exposure-coach
Generate a one-page Market Posture summary with net exposure ceiling, growth-vs-value bias, participation breadth, and new-entry-allowed vs cash-priority recommendation by integrating signals from breadth, regime, and flow analysis skills.
npx skills add BaggaT236/AI-Trading-Skills --skill exposure-coach --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Exposure Coach ## Overview Exposure Coach synthesizes outputs from market-breadth-analyzer, uptrend-analyzer, macro-regime-detector, market-top-detector, ftd-detector, theme-detector, sector-analyst, and institutional-flow-tracker into a unified control-plane decision. The skill answers the solo trader's core question: "How much capital should I commit to equities right now?" before any individual stock analysis begins. ## When to Use - Before initiating any new stock positions to determine appropriate capital commitment - At the start of each trading week to calibrate portfolio exposure - When multiple market signals conflict and a unified posture is needed - After significant macro or market events to reassess exposure ceiling - When transitioning between market regimes (broadening, concentration, contraction) ## Prerequisites - Python 3.9+ - FMP API key (set `FMP_API_KEY` environment variable) for institutional-flow-tracker data - Input JSON files from upstream skills (see Workflow Step 1) - Standard library + `argparse`, `json`, `datetime` ## Workflow ### Step 1: Gather Upstream Skill Outputs Collect the most recent JSON outputs from integrated skills. Each file provides a sp
- Overview
- When to Use
- Prerequisites
- Workflow
- Step 1: Gather Upstream Skill Outputs
- Step 2: Run Exposure Scoring Engine
- Step 3: Interpret the Market Posture Summary
- Step 4: Apply Exposure Guidance
- Output Format
- JSON Report
- Markdown Report
- Resources
- Key Principles
python3 skills/exposure-coach/scripts/calculate_exposure.py \
What does the exposure-coach skill do?
Generate a one-page Market Posture summary with net exposure ceiling, growth-vs-value bias, participation breadth, and new-entry-allowed vs cash-priority recommendation by integrating signals from breadth, regime, and flow analysis skills.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill exposure-coach --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.