Sample Strands Agent with Amazon Bedrock AgentCore is an end-to-end reference architecture for building multi-agent chatbots on AWS. Teams use it to explore agent orchestration, tool execution, memory, browser automation, and agent-to-agent collaboration with Strands Agents and Bedrock AgentCore.
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 aws-samples/sample-strands-agent-with-agentcore --skill gmailgit clone --depth 1 https://github.com/aws-samples/sample-strands-agent-with-agentcoreWrote 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/aws-samples/sample-strands-agent-with-agentcore/gmail)<a href="https://agentmods.dev/skills/aws-samples/sample-strands-agent-with-agentcore/gmail"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-strands-agent-with-agentcore/gmail/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/aws-samples/sample-strands-agent-with-agentcore/gmail"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-strands-agent-with-agentcore/gmail.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 | $0.00014 | $0.01481 |
| Opus 5.5 | $0.00006 | $0.00592 |
| Sonnet 5.5 | $0.00003 | $0.00296 |
| Haiku 4.5 | $0.00001 | $0.00148 |
Grade A, and why
gmail 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 1mo 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gmail
Available Tools
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list_labels(): List all Gmail labels (system: INBOX, SENT, TRASH, SPAM, DRAFT, IMPORTANT, STARRED, UNREAD; plus user-created labels).
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list_emails(label?, max_results?, include_spam_trash?): List emails by label.
label(string, optional, default: "INBOX"): Label to filter by (INBOX, SENT, DRAFT, TRASH, SPAM, STARRED, IMPORTANT, or custom label ID)max_results(integer, optional, default: 10, max: 100): Number of emails to returninclude_spam_trash(boolean, optional, default: false): Include SPAM and TRASH
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search_emails(query, max_results?): Search Gmail using query syntax.
query(string, required): Gmail search query (from:, to:, subject:, is:unread, has:attachment, after:, before:, older_than:, newer_than:)max_results(integer, optional, default: 10, max: 50): Number of results
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read_email(message_id): Read a full email including body and attachments.
message_id(string, required): Gmail message ID (from list_emails or search_emails)
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send_email(to, subject, body, cc?, bcc?, reply_to?, in_reply_to?, html_body?): Send an email.
to(string, required): Recipient(s), comma-separatedsubject(string, required): Email subjectbody(string, required): Plain text email bodycc(string, optional): CC recipient(s)bcc(string, optional): BCC recipient(s)reply_to(string, optional): Reply-To addressin_reply_to(string, optional): Message-ID to reply to (for threading)html_body(string, optional): HTML version of email body
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draft_email(to, subject, body, cc?, bcc?, reply_to?, in_reply_to?, html_body?): Create a draft for later editing/sending. Same parameters as
send_email. -
delete_email(message_id, permanent?): Delete an email.
message_id(string, required): Gmail message IDpermanent(boolean, optional, default: false): If true, permanently delete; if false, move to Trash
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bulk_delete_emails(query, reason, max_delete?): Permanently delete multiple emails by query. Cannot be undone.
query(string, required): Gmail search query to find emailsreason(string, required): Human-readable explanation (shown to user for approval)max_delete(integer, optional, default: 50, max: 100): Maximum emails to delete
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.
- 1mo ago First seen · 112 lines · 14 tokens per session scan A f654f83fc629
gmail is a skill published in the GitHub repository aws-samples/sample-strands-agent-with-agentcore (195 stars, last pushed 3d ago), licensed MIT. It adds 14 tokens to every session and 1,481 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
browser-use
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers hosted cloud browsers for AI agents: the browser-use Python library (Browser(usecloud=True)), Playwright, Puppeteer or any CDP client.
pinchtab-mcp
Use this skill when a task requires browser automation through PinchTab's MCP server connected to a remote browser instance. Covers navigation, element interaction, data extraction, form filling, multi-step flows, and session management via MCP tools.
gog-workspace
Use the gog CLI for Google Workspace tasks across Gmail, Calendar, Drive, Docs, Sheets, Contacts, and related services. Use when the user asks to check email, search Gmail, inspect calendar events, find Drive files, read Docs or Sheets, or manage Google Workspace data through gog.
apple-calendar
Read macOS Calendar events via the icalBuddy CLI and create events via AppleScript (osascript). Use to check the user's calendar, agenda, upcoming events, or add an event on macOS.
apple-reminders
Manage Apple Reminders via the remindctl CLI on macOS — list, add, complete, delete, manage lists.
imsg
Use imsg to read and send Messages.app iMessage/SMS on macOS.