aifolimizer: Skill for Claude Code

.claude/skills/sector-rotation/SKILL.md

sector-rotation is a skill for Claude Code from tusharagg1/aifolimizer. It costs 40 tokens per session (1,067 once invoked), scanned A, original, MIT.

A workflow for analyzing which market sectors are gaining or losing leadership, including trends, money flows, and institutional positioning. Sector rotation means investment money moving between industries over time.

In plain words
What is it for?
Use it when asking which sectors to favor, where money is flowing, or which industries are showing leadership.
Why use it?
It organizes several market signals into a view of where investment attention may be moving and checks earlier decisions for consistency.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is tusharagg1/aifolimizer's own configuration. It tells Claude Code how to work on aifolimizer itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything aifolimizer configures →

Part of the aifolimizer plugin — 28 skills, 2 agents shipped together

Reuse

Borrowing it

Nothing to install: this file belongs to tusharagg1/aifolimizer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/tusharagg1/aifolimizer/master/.claude/skills/sector-rotation/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/tusharagg1/aifolimizer

Made for: Claude Code.

Or install aifolimizer, the plugin that ships this one along with the rest of its 28 skills, 2 agents.

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/tusharagg1/aifolimizer/sector-rotation/github.svg)](https://agentmods.dev/skills/tusharagg1/aifolimizer/sector-rotation)
Your own site
<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/sector-rotation"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/sector-rotation/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 sector-rotation

Your own site · 80×15
<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/sector-rotation"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/sector-rotation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,067 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.
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 $0.00040 $0.01067
Opus 5 $0.00020 $0.00534
Sonnet 5 $0.00008 $0.00213
Haiku 4.5 $0.00004 $0.00107

Measured 10d ago against content hash 8b94a317ae96, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, 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 10d 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.

.claude/skills/sector-rotation/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Sector Rotation Detector (Renaissance + quantitative style)

Decision Memory Protocol (load first, log after)

Before forming any view, load prior decisions so verdicts stay consistent across sessions:

  • mcp__aifolimizer__get_cross_ticker_lessons (max_lessons=3) - portfolio-level win/loss patterns
  • For any name you issue a per-ticker BUY/SELL/TRIM/HOLD/ADD on, also load mcp__aifolimizer__get_ticker_decision_history (ticker=…, max_decisions=5) and mcp__aifolimizer__get_ticker_reflection (symbol=…, n=3). If a prior decision exists and this run flips it, state explicitly WHY (new data / catalyst / price); never silently contradict a logged decision.

After output, log every actionable verdict: for each BUY/SELL/TRIM/ADD/HOLD issued, call mcp__aifolimizer__log_recommendation (skill="sector-rotation", ticker, action, conviction, rationale, target_pct, stop_pct). Skipping breaks the cross-session feedback loop and causes drift.

How to run

  1. Call mcp__aifolimizer__get_profile - account types and capital. Rotation trades in TFSA tax-free; non-reg triggers capital gains
  2. Call mcp__aifolimizer__get_portfolio - current sector exposure
  3. Call mcp__aifolimizer__get_xray - true sector + geographic exposure after ETF expansion
  4. Call mcp__aifolimizer__get_market_breadth - VIX, SPY regime (bull/bear vs SMA200). Use market_regime to calibrate rotation conviction: bull_low_fear = high conviction; bear_high_fear = defensive only
  5. Call mcp__aifolimizer__get_factor_snapshot - Fama-French factor leadership. Factor rotation drives sector rotation: value leading → financials/energy/industrials; growth/momentum leading → tech/discretionary; quality (RMW) leading → defensives/staples
  6. WebSearch for: 30-day S&P 500 and TSX sector performance, relative strength rotations, ETF money flows, recent 13F filings (Berkshire, Renaissance, Bridgewater)
  7. Identify rotations and translate to actions for user's portfolio

Investor profile

  • Canadian retail investor
  • Account types and capital: always read from get_profile - never hardcode
  • Equities, ETFs, crypto exposure
  • Wants to spot institutional moves before they're obvious

Read the full file on GitHub · 62 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. 10d ago First seen · 62 lines · 40 tokens per session scan A 8b94a317ae96

Subscribe to this mod's changes

sector-rotation is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 8d ago), licensed MIT. It adds 40 tokens to every session and 1,067 once invoked, about $0.0002 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 2026-08-31.

Related

Other skills, from other repositories

quantoracle

63 deterministic quantitative finance calculators + 10 composite workflows via MCP. Options pricing, Greeks, exotic derivatives, risk metrics, portfolio optimization, Monte Carlo, statistics, crypto/DeFi, FX/macro, TVM, strategy backtesting, rebalance planning, options strategy selection, hedging. 1,000 free…

QuantOracledev/quantoracle · 92 tokens

gitee-expert

You have access to Gitee (gitee.com), China's largest GitHub-style platform. This skill teaches the effective workflows for discovery, intel, translation and webhook monitoring.

sandraschi/gitee-mcp · 0 tokens

transcribe-filing

Transcribe an insurance rate filing or internal rating manual into an OpenRater workbook, then validate, build, verify, and rate it through the OpenRater MCP tools. Use when the user shares a filing/manual (PDF or pages) and wants it executable — "build this filing", "digitize this rating manual", "make this rateable"…

vadim-filimonov/openrater · 102 tokens

tushare

A Python interface for Tushare, a financial data service that provides market and company information for stocks, funds, futures, and digital assets. It returns queried data as pandas tables.

HKUDS/Vibe-Trading · 79 tokens

social-media-intelligence

Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.

HKUDS/Vibe-Trading · 28 tokens

geopolitical-risk

Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.

HKUDS/Vibe-Trading · 34 tokens