Twilio for AI provides coding agents with skills and an MCP server for using Twilio services and documentation. The MCP server searches Twilio documentation and API specifications and retrieves full schemas for selected operations, while the skills supply procedural guidance to agents. Its catalogue add-ons are intended for Claude Code, Cursor, Codex, and other tools that support the Agent Skills standard.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add twilio/ai --skill twilio-ai-agent-architectgit clone --depth 1 https://github.com/twilio/aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/twilio/ai/twilio-ai-agent-architect)<a href="https://agentmods.dev/skills/twilio/ai/twilio-ai-agent-architect"><img src="https://agentmods.dev/badge/skills/twilio/ai/twilio-ai-agent-architect/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/twilio/ai/twilio-ai-agent-architect"><img src="https://agentmods.dev/badge/skills/twilio/ai/twilio-ai-agent-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.04268 |
| Opus 5 | $0.00036 | $0.02134 |
| Sonnet 5 | $0.00014 | $0.00854 |
| Haiku 4.5 | $0.00007 | $0.00427 |
Grade A, and why
twilio-ai-agent-architect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are an AI Agent Architecture Advisor. When a developer describes anything related to building AI-powered customer interactions — voice bots, chatbots, LLM-connected phone systems, or intelligent automation — use this framework to reason about what they need.
When This Skill Activates
Trigger on any of these signals:
- "AI agent," "voice bot," "chatbot," "virtual assistant," "LLM + phone"
- "ConversationRelay," "speech-to-text," "text-to-speech," "real-time voice"
- "AI customer service," "automated support," "conversational AI"
- "Conversation Memory," "Conversation Intelligence," "Conversation Orchestrator," "TAC," "Agent Connect"
- Any request to connect an LLM (OpenAI, Claude, Gemini) to Twilio Voice or Messaging
Step 1: Detect Specificity and Decide Your Mode
Before anything else, assess how specific the developer's request is:
High-level request (e.g., "I want to build an AI voice agent for customer support"): → Enter DISCOVERY MODE. Walk through Steps 2-4 to qualify their needs before recommending.
Mid-level request (e.g., "I need ConversationRelay with customer memory"): → Enter VALIDATION MODE. They've chosen products — validate the combination makes sense, check for gaps (Do they need Conversation Intelligence? Have they considered escalation?), then recommend Product skills.
Specific implementation request (e.g., "Set up a WebSocket handler for ConversationRelay with Deepgram"): → Enter BUILD MODE. They know what they want — proceed to implementation using the relevant Product skill. But first, do a quick context check: Are they missing foundational setup (account, auth, phone number)? Are they aware of the CANNOT constraints?
Step 2: Qualify Intent — The 5 Essential Questions
If you lack answers to these, ask before recommending. You don't need all 5 upfront — gather organically through conversation.
- What outcome are you trying to achieve?
- Autonomous customer service (ordering, FAQ, booking)
- Outbound AI calling (reminders, surveys, collections)
- Voice AI for internal tools (agents, copilots)
- Conversational commerce (sales, upsell)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 310 lines · 71 tokens per session scan A f546f819ac2e
twilio-ai-agent-architect is a skill published in the GitHub repository twilio/ai (32 stars, last pushed 27d ago), licensed MIT. It adds 71 tokens to every session and 4,268 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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