// Finance Guru™ Core Context Loader Auto-loads essential Finance Guru system configuration and user profile at session start. Ensures complete context availability for all financial operations.
Update Margin Dashboard with Fidelity balance data and calculate margin-living strategy metrics. Monitors margin balance, interest costs, coverage ratios, and scaling thresholds. Triggers safety alerts for large draws and provides time-based scaling recommendations. Use when updating margin, balances, coverage ratio, or margin strategy analysis.
Create institutional-grade financial documents from templates. Handles analysis reports, buy tickets, compliance memos, Excel model specs, presentations, and onboarding reports.
Run Monte Carlo simulations for Finance Guru portfolio strategy. USE WHEN user mentions monte carlo OR run simulation OR stress test portfolio OR probability analysis OR income projections OR margin safety analysis. Supports 4-layer portfolio (Growth, Income, Hedge, GOOGL) with auto-detection of current values from Fidelity CSV.
Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
Personalized coding tutorials that build on your existing knowledge and use your actual codebase for examples. Creates a persistent learning trail that compounds over time using the power of AI, spaced repetition and quizes.
This skill should be used when generating and editing images using the Gemini API (Nano Banana Pro). It applies when creating images from text prompts, editing existing images, applying style transfers, generating logos with text, creating stickers, product mockups, or any image generation/manipulation task. Supports text-to-image, image editing, multi-turn refinement, and composition from multiple reference images.
| name | fin-core |
| description | Finance Guru™ Core Context Loader Auto-loads essential Finance Guru system configuration and user profile at session start. Ensures complete context availability for all financial operations. |
Auto-loaded at every session start
System Name: Finance Guru™ v2.0.0 Architecture: BMAD-CORE™ v6.0.0 Type: Private Family Office AI System Owner: Sole client (exclusive service) Purpose: Institutional-grade multi-agent financial intelligence, quantitative analysis, strategic portfolio planning, and compliance oversight
Key Principle: This is NOT a software product - this IS Finance Guru, your personal financial command center.
These files are automatically loaded into context at session start:
Path: fin-guru/config.yaml
Contains: Module identity, agent roster (13 agents), workflow pipeline, tools, temporal awareness
Path: fin-guru/data/user-profile.yaml
Contains: Portfolio structure (${FG_PORTFOLIO_STRUCTURE}), investment capacity (${FG_W2_MONTHLY_INCOME}/month W2), risk profile (aggressive), Layer 2 Income strategy
Path: notebooks/updates/
Contains: Latest Fidelity account balances, positions, transaction history
File Patterns:
Balances_for_Account_{account_id}.csv (exact match)Portfolio_Positions_MMM-DD-YYYY.csv (e.g., Portfolio_Positions_Nov-05-2025.csv)Path: fin-guru/data/system-context.md
Contains: Private family office positioning, agent team structure, privacy commitments
All tools use 3-layer type-safe architecture (Pydantic → Calculator → CLI):
Risk Metrics (src/analysis/risk_metrics_cli.py)
VaR, CVaR, Sharpe, Sortino, Max Drawdown, Beta, Alpha
Volatility Metrics (src/utils/volatility_cli.py)
Bollinger Bands, ATR, Historical Vol, Keltner Channels, regime assessment
Momentum Indicators (src/utils/momentum_cli.py)
RSI, MACD, Stochastic, Williams %R, ROC, confluence analysis
Moving Averages (src/utils/moving_averages_cli.py)
SMA, EMA, WMA, HMA, Golden Cross/Death Cross detection
Correlation & Covariance (src/analysis/correlation_cli.py)
Pearson correlation, covariance matrices, diversification scoring
Portfolio Optimizer (src/strategies/optimizer_cli.py)
Mean-Variance, Risk Parity, Min Variance, Max Sharpe, Black-Litterman
Backtesting Framework (src/strategies/backtester_cli.py)
Strategy validation, performance metrics, deployment recommendations
Documentation: See CLAUDE.md for usage examples and agent workflows
Primary Entry: Finance Orchestrator (Cassandra Holt) Specialist Agents: Market Researcher, Quant Analyst, Strategy Advisor, Compliance Officer, Margin Specialist, Dividend Specialist, Teaching Specialist, Builder, QA Advisor, Onboarding Specialist
Workflow Pipeline: RESEARCH → QUANT → STRATEGY → ARTIFACTS
Real portfolio size, income, target, and model-probability values are read from .env (see .env.example): FG_PORTFOLIO_STRUCTURE, FG_W2_MONTHLY_INCOME, FG_ANNUAL_DIVIDEND_TARGET, FG_DIVIDEND_TARGET_MONTHS, and FG_MONTE_CARLO_PROBABILITY. Do not hardcode personal numbers in this skill.
Layer 1 (Growth): Keep 100% - DO NOT TOUCH Layer 2 (Income): Building dividend portfolio with ${FG_W2_MONTHLY_INCOME}/month W2 income Target: ${FG_ANNUAL_DIVIDEND_TARGET} annual dividend income in ${FG_DIVIDEND_TARGET_MONTHS} months (${FG_MONTE_CARLO_PROBABILITY} Monte Carlo probability) Strategy: Hybrid DRIP v2 with active rotation, confidence-based margin scaling
CRITICAL: Always execute date command before market research or analysis.
Ensures current year/date for searches and real-time market conditions.
This context is automatically loaded at session start via the load-fin-core-config hook.