quarterly-kpi-calculator

quarterly-kpi-calculator is a skill for Claude Code, Codex from awslabs/agentcore-samples. It costs 84 tokens per session (819 once invoked), scanned A, original, Apache-2.0.

A workflow for calculating quarterly financial performance measures from profit-and-loss data. It covers measures such as gross margin, EBITDA margin, operating expense ratio, and quarter-over-quarter revenue growth.

In plain words
What is it for?
Use it to assess a company's quarterly results from revenue, cost of goods sold, EBITDA, operating expenses, and prior-quarter revenue.
Why use it?
It turns revenue and expense figures into standard business measures and compares them with current benchmarks. The figures can come from the user or a financial data service.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one.

Good fit Use it to assess a company's quarterly results from revenue, cost of goods sold, EBITDA, operating expenses, and prior-quarter revenue.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/awslabs/agentcore-samples/quarterly-kpi-calculator
About the project

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.

awslabs/agentcore-samples · 3,354 stars · on GitHub · aws.amazon.com

Install

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.

Any agent
npx skills add awslabs/agentcore-samples --skill quarterly-kpi-calculator
Clone the repo
git clone --depth 1 https://github.com/awslabs/agentcore-samples

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for quarterly-kpi-calculator

README.md
[![agentmods](https://agentmods.dev/badge/skills/awslabs/agentcore-samples/quarterly-kpi-calculator/github.svg)](https://agentmods.dev/skills/awslabs/agentcore-samples/quarterly-kpi-calculator)
Your own site
<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.

agentmods 80×15 button for quarterly-kpi-calculator

Your own site · 80×15
<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>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 819 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00084 $0.00819
Opus 5 $0.00042 $0.00409
Sonnet 5 $0.00017 $0.00164
Haiku 4.5 $0.00008 $0.00082

Measured 11d ago against content hash a12a940105c6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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 11d 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.

01-features/07-centralize-and-govern-your-ai-infrastructure/03-registry/03-advanced/strands-mcp-ecs-registry/my_skills/quarterly-kpi-calculator/SKILL.md · 93 lines

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}%")

Read the full file on GitHub · 93 lines

Files

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.

Changes

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.

  1. 11d ago First seen · 93 lines · 84 tokens per session scan A a12a940105c6

Subscribe to this mod's changes

quarterly-kpi-calculator is a skill published in the GitHub repository awslabs/agentcore-samples (3,354 stars, last pushed yesterday), licensed Apache-2.0. It adds 84 tokens to every session and 819 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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