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 quarterly-kpi-calculatorgit 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/quarterly-kpi-calculator)<a href="https://agentmods.dev/skills/awslabs/agentcore-samples/quarterly-kpi-calculator"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/quarterly-kpi-calculator/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/quarterly-kpi-calculator"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/quarterly-kpi-calculator.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 | $0.00084 | $0.00819 |
| Opus 5.5 | $0.00034 | $0.00328 |
| Sonnet 5.5 | $0.00017 | $0.00164 |
| Haiku 4.5 | $0.00008 | $0.00082 |
Grade A, and why
quarterly-kpi-calculator 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quarterly KPI Calculator
Calculates and interprets financial KPIs. P&L data is fetched from the financial MCP server or taken from figures the user provides.
Prerequisites
At minimum: Revenue and COGS (provided by user or fetched via get_financial_data). Optional: EBITDA, Operating Expenses, prior quarter Revenue for QoQ growth.
Steps
Step 1: Retrieve benchmark thresholds
Call the get_kpi_benchmarks tool to get current KPI formulas and benchmark values:
get_kpi_benchmarks()
Store the result — you will use the formulas and benchmarks in Steps 3 and 4.
Step 2: Get P&L data
If the user provided P&L figures directly (Revenue, COGS, EBITDA, Operating Expenses), use those values.
If the user specified only a quarter (e.g. "Q3 2025") without raw figures, call get_financial_data to retrieve them:
get_financial_data(period="Q3 2025")
If QoQ Revenue Growth is requested and prior quarter data is needed, call get_financial_data for the prior quarter as well:
get_financial_data(period="Q2 2025")
Step 3: Calculate KPIs
Use python_exec to calculate the following from the P&L data (use values from Step 2):
- Gross Margin % = (Revenue - COGS) / Revenue * 100
- EBITDA Margin % = EBITDA / Revenue * 100 (if EBITDA available)
- Operating Expense Ratio = Operating Expenses / Revenue * 100 (if OpEx available)
- Revenue Growth % QoQ = (Current - Prior) / Prior * 100 (if prior available)
Round all percentages to one decimal place.
Example:
revenue = 4200000
cogs = 1890000
ebitda = 1260000
opex = 1050000
prior_rev = 3800000
gross_margin = round((revenue - cogs) / revenue * 100, 1)
ebitda_margin = round(ebitda / revenue * 100, 1)
opex_ratio = round(opex / revenue * 100, 1)
rev_growth = round((revenue - prior_rev) / prior_rev * 100, 1)
print(f"Gross Margin: {gross_margin}%")
print(f"EBITDA Margin: {ebitda_margin}%")
print(f"Operating Expense Ratio: {opex_ratio}%")
print(f"Revenue Growth QoQ: {rev_growth}%")
What ships with it
1 file 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 · 93 lines · 84 tokens per session scan A a12a940105c6
quarterly-kpi-calculator is a skill published in the GitHub repository awslabs/agentcore-samples (3,480 stars, last pushed today), licensed Apache-2.0. It adds 84 tokens to every session and 819 once invoked, about $0.0003 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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