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keith-mvs AI Skills

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Browse published AI skills maintained by keith-mvs. Compare skill descriptions and follow each source repository for setup instructions.

Author statistics

Indexed skills

28

Repository stars across indexed skills

30

Repository forks

0

Average repository stars per indexed skill

1

Repository stars may repeat across skills from the same repository.

28 skills

Browse skills

GitHub

gpu-architecture-advisor

keith-mvs

Provide architecture-specific GPU optimization advice for NVIDIA GPUs (Ampere, Hopper, Ada). Use when discussing GPU architecture features, compute capability, or hardware-specific optimizations.

GitHub

cuda-optimization

keith-mvs

Optimize CUDA kernels and GPU code for performance. Use when reviewing CUDA code, analyzing performance, or suggesting GPU optimizations.

GitHub

options-strategies

keith-mvs

Expert-level options trading framework for multi-leg strategy design, quantitative modeling, and programmatic execution using Alpaca Markets APIs. Covers straddles, butterflies, spreads, iron condors, and calen...

GitHub

long-straddle

keith-mvs

Analyzes long straddle volatility plays with ATM call and put at same strike. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting large price movement in either direction...

GitHub

iron-butterfly

keith-mvs

Analyzes iron butterfly credit spreads with ATM short straddle and OTM long strangle protection. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting stock to pin at speci...

GitHub

credit-risk

keith-mvs

Assess credit risk and default probability for bonds using credit spreads, rating transitions, and recovery analysis. Requires numpy>=1.24.0, pandas>=2.0.0, scipy>=1.10.0. Use when evaluating corporate bonds, a...

GitHub

married-put-strategy

keith-mvs

Analyzes and implements married-put options strategies for portfolio protection. Calculates breakeven points, profit/loss scenarios, and compares strike prices (ITM, ATM, OTM) across multiple expiration cycles....

GitHub

covered-call

keith-mvs

Implement covered call options strategy for income generation on long stock positions. Use when seeking to enhance returns through option premium collection while maintaining stock ownership. Covers structure,...

GitHub

iron-condor

keith-mvs

Analyzes iron condor credit spreads with OTM put and call spreads for range-bound trading. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting sideways price action, want...

GitHub

bond-benchmarking

keith-mvs

Compare bond performance against market benchmarks and indices. Analyzes yield spreads, duration matching, and relative value. Requires numpy>=1.24.0, pandas>=2.0.0. Use when evaluating bonds against peers, mea...

GitHub

financial-analysis

keith-mvs

Comprehensive financial statement analysis including ratio calculation, trend analysis, and peer comparison. Evaluates liquidity, profitability, efficiency, and leverage metrics. Requires numpy>=1.24.0, pandas>...

GitHub

bond-pricing

keith-mvs

Price fixed income securities using present value, yield-to-maturity, and market conventions. Handles treasuries, corporates, municipals with various coupon frequencies. Requires numpy>=1.24.0, pandas>=2.0.0, s...

GitHub

long-call-butterfly

keith-mvs

Analyzes long call butterfly spreads with 3 strikes and 4 legs for neutral outlook. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting minimal price movement, want low-c...

GitHub

bear-put-spread

keith-mvs

Analyzes bear-put-spread debit spreads for bearish directional plays with defined risk. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting moderate price decline, compar...

GitHub

protective-collar

keith-mvs

Analyzes protective collar strategies combining long stock, long put protection, and short call income. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0. Use when protecting stock positions with reduced...

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.