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/awslabs/agentcore-samples/cost-efficiency-analyzernpx skills add awslabs/agentcore-samples --skill cost-efficiency-analyzergit 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/cost-efficiency-analyzer)<a href="https://agentmods.dev/skills/awslabs/agentcore-samples/cost-efficiency-analyzer"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/cost-efficiency-analyzer.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 | $0.00098 | $0.01185 |
| Opus 5 | $0.00049 | $0.00593 |
| Sonnet 5 | $0.00020 | $0.00237 |
| Haiku 4.5 | $0.00010 | $0.00119 |
Grade A, and why
cost-efficiency-analyzer 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 4d 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.
Cost Efficiency Analyzer
Analyzes cost structure and operational efficiency by examining COGS, operating expenses, and their ratios relative to revenue over time.
Prerequisites
No inputs required unless the user asks about a specific quarter. Default: analyze the most recent quarter (Q3 2025) with comparison to Q2 2025.
Steps
Step 1: Fetch cost data
Fetch the quarter(s) needed:
get_financial_data(period="Q3 2025")
get_financial_data(period="Q2 2025")
If the user asks for a different quarter, fetch that instead.
Step 2: Fetch benchmarks
get_kpi_benchmarks()
Extract:
gross_margin_pct: formula and benchmark (40%)opex_ratio: formula and benchmark (30%) — note: lower is better
Step 3: Compute cost metrics
Use python_exec to calculate cost efficiency metrics:
# Q3 2025 data
r3 = 4200000; cogs3 = 1890000; opex3 = 1050000; ebitda3 = 1260000
# Q2 2025 data (for comparison)
r2 = 3800000; cogs2 = 1710000; opex2 = 980000; ebitda2 = 1110000
def cost_metrics(revenue, cogs, opex, ebitda, label):
gross_profit = revenue - cogs
gross_margin = round(gross_profit / revenue * 100, 1)
cogs_pct = round(cogs / revenue * 100, 1)
opex_pct = round(opex / revenue * 100, 1)
total_cost = cogs + opex
total_cost_pct = round(total_cost / revenue * 100, 1)
ebitda_margin = round(ebitda / revenue * 100, 1)
cost_per_rev = round(total_cost / revenue, 4) # $ of cost per $ of revenue
print(f"\n{label}:")
print(f" COGS: ${cogs:,} ({cogs_pct}% of revenue)")
print(f" Operating Expenses: ${opex:,} ({opex_pct}% of revenue)")
print(f" Total Cost: ${total_cost:,} ({total_cost_pct}% of revenue)")
print(f" Gross Margin: {gross_margin}% (benchmark: 40%)")
print(f" EBITDA Margin: {ebitda_margin}% (benchmark: 15%)")
print(f" Cost per $1 revenue: ${cost_per_rev:.4f}")
return {"gross_margin": gross_margin, "opex_pct": opex_pct, "cogs_pct": cogs_pct,
"total_cost_pct": total_cost_pct}
m3 = cost_metrics(r3, cogs3, opex3, ebitda3, "Q3 2025")
m2 = cost_metrics(r2, cogs2, opex2, ebitda2, "Q2 2025")
# QoQ cost efficiency change
print(f"\nQoQ Cost Efficiency Change (Q2 → Q3):")
print(f" COGS ratio: {m2['cogs_pct']}% → {m3['cogs_pct']}% ({m3['cogs_pct']-m2['cogs_pct']:+.1f}pp)")
print(f" OpEx ratio: {m2['opex_pct']}% → {m3['opex_pct']}% ({m3['opex_pct']-m2['opex_pct']:+.1f}pp)")
print(f" Total cost%: {m2['total_cost_pct']}% → {m3['total_cost_pct']}% ({m3['total_cost_pct']-m2['total_cost_pct']:+.1f}pp)")
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.
- 4d ago First seen · 105 lines · 98 tokens per session scan A d48060aef965
cost-efficiency-analyzer is a skill published in the GitHub repository awslabs/agentcore-samples (3,326 stars, last pushed 2d ago), licensed Apache-2.0. It adds 98 tokens to every session and 1,185 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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