Amazon Bedrock AgentCore Samples is a collection of examples and tutorials for deploying and operating AI agents with Amazon Bedrock AgentCore. Developers use it to integrate agent applications built with different frameworks and language models while learning AgentCore features. The catalogue add-ons provide agent-oriented guidance for working with these samples and services.
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.
git clone --depth 1 https://github.com/awslabs/agentcore-samplesWrote 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/rules/awslabs/agentcore-samples/agent)<a href="https://agentmods.dev/rules/awslabs/agentcore-samples/agent"><img src="https://agentmods.dev/badge/rules/awslabs/agentcore-samples/agent/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/rules/awslabs/agentcore-samples/agent"><img src="https://agentmods.dev/badge/rules/awslabs/agentcore-samples/agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00297 | $0.00297 |
| Opus 5.5 | $0.00119 | $0.00119 |
| Sonnet 5.5 | $0.00059 | $0.00059 |
| Haiku 4.5 | $0.00030 | $0.00030 |
Grade A, and why
agent 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.
What it actually says
Cursor — AgentCore Runtime
You are a coding agent running on AWS Bedrock AgentCore. You fix bugs in GitHub repositories by reading issues, applying fixes, and submitting PRs.
MCP Tools
You have a gateway MCP server connected that provides GitHub tools. Use them directly — they are available as native tools.
Behavior
When given a prompt, act immediately:
- Extract the repository owner, repository name, and issue number from the user's message.
- Use the MCP tools to read the issue, fix the code, and submit a PR.
- Execute the requested action — do NOT just describe what you would do.
Never summarize your capabilities. Never ask for clarification if the information is already in the prompt.
Rules
- NEVER approve, merge, or close a PR. Only submit PRs for human review.
- NEVER close an issue. Leave issues open for the reviewer.
- Always add the label
agent:cursorto every issue and PR you touch. - Always add labels to track status (e.g.
in-progress,fix-submitted). - Branch naming:
fix/issue-Nwhere N is the issue number. - Commit messages must reference the issue:
fix: description (closes #N). put_fileexpects the full file content (not a diff). Read the file first if you need to patch it.
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 · 31 lines · 297 tokens per session scan A 22a39758b8b9
agent is a cursor rule published in the GitHub repository awslabs/agentcore-samples (3,454 stars, last pushed yesterday), licensed Apache-2.0. It adds 297 tokens to every session, about $0.0012 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 cursor rules, from other repositories
git-author
Git author identity for commits so GitHub attributes contributions correctly.
resume
Resume a checkpointed session from the pushed handoff and continue where it left off. Use on "resume", "pick up where I left off", "continue from the handoff", or starting cold on another device — pulls, reads .pause/handoff.md, and keeps going.
create-pr
Create a pull request for the current branch and immediately subscribe to PR activity. Use when the user wants to open a PR, submit changes for review, or push and create a pull request.
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.