skills.rest
fall-development-rob
fall-development-robCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

Corp Finance Tools - Risk & Quant

Perform quantitative risk and portfolio analytics with 128-bit decimal precision.

7|1|Updated Feb 9, 2026
Search Tags:#mcp#risk#credit-risk#factor-model#portfolio-optimization#var#black-litterman
One-click install
npx skills add https://github.com/fall-development-rob/corp_finance --skill corp-finance-tools-risk-quant
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Corp Finance Tools - Risk & Quant
Source: https://github.com/fall-development-rob/corp_finance/tree/main/.claude/skills/corp-finance-tools-risk
Command: npx skills add https://github.com/fall-development-rob/corp_finance --skill corp-finance-tools-risk-quant

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates manual, error-prone quantitative risk and portfolio calculations by providing a suite of high-precision, production-ready MCP tools that run financial math in 128-bit decimal precision and return structured, auditable outputs.

Core Features & Use Cases

  • Factor & Attribution Analysis: multi-factor regressions (CAPM, Fama-French, Carhart), factor-based attribution, and Brinson-style sector attribution for performance decomposition.
  • Portfolio Construction & Optimization: mean-variance optimisation, Black-Litterman posterior estimation, risk parity, and index weighting/rebalancing workflows with constraint support and transaction-cost aware rebalances.
  • Risk Measurement & Credit Analytics: parametric/historical VaR and CVaR, tail-risk componentisation, credit portfolio VaR via Gaussian copula, rating migration analytics, PD calibration, and economic capital calculations.
  • Market Microstructure & Execution: bid-ask spread decomposition, Kyle lambda, and Almgren–Chriss optimal execution trajectories for institutional trading decisions.
  • Real-world Example: an asset manager can run factor_model to decompose active returns, feed posterior returns to black_litterman_portfolio to produce tilt weights, and run tail_risk_analysis and stress_test to quantify CVaR and scenario losses before rebalancing.

Quick Start

Use the corp-finance-mcp tools to run a 99% CVaR tail risk analysis and factor risk decomposition for my portfolio and return component contributions, methodology, assumptions, warnings, and metadata.

Frequently Asked Questions about Corp Finance Tools - Risk & Quant

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run Black-Litterman portfolio optimization with custom market views?▼

Black-Litterman portfolio optimization integrates your custom market views with equilibrium returns to produce posterior expected returns, which are then passed to mean-variance optimization to generate tilt weights. The tool returns structured JSON containing results, methodology, assumptions, and warnings.

Can I calculate parametric VaR and CVaR for tail risk analysis programmatically?▼

Parametric VaR and CVaR tail risk analysis can be calculated programmatically alongside tail-risk componentization and stress testing. The tool returns auditable JSON outputs with component contributions, methodology, assumptions, warnings, and metadata suitable for MCP chaining.

How do I decompose portfolio active returns using Fama-French and Carhart factor models?▼

Factor model analysis decomposes portfolio active returns using multi-factor regressions including CAPM, Fama-French, and Carhart models. It supports factor-based attribution and Brinson-style sector attribution for performance decomposition, returning structured JSON with methodology and metadata fields.

Does this tool support credit portfolio VaR calculation using a Gaussian copula?▼

Credit portfolio analytics supports Gaussian copula-based VaR calculation, rating migration analytics, PD calibration, and economic capital calculations. It operates in 128-bit decimal precision and returns structured JSON outputs with assumptions, warnings, and metadata for CLI workflows.

What's the best way to generate Almgren-Chriss optimal execution trajectories for institutional trading?▼

Almgren-Chriss optimal execution trajectories are generated as part of market microstructure and execution analytics, alongside bid-ask spread decomposition and Kyle lambda calculations. The tool outputs programmatic JSON results with methodology and metadata suitable for institutional trading decisions.

Can I chain risk budgeting and stress testing results into other CLI workflows?▼

Risk budgeting and stress testing results are returned as programmatic JSON containing result, methodology, assumptions, warnings, and metadata fields explicitly designed for MCP chaining and CLI workflows. This structured output enables seamless integration into downstream quantitative research pipelines.

Related Skills

Explore More Finance & Accounting Skills→
fall-development-robfall-development-rob
7

Financial Analyst - Risk & Quant

Perform CFA-level quantitative risk analysis and portfolio optimization for institutional portfolios.

Community
Advanced
tmcgatmcga
2

portfolio-construction

Automates multi-asset portfolio construction and optimization using mean-variance, Black-Litterman, risk budgeting, and rebalancing rules.

Community
Advanced
Snowflake-LabsSnowflake-Labs
12

implementation-planning

Calculate trade execution plans with notional amounts, costs, and settlement timelines.

Official
Advanced
Muath2000Muath2000

quant-analyst

Develop quantitative trading strategies and financial models with risk analytics.

Community
Advanced