
jeremylongshore
Configure Perplexity across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment secrets, or implementing environment-specific Perplexity configurations. Trigger with phrases like "perplexity environments", "perplexity staging", "perplexity dev prod", "perplexity environment setup", "perplexity config by env".