Results for “mortality-prediction”

在启动高风险项目或重要决策前，需要预先识别潜在失败点以增强心理韧性和方案鲁棒性时

Enumerating failure modes via pre-mortem analysis. Systematically identifies failure scenarios for plans, designs, and features, scoring them with RPN/AP. Does not write code.

Use when making a forward-looking claim with a checkable outcome (reply within 24h, error rate will drop, this skill will see more use) — record to state/predictions.jsonl with a review horizon so reflection can grade you later. Closes the in-the-moment double-loop.

在启动一个重要项目或关键行动之前，用于主动识别和防范风险

Run a pre-mortem — imagine the project has already failed one year from now and work backward to find the root causes before they happen. Load when the user asks for a pre-mortem, wants to imagine failure before committing, asks "what could go wrong before we start", "assume this fails — why", "risk analysis before launch", "what kills this project", "what could go wrong", or when deep-thinking diagnoses a pre-mortem frame. Based on Gary Klein's prospective hindsight method, which surfaces failure causes more effectively than forward risk analysis. Most useful right before a major commitment.

Challenge

Compares prediction market probabilities from Polymarket with options-implied probabilities to identify mispricing signals and potential arbitrage opportunities.

Build reproducible ML experiment plans with hypotheses, metrics, and ablations. Use when: (1) planning experiments, (2) comparing variants, (3) defining acceptance thresholds. NOT for: long-running experiment execution.

Botany Based Prediction Skill

当意识到当前行为模式可能导致不理想的未来，需要一种具体方法来激发改变动力时

Emu: Generative Pretraining in Multimodality

Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

The instruction to profile before optimizing survives because intuition about

Delivers measurement-driven performance optimization for latency, throughput, memory, and tail behavior, with correctness preservation and regression guards.

Prediction markets data - Polymarket, Kalshi markets, prices, positions, and trades. Use when: the user needs market data, stock analysis, watchlists, or portfolio workflows.

Simulates profit and loss for option positions across underlying price, implied volatility, and time to expiration, generating diagrams, breakeven analysis, and probability distributions.

Design and implement Monte Carlo methods for uncertainty quantification, risk analysis, and probabilistic simulations across scientific and financial domains. Use when "monte carlo, random sampling, uncertainty quantification, risk analysis, stochastic simulation, MCMC, variance reduction, probabilistic, " mentioned.

Calculate the Maryland Aggregate Pathology Index (MAPI) on a deceased donor procurement kidney biopsy to predict graft survival and decide single vs dual vs discard allocation. Trigger when a clinician asks "what's the MAPI score", "interpret donor kidney biopsy", "should I use this marginal kidney", "MAPI cutoff for transplant", "procurement biopsy scoring", or has histology results from a deceased donor kidney and needs an objective grading.

Diagnose and validate cron expressions before they ship, catching impossible dates, OR-semantics, midnight spikes, uneven step drift, and leap-year February 29.

Analyzes the volatility smile and skew for a single options expiration, providing implied volatility curves, skew metrics, and shape classification to reveal market pricing of tail risk and directional fear.

Price-to-win lens using GSA CALC+, BLS OEWS, and incumbent USASpending award patterns for a pursuit. Use when user asks for realism checks or competitive pricing posture before proposal — draft skill, not production-verified.

Generates at most one token recommendation and one prediction-market pick per run, each with a falsifiable thesis, entry, sizing, and kill criterion. Returns NO_PICK when no candidate meets the bar.

Suggests oral contraceptives over injectable contraceptives for women with BMI ≥27 kg/m2 with comorbidities or BMI ≥30 kg/m2 seeking contraception, provided they are well‑informed and oral contraceptives are not contraindicated. Triggers include clinician questions such as “Which contraceptive method is less likely to cause weight gain in this obese patient?” or “Should I recommend oral pills instead of depot injection for this patient with BMI 29 and hypertension?”.

Recommends a shared decision‑making process that provides patients with quantitative estimates of the expected weight effect of antidepressants to inform drug choice, also considering expected treatment length. Triggers include clinician questions such as ‘How should I discuss weight‑change risks with this patient starting an antidepressant?’ or ‘What tool can I use to show expected weight impact of sertraline vs bupropion?’.

Analyzes a completed phase diff for high-confidence behavior-preserving simplifications and applies only explicitly approved proposals, reducing duplication, dead code, needless complexity, or avoidable inefficiency.