multi-quarter-trend-analysis

multi-quarter-trend-analysis is a skill for Claude Code, Codex from awslabs/agentcore-samples. It costs 82 tokens per session (1,106 once invoked), scanned A, original, Apache-2.0.

A report of how a company’s income, costs and profits change from quarter to quarter. It compares at least three quarters and highlights whether key measures are improving, slowing or worsening.

In plain words
What is it for?
Use it to review revenue, cost and profit trends across quarters, including profit margins and revenue growth. It can also show whether changes are speeding up or slowing down.
Why use it?
It removes the need to compare separate quarterly figures by hand. It makes the direction of financial performance easier to see.

Skill for Claude CodeCodex

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

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 · 3,336 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.

agentmods
npx agentmods add skills/awslabs/agentcore-samples/multi-quarter-trend-analysis
Any agent
npx skills add awslabs/agentcore-samples --skill multi-quarter-trend-analysis
Clone the repo
git clone --depth 1 https://github.com/awslabs/agentcore-samples

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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agentmods badge for multi-quarter-trend-analysis

README.md
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<a href="https://agentmods.dev/skills/awslabs/agentcore-samples/multi-quarter-trend-analysis"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/multi-quarter-trend-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,106 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00082 $0.01106
Opus 5 $0.00041 $0.00553
Sonnet 5 $0.00016 $0.00221
Haiku 4.5 $0.00008 $0.00111

Measured 6d ago against content hash a4d19ec44ef5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

multi-quarter-trend-analysis 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 6d 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/multi-quarter-trend-analysis/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Multi-Quarter Trend Analysis

Analyzes P&L data across all available quarters to identify directional trends and flag acceleration or deceleration in key financial metrics.

Prerequisites

No inputs required from the user — all data is fetched from the MCP server. Available quarters: Q1 2025, Q2 2025, Q3 2025, Q4 2024.

Steps

Step 1: Fetch all quarterly P&L data

Call get_financial_data for each quarter:

get_financial_data(period="Q4 2024")
get_financial_data(period="Q1 2025")
get_financial_data(period="Q2 2025")
get_financial_data(period="Q3 2025")

Store all four results. You now have a time series of: revenue, cogs, operating_expenses, ebitda.

Step 2: Fetch benchmark thresholds

get_kpi_benchmarks()

Store the formulas and benchmarks for use in Step 4.

Step 3: Calculate derived metrics for each quarter

Use python_exec to compute the following for every quarter:

  • Gross Margin % = (Revenue - COGS) / Revenue * 100
  • EBITDA Margin % = EBITDA / Revenue * 100
  • Operating Expense Ratio = Operating Expenses / Revenue * 100
  • QoQ Revenue Growth % = (Current Revenue - Prior Revenue) / Prior Revenue * 100 (Q4 2024 has no prior quarter — mark as N/A)

Example:

quarters = {
    "Q4 2024": {"revenue": 4000000, "cogs": 1800000, "opex": 1000000, "ebitda": 1200000},
    "Q1 2025": {"revenue": 3500000, "cogs": 1575000, "opex": 910000,  "ebitda": 1015000},
    "Q2 2025": {"revenue": 3800000, "cogs": 1710000, "opex": 980000,  "ebitda": 1110000},
    "Q3 2025": {"revenue": 4200000, "cogs": 1890000, "opex": 1050000, "ebitda": 1260000},
}

order = ["Q4 2024", "Q1 2025", "Q2 2025", "Q3 2025"]
results = {}
for i, q in enumerate(order):
    d = quarters[q]
    gm     = round((d["revenue"] - d["cogs"]) / d["revenue"] * 100, 1)
    em     = round(d["ebitda"] / d["revenue"] * 100, 1)
    opex_r = round(d["opex"] / d["revenue"] * 100, 1)
    if i > 0:
        prev_rev = quarters[order[i-1]]["revenue"]
        qoq = round((d["revenue"] - prev_rev) / prev_rev * 100, 1)
    else:
        qoq = None
    results[q] = {"gross_margin": gm, "ebitda_margin": em, "opex_ratio": opex_r, "qoq_growth": qoq}
    print(f"{q}: GM={gm}%  EBITDA={em}%  OpEx={opex_r}%  QoQ={qoq}%")

Read the full file on GitHub · 103 lines

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. 6d ago First seen · 103 lines · 82 tokens per session scan A a4d19ec44ef5

Subscribe to this mod's changes

multi-quarter-trend-analysis is a skill published in the GitHub repository awslabs/agentcore-samples (3,336 stars, last pushed today), licensed Apache-2.0. It adds 82 tokens to every session and 1,106 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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