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 persistent-notesgit 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/persistent-notes)<a href="https://agentmods.dev/skills/awslabs/agentcore-samples/persistent-notes"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/persistent-notes/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/persistent-notes"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/persistent-notes.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 | $0.00032 | $0.00309 |
| Opus 5.5 | $0.00013 | $0.00124 |
| Sonnet 5.5 | $0.00006 | $0.00062 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
persistent-notes 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
Persistent Notes Skill
Save notes to a local /mnt/workspace/notes.json file.
Usage
python3 persistent-notes/scripts/note_manager.py "Your note content here"
What it does
- Appends note with timestamp to
notes.json - Creates file if it doesn't exist
- Each note includes content and ISO timestamp
- Returns JSON confirmation
Example
python3 persistent-notes/scripts/note_manager.py "Deploy to production on Friday"
Output:
{
"status": "success",
"message": "Note saved to notes.json",
"note": {
"content": "Deploy to production on Friday",
"timestamp": "2026-03-19T10:30:00.123456"
}
}
Notes Storage
Notes are saved to ./notes.json in the current working directory as a JSON array:
[
{
"content": "First note",
"timestamp": "2026-03-19T10:00:00.000000"
},
{
"content": "Second note",
"timestamp": "2026-03-19T10:30:00.000000"
}
]
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.
- 1mo ago First seen · 57 lines · 32 tokens per session scan A 2922e9b4b402
persistent-notes is a skill published in the GitHub repository awslabs/agentcore-samples (3,454 stars, last pushed today), licensed Apache-2.0. It adds 32 tokens to every session and 309 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.
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search-shared-sessions
Search an Agent Relay workspace's shared coding-agent sessions for prior work, decisions, fixes, or the context behind a task or pull request. Use when team history could answer the user's question or prevent repeating an investigation.
autogen-agents
Use when autoGen multi-agent conversations — AssistantAgent, UserProxyAgent, group chat, code execution. Use when working with autogen agents.
crewai-agents
Use when crewAI multi-agent orchestration — agents, tasks, crews, tools, memory, delegation. Use when working with crewai agents.
session-sync
Syncs Claude Code project data — memories, session transcripts, settings, agent definitions, remember-logs, and handoff documents — between the same project on a remote workstation and the local machine over SSH, so local work can resume exactly where a remote session left off. Use when starting on a new machine…
session-resume
Cold-start resume — load all memory and project state to continue any interrupted project.