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 revenue-growth-analystgit 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/revenue-growth-analyst)<a href="https://agentmods.dev/skills/awslabs/agentcore-samples/revenue-growth-analyst"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/revenue-growth-analyst/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/revenue-growth-analyst"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/revenue-growth-analyst.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 | $0.00072 | $0.00914 |
| Opus 5 | $0.00036 | $0.00457 |
| Sonnet 5 | $0.00014 | $0.00183 |
| Haiku 4.5 | $0.00007 | $0.00091 |
Grade A, and why
revenue-growth-analyst 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 13d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revenue Growth Analyst
Provides an in-depth analysis of revenue growth patterns across quarters, including absolute growth, growth rate trends, and growth quality assessment.
Prerequisites
No inputs required unless the user specifies a particular quarter to focus on. Default: analyze all available quarters (Q4 2024 through Q3 2025).
Steps
Step 1: Fetch all quarterly revenue data
get_financial_data(period="Q4 2024")
get_financial_data(period="Q1 2025")
get_financial_data(period="Q2 2025")
get_financial_data(period="Q3 2025")
Extract the revenue field from each response.
Step 2: Fetch growth benchmarks
get_kpi_benchmarks()
Extract revenue_growth_qoq_pct: high_growth_benchmark (20%) and
stable_growth_benchmark (5%).
Step 3: Calculate growth metrics
Use python_exec to compute:
revenues = {
"Q4 2024": 4000000,
"Q1 2025": 3500000,
"Q2 2025": 3800000,
"Q3 2025": 4200000,
}
order = ["Q4 2024", "Q1 2025", "Q2 2025", "Q3 2025"]
results = {}
for i in range(1, len(order)):
curr_q = order[i]
prev_q = order[i-1]
curr_r = revenues[curr_q]
prev_r = revenues[prev_q]
abs_growth = curr_r - prev_r
pct_growth = round(abs_growth / prev_r * 100, 1)
results[curr_q] = {
"absolute_growth": abs_growth,
"pct_growth": pct_growth,
}
label = "HIGH" if pct_growth >= 20 else ("STABLE" if pct_growth >= 5 else "LOW/NEGATIVE")
print(f"{prev_q} → {curr_q}: ${abs_growth:+,} ({pct_growth:+.1f}%) [{label}]")
# Cumulative growth from Q4 2024 baseline
baseline = revenues["Q4 2024"]
latest = revenues["Q3 2025"]
cumulative = round((latest - baseline) / baseline * 100, 1)
print(f"\nCumulative growth Q4 2024 → Q3 2025: {cumulative:+.1f}%")
# Peak and trough
peak = max(revenues, key=revenues.get)
trough = min(revenues, key=revenues.get)
print(f"Peak quarter: {peak} (${revenues[peak]:,})")
print(f"Trough quarter: {trough} (${revenues[trough]:,})")
Step 4: Assess growth quality
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.
- 13d ago First seen · 108 lines · 72 tokens per session scan A 3c1b40b7054f
revenue-growth-analyst is a skill published in the GitHub repository awslabs/agentcore-samples (3,359 stars, last pushed yesterday), licensed Apache-2.0. It adds 72 tokens to every session and 914 once invoked, about $0.0004 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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