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 executive-financial-briefinggit 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/executive-financial-briefing)<a href="https://agentmods.dev/skills/awslabs/agentcore-samples/executive-financial-briefing"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/executive-financial-briefing.svg" alt="Measured on agentmods" 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 | $0.00095 | $0.01010 |
| Opus 5 | $0.00048 | $0.00505 |
| Sonnet 5 | $0.00019 | $0.00202 |
| Haiku 4.5 | $0.00010 | $0.00101 |
Grade A, and why
executive-financial-briefing 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 8d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executive Financial Briefing
Produces a one-page executive financial briefing covering the most recent quarter's performance, key KPIs, notable trends, and 3 action items.
Prerequisites
No inputs required. Uses the most recent quarter (Q3 2025) as the primary period, with Q2 2025 as the comparison.
Steps
Step 1: Gather all data in parallel
Fetch current and prior quarter P&L:
get_financial_data(period="Q3 2025")
get_financial_data(period="Q2 2025")
get_kpi_benchmarks()
Step 2: Compute the full KPI dashboard
Use python_exec to calculate all KPIs for both quarters:
# Q3 2025
r3 = 4200000; c3 = 1890000; o3 = 1050000; e3 = 1260000
# Q2 2025
r2 = 3800000; c2 = 1710000; o2 = 980000; e2 = 1110000
# KPIs
gm3 = round((r3 - c3) / r3 * 100, 1)
em3 = round(e3 / r3 * 100, 1)
op3 = round(o3 / r3 * 100, 1)
qoq3 = round((r3 - r2) / r2 * 100, 1)
gm2 = round((r2 - c2) / r2 * 100, 1)
em2 = round(e2 / r2 * 100, 1)
op2 = round(o2 / r2 * 100, 1)
def status(val, benchmark, higher_better=True):
diff = val - benchmark
if higher_better:
if diff >= 0: return "GREEN"
if diff >= -5: return "YELLOW"
return "RED"
else:
if diff <= 0: return "GREEN"
if diff <= 5: return "YELLOW"
return "RED"
print("=== Q3 2025 EXECUTIVE DASHBOARD ===")
print(f"Revenue: ${r3:,.0f} (QoQ: {qoq3:+.1f}%)")
print(f"EBITDA: ${e3:,.0f} (margin: {em3}%) [{status(em3,15)}]")
print(f"Gross Margin: {gm3}% (benchmark: 40%) [{status(gm3,40)}]")
print(f"OpEx Ratio: {op3}% (benchmark: 30%) [{status(op3,30,False)}]")
print(f"\nQoQ Changes:")
print(f" Revenue: {qoq3:+.1f}%")
print(f" Gross Margin: {gm3-gm2:+.1f}pp")
print(f" EBITDA Margin: {em3-em2:+.1f}pp")
print(f" OpEx Ratio: {op3-op2:+.1f}pp")
Step 3: Compose the briefing
Write a structured executive briefing with exactly these sections:
FINANCIAL BRIEFING — Q3 2025 Prepared by Financial Analyst Agent | [today's date]
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.
- 8d ago First seen · 105 lines · 95 tokens per session scan A 09af3ff666ac
executive-financial-briefing is a skill published in the GitHub repository awslabs/agentcore-samples (3,338 stars, last pushed 2d ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,010 once invoked, about $0.0005 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.
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