Skip to main content الرئيسية المنشئون jeremylongshore tons-of-skills-marketplace twinmind-rate-limits
twinmind-rate-limits Implement TwinMind rate limiting, backoff, and optimization patterns.
Use when handling rate limit errors, implementing retry logic,
or optimizing API request throughput for TwinMind.
Trigger with phrases like "twinmind rate limit", "twinmind throttling",
"twinmind 429", "twinmind retry", "twinmind backoff".
الانتقال إلى التثبيت سوق المهارات اكتشف واستكشف مهارات الذكاء الاصطناعي التي بناها المجتمع.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill twinmind-rate-limitsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
تحميل Zip جاري التحميل... المزيد من هذا المستودع langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
langchain-langgraph-agents Build a correct LangGraph 1.0 ReAct agent with create_react_agent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. Use when writing a first tool-calling agent, migrating from AgentExecutor or initialize_agent, or diagnosing an agent that loops on vague prompts. Trigger with "langgraph agent", "create_react_agent", "langgraph tool calling", "AgentExecutor migration", or "agent loop cost".
langchain-langgraph-human-in-loop Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before /
interrupt_after and Command(resume=...) — JSON-serializable state, clean
resume semantics, and UI wiring for approval decisions. Use when adding an
approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
name twinmind-rate-limits description Implement TwinMind rate limiting, backoff, and optimization patterns.
Use when handling rate limit errors, implementing retry logic,
or optimizing API request throughput for TwinMind.
Trigger with phrases like "twinmind rate limit", "twinmind throttling",
"twinmind 429", "twinmind retry", "twinmind backoff".
allowed-tools Read, Write, Edit version 1.13.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","twinmind","api"] compatibility Designed for Claude Code
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
interface RateLimitConfig {
maxRetries : number ;
baseDelayMs : number ;
maxDelayMs : number ;
jitterMs : number ;
}
const defaultConfig : RateLimitConfig = {
maxRetries : 5 ,
baseDelayMs : 1000 , # 1000 : 1 second in ms
maxDelayMs : 60000 ,
jitterMs : 500
HTTP
500
Internal
Server
Error
export
async
function
operation
() =>
Promise
config
Partial
RateLimitConfig
Promise
const
for
let
0
try
return
await
operation
catch
error
any
if
throw
const
response
status
if
429
503
throw
const
response
headers
'retry-after'
let
delay
number
if
parseInt
1000
1
in
else
const
Math
pow
2
const
Math
random
Math
min
console
log
`Rate limited (attempt ${attempt + 1 } ). Waiting ${delay} ms...`
await
new
Promise
r =>
setTimeout
throw
new
Error
'Max retries exceeded'
Step 3: Implement Request Queue
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 ();
}
}
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
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 ;
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
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 ++;
if (this .currentDelay > 0 ) {
this .currentDelay = Math .max (0 , this .currentDelay - 100 );
}
}
recordFailure (isRateLimit : boolean ): void {
this .maybeResetWindow ();
this .failureCount ++;
if (isRateLimit) {
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
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 {
const results = await this .processBatch (batch.map (b =>audioUrl ));
batch.forEach ((item, index ) => {
item.resolve (results[index]);
});
} catch (error) {
batch.forEach (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 appUse request queues - Don't burst requestsMonitor remaining quota - Throttle before hitting limitsImplement circuit breakers - Fail fast when API is overloadedCache responses - Avoid redundant requestsBatch when possible - Reduce total request count
Resources
Next Steps For security configuration, see twinmind-security-basics.
Examples Basic usage : Apply twinmind rate limits to a standard project setup with default configuration options.
Advanced scenario : Customize twinmind rate limits for production environments with multiple constraints and team-specific requirements.
