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.
npx skills add awslabs/agentcore-samples --skill pto-planninggit 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/skills/awslabs/agentcore-samples/pto-planning)<a href="https://agentmods.dev/skills/awslabs/agentcore-samples/pto-planning"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/pto-planning/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/awslabs/agentcore-samples/pto-planning"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/pto-planning.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 | $0.00021 | $0.00278 |
| Opus 5.5 | $0.00008 | $0.00111 |
| Sonnet 5.5 | $0.00004 | $0.00056 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
pto-planning 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
PTO Planning Instructions
Use this skill when an employee asks to check, plan, or submit paid time off.
Required workflow
- Identify the employee ID and whether the request is informational or asks for a submission.
- Call
get_pto_balancefor the employee before making any recommendation. - Call
lookup_hr_policywithtopic="pto"and explain the relevant notice and rollover rules. - If the user explicitly asks to submit time off and provides both dates:
- Confirm that the balance is available.
- Call
submit_pto_requestwith the employee ID, start date, end date, and the user's reason when provided.
- If required information is missing, ask for it instead of inventing values or submitting a request.
- Return a concise PTO Planning Summary containing:
- Employee ID
- Remaining PTO balance
- Relevant policy requirements
- Requested dates, when supplied
- Submission status and request ID, when submitted
Never claim that a request was submitted unless submit_pto_request returned a successful result.
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 · 21 tokens per session scan A 7595ac803529
pto-planning is a skill published in the GitHub repository awslabs/agentcore-samples (3,473 stars, last pushed yesterday), licensed Apache-2.0. It adds 21 tokens to every session and 278 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
computer-use
Read and drive native desktop apps through the accessibility layer — list apps, snapshot a window as a numbered element tree, then click/type/scroll/drag by element index or screen coordinates. Use for a desktop app, not a web page. macOS + Windows; off unless enabled in Settings.
meetings
Context for the Meetings app's data — where a meeting's notes, diagram, transcript-derived tasks and calendar cache live, what the lifecycle states mean, how the app's agents are driven. Load when asked about a meeting's notes or action items, or when touching the meetings app data dir.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.
dashboard
Build or change a crewmate's Dashboard tab: a page worth returning to, with real work woven into its design. dashboardsave stores it; dashboardapply puts it live. Values arrive through declared fields, independently of the page.
mochi-tips
Feature tips catalog for new users. Plan skill loads this to pick a tip during the first week.
bootstrap
Onboarding conversation to generate a user profile.