us-market-bubble-detector
Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install tradermonty-claude-trading-skills-us-market-bubble-detector
Repository
Skill path: skills/weekly-trade-strategy/skills/us-market-bubble-detector
Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: Development teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: tradermonty.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install us-market-bubble-detector into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/tradermonty/claude-trading-skills before adding us-market-bubble-detector to shared team environments
- Use us-market-bubble-detector for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.