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-sendgrid-inbound-parsegit 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-sendgrid-inbound-parse)<a href="https://agentmods.dev/skills/twilio/ai/twilio-sendgrid-inbound-parse"><img src="https://agentmods.dev/badge/skills/twilio/ai/twilio-sendgrid-inbound-parse.svg" alt="Measured on agentmods" 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 | $0.00080 | $0.00989 |
| Opus 5 | $0.00040 | $0.00495 |
| Sonnet 5 | $0.00016 | $0.00198 |
| Haiku 4.5 | $0.00008 | $0.00099 |
Grade A, and why
twilio-sendgrid-inbound-parse 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 8d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Inbound Parse converts incoming email into HTTP POST requests to your webhook endpoint. SendGrid receives the email at your domain's MX records and forwards the parsed content to your application.
Setup
- Configure MX records: Point your domain (or subdomain) to
mx.sendgrid.net - Add webhook: SendGrid Console > Settings > Inbound Parse > Add Host & URL
- Choose mode: Parsed (default) or Raw
Subdomain recommended: Use inbound.yourdomain.com to avoid disrupting existing email on yourdomain.com.
Parsed Mode (Default)
SendGrid extracts fields and POSTs them as form data:
| Field | Description |
|---|---|
from |
Sender address ("Name <[email protected]>") |
to |
Envelope recipient |
subject |
Email subject line |
text |
Plain text body |
html |
HTML body |
envelope |
JSON string with to array and from |
attachments |
Number of attachments (as string) |
attachment-info |
JSON metadata for each attachment |
attachment1, attachment2... |
Actual attachment files |
Python (Flask)
from flask import Flask, request
import json
app = Flask(__name__)
@app.route("/inbound", methods=["POST"])
def handle_inbound():
sender = request.form.get("from")
subject = request.form.get("subject")
text_body = request.form.get("text")
html_body = request.form.get("html")
envelope = json.loads(request.form.get("envelope", "{}"))
attachment_count = int(request.form.get("attachments", "0"))
print(f"From: {sender}, Subject: {subject}")
for i in range(1, attachment_count + 1):
attachment = request.files.get(f"attachment{i}")
if attachment:
print(f"Attachment: {attachment.filename}, {attachment.content_type}")
return "", 200
Security: All inbound email content (
from,subject,text,html, attachments) is untrusted external input. Sanitize HTML to prevent XSS before rendering. If feeding content to an LLM, isolate it as user input — never concatenate into system prompts. Verify webhook authenticity using signed webhooks (see Security section below).
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.
- 8d ago First seen · 104 lines · 80 tokens per session scan A 754b2e3e0997
twilio-sendgrid-inbound-parse is a skill published in the GitHub repository twilio/ai (30 stars, last pushed 24d ago), licensed MIT. It adds 80 tokens to every session and 989 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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