sample-ai-assistant-on-agentcore is a sample full-stack AI assistant that runs its frontend and backend on AWS services, with the backend deployed as Amazon Bedrock AgentCore runtimes. It demonstrates a self-hosted assistant with model selection, sandboxed code execution, research tools, content-creation panels, and web browsing. Its catalogue contains instructions and skills for working with the sample.
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 agentmods add skills/aws-samples/sample-ai-assistant-on-agentcore/create-pptnpx skills add aws-samples/sample-ai-assistant-on-agentcore --skill create-pptgit clone --depth 1 https://github.com/aws-samples/sample-ai-assistant-on-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-ai-assistant-on-agentcore/create-ppt)<a href="https://agentmods.dev/skills/aws-samples/sample-ai-assistant-on-agentcore/create-ppt"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-ai-assistant-on-agentcore/create-ppt.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.06497 |
| Opus 5 | $0.00018 | $0.03248 |
| Sonnet 5 | $0.00007 | $0.01299 |
| Haiku 4.5 | $0.00004 | $0.00650 |
Grade A, and why
create-ppt 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 5d 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.
The source is not reproduced here
Licensed MIT-0
The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
17 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.
- references/charts.md 14 KB
- references/colors.md 4.0 KB
- references/patterns.md 8.1 KB
- references/qa-workflow.md 1.2 KB
- references/spatial-rules.md 1.3 KB
- references/tables.md 9.4 KB
- references/templates.md 1.9 KB
- scripts/ppt_analyzer.py 10 KB runs code
- scripts/ppt_charts.py 13 KB runs code
- scripts/ppt_colors.py 12 KB runs code
- scripts/ppt_core.py 15 KB runs code
- scripts/ppt_fonts.py 5.5 KB runs code
- scripts/ppt_images.py 8.4 KB runs code
- scripts/ppt_layouts.py 15 KB runs code
- scripts/ppt_modifier.py 6.8 KB runs code
- scripts/ppt_qa.py 28 KB runs code
- scripts/ppt_templates.py 7.9 KB runs code
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.
- 5d ago First seen · 483 lines · 37 tokens per session scan A 495541c3ad61
create-ppt is a skill published in the GitHub repository aws-samples/sample-ai-assistant-on-agentcore (19 stars, last pushed 17d ago), licensed MIT-0. It adds 37 tokens to every session and 6,497 once invoked, about $0.0002 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.
Other skills, from other repositories
export-as-pptx-editable
Native text & shapes — editable in PowerPoint.
export-as-pptx-screenshots
Flat images — pixel-perfect but not editable.
seedance
Generate AI videos using Volcengine Seedance model. Supports text-to-video (T2V), image-to-video (I2V), and audio-synced video generation. Use this skill when the user wants to create or generate videos.
seedream
Generate AI images using Volcengine Seedream model. Supports text-to-image (T2I), image editing (I2I), multi-image fusion, and web-search-based generation. Use this skill when the user wants to create, generate, or edit images.
skin-creator
Create and apply a two-asset LobsterAI visual skin from the user's style description. Use only when the AI Skin Designer kit supplies the structured skinpack workflow marker; do not use for ordinary theme or image requests.
sn-ppt-entry
Entry point for PPT generation. Asks the user to choose a mode (fast, standard, or creative), then collects role / audience / scene / pagecount as needed. For standard mode, also asks how images should be sourced (AI generation, web search, or none), whether charts should use AI-generated infographics or ECharts, and…