twilio-communications
Build communication features with Twilio: SMS messaging, voice calls, WhatsApp Business API, and user verification (2FA). Covers the full spectrum from simple notifications to complex IVR systems and multi-channel authentication. Critical focus on compliance, rate limits, and error handling. Use when: twilio, send SMS, text message, voice call, phone verification.
What this skill does
# Twilio Communications
## Patterns
### SMS Sending Pattern
Basic pattern for sending SMS messages with Twilio.
Handles the fundamentals: phone number formatting, message delivery,
and delivery status callbacks.
Key considerations:
- Phone numbers must be in E.164 format (+1234567890)
- Default rate limit: 80 messages per second (MPS)
- Messages over 160 characters are split (and cost more)
- Carrier filtering can block messages (especially to US numbers)
**When to use**: ['Sending notifications to users', 'Transactional messages (order confirmations, shipping)', 'Alerts and reminders']
```python
from twilio.rest import Client
from twilio.base.exceptions import TwilioRestException
import os
import re
class TwilioSMS:
"""
SMS sending with proper error handling and validation.
"""
def __init__(self):
self.client = Client(
os.environ["TWILIO_ACCOUNT_SID"],
os.environ["TWILIO_AUTH_TOKEN"]
)
self.from_number = os.environ["TWILIO_PHONE_NUMBER"]
def validate_e164(self, phone: str) -> bool:
"""Validate phone number is in E.164 format."""
pattern = r'^\+[1-9]\d{1,14}$'
return bool(re.match(pattern, phone))
def send_sms(
self,
to: str,
body: str,
status_callback: str = None
) -> dict:
"""
Send an SMS message.
Args:
to: Recipient phone number in E.164 format
body: Message text (160 chars = 1 segment)
status_callback: URL for delivery status webhooks
Returns:
Message SID and status
"""
# Validate phone number format
if not self.validate_e164(to):
return {
"success": False,
"error": "Phone number must be in E.164 format (+1234567890)"
}
# Check message length (warn about segmentation)
segment_count = (len(body) + 159) // 160
if segment_count > 1:
print(f"Warning: Message will be sent as {segment_count} segments")
try:
message = self.client.messages.create(
to=to,
from_=self.from_number,
body=body,
status_callback=status_callback
)
return {
"success": True,
"message_sid": message.sid,
"status": message.status,
"segments": segment_count
}
except TwilioRestException as e:
return self._handle_error(e)
def _handle_error(self, error: Twilio
```
### Twilio Verify Pattern (2FA/OTP)
Use Twilio Verify for phone number verification and 2FA.
Handles code generation, delivery, rate limiting, and fraud prevention.
Key benefits over DIY OTP:
- Twilio manages code generation and expiration
- Built-in fraud prevention (saved customers $82M+ blocking 747M attempts)
- Handles rate limiting automatically
- Multi-channel: SMS, Voice, Email, Push, WhatsApp
Google found SMS 2FA blocks "100% of automated bots, 96% of bulk
phishing attacks, and 76% of targeted attacks."
**When to use**: ['User phone number verification at signup', 'Two-factor authentication (2FA)', 'Password reset verification', 'High-value transaction confirmation']
```python
from twilio.rest import Client
from twilio.base.exceptions import TwilioRestException
import os
from enum import Enum
from typing import Optional
class VerifyChannel(Enum):
SMS = "sms"
CALL = "call"
EMAIL = "email"
WHATSAPP = "whatsapp"
class TwilioVerify:
"""
Phone verification with Twilio Verify.
Never store OTP codes - Twilio handles it.
"""
def __init__(self, verify_service_sid: str = None):
self.client = Client(
os.environ["TWILIO_ACCOUNT_SID"],
os.environ["TWILIO_AUTH_TOKEN"]
)
# Create a Verify Service in Twilio Console first
self.service_sid = verify_service_sid or os.environ["TWILIO_VERIFY_SID"]
def send_verification(
self,
to: str,
channel: VerifyChannel = VerifyChannel.SMS,
locale: str = "en"
) -> dict:
"""
Send verification code to phone/email.
Args:
to: Phone number (E.164) or email
channel: SMS, call, email, or whatsapp
locale: Language code for message
Returns:
Verification status
"""
try:
verification = self.client.verify \
.v2 \
.services(self.service_sid) \
.verifications \
.create(
to=to,
channel=channel.value,
locale=locale
)
return {
"success": True,
"status": verification.status, # "pending"
"channel": channel.value,
"valid": verification.valid
}
except TwilioRestException as e:
return self._handle_verify_error(e)
def check_verification(self, to: str, code: str) -> dict:
"""
Check if verification code is correct.
Args:
to: Phone number or email that received code
code: The code entered by user
R
```
### TwiML IVR Pattern
Build Interactive Voice Response (IVR) systems using TwiML.
TwiML (Twilio Markup Language) is XML that tells Twilio what to do
when receiving calls.
Core TwiML verbs:
- <Say>: Text-to-speech
- <Play>: Play audio file
- <Gather>: Collect keypad/speech input
- <Dial>: Connect to another number
- <Record>: Record caller's voice
- <Redirect>: Move to another TwiML endpoint
Key insight: Twilio makes HTTP request to your webhook, you return
TwiML, Twilio executes it. Stateless, so use URL params or sessions.
**When to use**: ['Phone menu systems (press 1 for sales...)', 'Automated customer support', 'Appointment reminders with confirmation', 'Voicemail systems']
```python
from flask import Flask, request, Response
from twilio.twiml.voice_response import VoiceResponse, Gather
from twilio.request_validator import RequestValidator
import os
app = Flask(__name__)
def validate_twilio_request(f):
"""Decorator to validate requests are from Twilio."""
def wrapper(*args, **kwargs):
validator = RequestValidator(os.environ["TWILIO_AUTH_TOKEN"])
# Get request details
url = request.url
params = request.form.to_dict()
signature = request.headers.get("X-Twilio-Signature", "")
if not validator.validate(url, params, signature):
return "Invalid request", 403
return f(*args, **kwargs)
wrapper.__name__ = f.__name__
return wrapper
@app.route("/voice/incoming", methods=["POST"])
@validate_twilio_request
def incoming_call():
"""Handle incoming call with IVR menu."""
response = VoiceResponse()
# Gather digits with timeout
gather = Gather(
num_digits=1,
action="/voice/menu-selection",
method="POST",
timeout=5
)
gather.say(
"Welcome to Acme Corp. "
"Press 1 for sales. "
"Press 2 for support. "
"Press 3 to leave a message."
)
response.append(gather)
# If no input, repeat
response.redirect("/voice/incoming")
return Response(str(response), mimetype="text/xml")
@app.route("/voice/menu-selection", methods=["POST"])
@validate_twilio_request
def menu_selection():
"""Route based on menu selection."""
response = VoiceResponse()
digit = request.form.get("Digits", "")
if digit == "1":
# Transfer to sales
response.say("Connecting you to sales.")
response.dial(os.environ["SALES_PHONE"])
elif digit == "2":
# Transfer to support
response.say("Connecting you to support.")
response.dial(os.environ["SUPPORT_PHONE"])
elif digit == "3":
# Voicemail
response.say("PlRelated in Image & Video
watch
IncludedWatch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
physical-ai-defect-image-generation
IncludedUse when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
accelint-react-best-practices
IncludedReact performance optimization and best practices. ALWAYS use this skill when working with any React code - writing components, hooks, JSX; refactoring; optimizing re-renders, memoization, state management; reviewing for performance; fixing hydration mismatches; debugging infinite re-renders, stale closures, input focus loss, animations restarting; preventing remounting; implementing transitions, lazy initialization, effect dependencies. Even simple React tasks benefit from these patterns. Covers React 19+ (useEffectEvent, Activity, ref props). Triggers - useEffect, useState, useMemo, useCallback, memo, inline components, nested components, components inside components, re-render, performance, hydration, SSR, Next.js, useDeferredValue, combined hooks.
elevenlabs-agents
IncludedBuild conversational AI voice agents with ElevenLabs Platform using React, JavaScript, React Native, or Swift SDKs. Configure agents, tools (client/server/MCP), RAG knowledge bases, multi-voice, and Scribe real-time STT. Use when: building voice chat interfaces, implementing AI phone agents with Twilio, configuring agent workflows or tools, adding RAG knowledge bases, testing with CLI "agents as code", or troubleshooting deprecated @11labs packages, Android audio cutoff, CSP violations, dynamic variables, or WebRTC config. Keywords: ElevenLabs Agents, ElevenLabs voice agents, AI voice agents, conversational AI, @elevenlabs/react, @elevenlabs/client, @elevenlabs/react-native, @elevenlabs/elevenlabs-js, @elevenlabs/agents-cli, elevenlabs SDK, voice AI, TTS, text-to-speech, ASR, speech recognition, turn-taking model, WebRTC voice, WebSocket voice, ElevenLabs conversation, agent system prompt, agent tools, agent knowledge base, RAG voice agents, multi-voice agents, pronunciation dictionary, voice speed control, elevenlabs scribe, @11labs deprecated, Android audio cutoff, CSP violation elevenlabs, dynamic variables elevenlabs, case-sensitive tool names, webhook authentication
humanizer
IncludedHumanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
generating-mermaid-diagrams
IncludedSalesforce architecture diagrams using Mermaid with ASCII fallback. Use this skill when generating text-based diagrams for Salesforce architecture, OAuth flows, ERDs, integration sequences, or Agentforce structure. TRIGGER when: user says "diagram", "visualize", "ERD", or asks for sequence diagrams, flowcharts, class diagrams, or architecture visualizations in Mermaid. DO NOT TRIGGER when: user wants PNG/SVG image output (use generating-visual-diagrams), or asks about non-Salesforce systems.