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Found 12 Skills
Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.
Financial planning integration: planning tool data flows, goal-based plans, Monte Carlo simulation, plan-to-portfolio linkage, scenario analysis, tax projection, Social Security optimization.
Financial calculations: loans, investments, NPV/IRR, retirement planning, Monte Carlo simulations. Generates tables, charts, and exportable reports.
PluginEval quality methodology — dimensions, rubrics, statistical methods, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when calibrating scoring thresholds for your marketplace, or when explaining quality badges to external partners like Neon.
Solve the influence maximization problem to select optimal seed nodes for maximum information spread. Use this skill when the user needs to choose seed users for viral campaigns, maximize network reach under a budget constraint, or compare seeding strategies — even if they say 'who should we seed first', 'maximize viral reach', or 'optimal influencer selection'.
Stress-test financial plans across scenarios (bull/bear/base), sensitivity tables, and Monte Carlo-style analysis. Use when evaluating financial assumptions, modeling risk scenarios, or building scenario-based financial plans.
Domain-agnostic strategic decision analysis and wargaming. Auto-classifies scenario complexity: simple decisions get structured analysis (pre-mortem, ACH, decision trees); complex or adversarial scenarios get full multi-turn interactive wargames with AI-controlled actors, Monte Carlo outcome exploration, and structured adjudication. Generates visual dashboards and saves markdown decision journals. Use for business strategy, crisis management, competitive analysis, geopolitical scenarios, personal decisions, or any consequential choice under uncertainty. NOT for simple pros/cons lists, non-strategic decisions, or academic debate.
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
Estimate potential future losses using VaR, Expected Shortfall, Monte Carlo simulation, and stress testing. Use when the user asks about Value-at-Risk, CVaR, Expected Shortfall, scenario analysis, stress testing, or factor-based risk decomposition. Also trigger when users mention 'how much could I lose', 'worst-case scenario', 'tail risk', 'risk budget', 'component VaR', 'marginal VaR', '99% confidence loss', 'Monte Carlo simulation', or ask how to project portfolio risk forward.
Run a heavy neural-trader job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on the Anthropic Managed Agent cloud runtime instead of locally
Impermanent loss calculation, modeling, and breakeven analysis for AMM liquidity provision across pool types
Time Machine. Replays every possible outcome of a trade before you risk a dollar: 10,000 simulated sessions plus every past time the setup fired. Takes the plan from trader-exit (entry, stop, target), rebuilds the rest of the day 10,000 times from the stock's own 5-minute bars over the last 60 days, and re-runs the same rule on every past session. Prints the odds of target first, stop first, or neither by the close, where the price sits at the close, the average R, the history, and one honest verdict, then hands off to /trader-budget. Use when the user says "run the time machine", "replay this trade", "what are the odds", "simulate this trade", "monte carlo", "backtest this setup", "how often does this hit the target", "is this a coin flip", or after /trader-exit writes a plan, before /trader-budget.