sector-rotation

sector-rotation is a skill for Claude Code, Codex from awslabs/agentcore-samples. It costs 22 tokens per session (374 once invoked), scanned A, original, Apache-2.0.

A workflow for comparing stock-market sectors and deciding which sectors to favour or avoid based on market performance and economic conditions. Sector rotation means shifting investment emphasis between industries as conditions change.

In plain words
What is it for?
Use it to produce sector allocation views with overweight, neutral, and underweight rankings, including the two sectors to favour and the two to reduce.
Why use it?
It creates a consistent process for combining market data, sector trends, and economic news instead of relying on a single signal.

Skill for Claude CodeCodex

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 · 3,335 stars · on GitHub

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/sector-rotation
Any agent
npx skills add awslabs/agentcore-samples --skill sector-rotation
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 sector-rotation

README.md
[![agentmods](https://agentmods.dev/badge/skills/awslabs/agentcore-samples/sector-rotation.svg)](https://agentmods.dev/skills/awslabs/agentcore-samples/sector-rotation)
Your own site
<a href="https://agentmods.dev/skills/awslabs/agentcore-samples/sector-rotation"><img src="https://agentmods.dev/badge/skills/awslabs/agentcore-samples/sector-rotation.svg" alt="Measured on agentmods" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 374 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 $0.00022 $0.00374
Opus 5 $0.00011 $0.00187
Sonnet 5 $0.00004 $0.00075
Haiku 4.5 $0.00002 $0.00037

Measured 5d ago against content hash b15f984f1721, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sector-rotation 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 5d 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.

02-use-cases/01-conversational-agents/market-trends-agent/skills/sector-rotation/SKILL.md · 39 lines

What it actually says

Sector Rotation Skill

Use this skill when a user asks about sector allocation, where to rotate capital, which sectors to favor or avoid, or how to position a portfolio across sectors.

Required workflow

  1. Retrieve the overall market overview using get_market_overview.
  2. Retrieve detailed data for the following sectors using get_sector_data for each: technology, healthcare, financials, energy, consumer_staples, consumer_discretionary.
  3. Search for macro news using search_news with sector="macro" to understand the rate/GDP backdrop.
  4. Rank sectors by today's performance and YTD trend direction.
  5. Classify each sector as Overweight, Neutral, or Underweight based on:
    • Today's performance relative to the broad market
    • Outlook field from sector data
    • Macro backdrop (rate environment, growth signals)
  6. Identify the top 2 sectors to overweight and the bottom 2 to underweight.
  7. Provide a rotation rationale (2–3 sentences) explaining the macro driver.

Output Format

Sector Rotation Recommendation
  Market backdrop : {brief macro summary}
  Overweight  (1) : {sector}  —  {reason}
  Overweight  (2) : {sector}  —  {reason}
  Neutral         : {sectors list}
  Underweight (1) : {sector}  —  {reason}
  Underweight (2) : {sector}  —  {reason}
  Rotation theme  : {1–2 sentence theme}
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. 5d ago First seen · 39 lines · 22 tokens per session scan A b15f984f1721

Subscribe to this mod's changes

sector-rotation is a skill published in the GitHub repository awslabs/agentcore-samples (3,335 stars, last pushed yesterday), licensed Apache-2.0. It adds 22 tokens to every session and 374 once invoked, about $0.0001 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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