Discover blockchain finance investment skills, compare source repositories and read the instructions for each workflow.
Apply Charlie Munger's latticework of mental models to any problem. Use when user requests decision analysis, says "help me think", "apply mental model", mentions model names (inversion, bottlenecks, second-ord...
Analyzes medical education content ideas from PDFs/DOCX files to predict viral potential (views, likes, shares, AVD) and provides actionable insights. Use when the user needs to evaluate content ideas for engag...
Plan, implement, track, and reflect on your work goals and decisions.
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, scenario planning, LBO analysis, and real estate investment modeling for investment deci...
Converts app ideas into shippable MVPs by providing structured product planning, technical guidance, and decision-making support focused on rapid validation. Use when the user shares an app idea, needs help sco...
Assess assumptions on certainty and risk to prioritize validation efforts. Use at project start or before phase transitions.
Create structured visualizations of stakeholder perspectives (says, thinks, does, feels) to build deep understanding. Use when conducting user research or validating assumptions.
Transform raw research into actionable insights that inform design decisions. Use during Define phase after completing research.
Test whether assumptions are true before making commitments. Use when assumptions have low certainty and high risk.
Analyzes completed projects to identify generalizable patterns, common workflows, and reusable knowledge worth extracting into skills. Use after completing a project to review LOG.md, skill updates, and project...
Calculate AI-readiness score (0-100%) for any project with project.faf file. Shows Podium tier (Trophy/Gold/Silver/Bronze), identifies strengths and gaps, provides improvement roadmap. Use when user asks "what'...
Improve project.faf AI-readiness score through guided enhancements. Identifies missing sections, suggests improvements, helps reach next Podium tier (Bronze→Silver→Gold→Trophy). Use when user asks "improve my s...
Use when assessing risk of bias or study quality. Covers RoB 2 for RCTs, Newcastle-Ottawa Scale for cohorts, ROBINS-I for non-randomized interventions, and QUADAS-2 for diagnostic studies. Invoke for quality as...
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...
Advanced optimization for trading: OCO, multi-objective, DRO, MIP, Bayesian opt, plus convex/Markowitz/BL references.
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...
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...
Probability, moments/tails, Bayes, and statistical learning foundations for systematic trading.
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...
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....
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,...
Time series modeling, stationarity, ARIMA/GARCH, SDEs, and regime detection for trading.
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...
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...
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.