Results for “uncertainty-coefficient”

Detect insider threats by modeling normal user and entity behavior with Elasticsearch, computing anomaly scores, and correlating low-confidence indicators into high-confidence alerts.

Decompose an unknown quantity into 3-5 estimable factors and produce a defensible order-of-magnitude answer without needing precise data. Load when the user needs to size something without data — market size, resource requirements, effort estimates, user numbers, costs — or when a decision is blocked by "we don't know the numbers". Also triggers on "ballpark this", "rough estimate", "how big is this market", "how long would this take", "how many users", or when deep-thinking diagnoses a sizing/estimation frame. The goal is not precision — it is a defensible answer that enables a decision to be made. Based on Enrico Fermi's estimation method.

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.

Build a quarterly bookings forecast with commit, best-case, and pipe-only tiers, project cohort-level NRR/GRR to surface leaky cohorts, and score per-stage funnel confidence using coefficient-of-variation analysis.

Calculates a per-ticker panic index (0-100) from six weighted signals including VIX, IV Rank, RSI-14, volume anomaly, put/call ratio, and consecutive down days, triggering Bull Put Spread entry signals at scores ≥60.

Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints.

"Feature importance" is ambiguous.

Surface and clarify unknowns before prompting, log deviations during implementation, and verify understanding after, using HTML artifacts that pair prompts with outputs.

Beta coefficient reference — CAPM, systematic risk, portfolio sensitivity, regression analysis. Use when measuring stock volatility relative to the market or constructing risk-adjusted portfolios.

Ranks weekly token unlocks by Absorption Ratio (unlock value / 7d avg volume) instead of supply percentage, with per-event cliff vs linear classification, recipient category, and a one-line market read.

"Quality" is unmanageable until it is a set of numbers with agreed definitions.

Debugging constant Markov regime features in RL observations - when HMM probabilities show uniform values instead of dynamic regime estimates

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.

Cost-benefit analysis. Produces economic NPV and financial NPV side by side, with BCR, optimism bias (with mitigation), Marginal Excess Tax Burden, real-terms rebasing, WELLBY / QALY / VPF wellbeing valuation, sensitivity, switching values, EANC for unequal-life options, validation gate, and a one-line headline verdict (socially worthwhile vs financially self-sustaining). Backed by the greenbook R package (HM Treasury Green Book primitives) when available, with graceful fallback. Supports HMT Green Book, EU Better Regulation, World Bank, ADB, and Victorian HVHR. Reads a longlist markdown file directly via --from.

Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

VaR Unit Mismatch Fix

Calculates IV Rank and IV Percentile for any ticker to determine whether implied volatility is high or low relative to its 252-day history, and provides trading signals based on IV zones.

Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

Quantifies whether a stock is suitable for long-term holding or requires tiered profit-taking using a novel 'rollercoaster rate' metric, running 15 exit strategies over ~10 years of daily history per ticker.

Econometrics skill for Regression Discontinuity Design (RDD). Activates when the user asks about: "regression discontinuity", "RDD", "RD design", "sharp RDD", "fuzzy RDD", "running variable", "forcing variable", "cutoff", "bandwidth selection", "local linear regression", "McCrary test", "density test", "RDROBUST", "continuity assumption", "donut hole RDD", "geographic RDD", "断点回归", "回归不连续", "运行变量", "截断值", "带宽选择", "精确断点", "模糊断点", "密度检验", "局部线性回归"

Systematically fuzz web applications for hidden content and input validation vulnerabilities across directories, files, parameters, and authentication bypasses.

Estimate monthly burn rate from infrastructure signals and calculate payback period against revenue projections

Derive selection thresholds from market data instead of hardcoding. Trigger when: (1) reviewing hardcoded parameters, (2) volume/price thresholds seem arbitrary, (3) selection returns too many/few candidates.

Fixes common PyTorch bugs including percentile calculations, LayerNorm for Conv1d, and buffer edge cases in reinforcement learning and neural network code.

Evaluates geometric consistency in text-to-video generation by measuring structural and motion coherence across camera trajectories, detecting deformation and occlusion artifacts in static scenes.

Customer retention analysis, churn prediction, cohort analysis, win-back campaigns, and loyalty program design. Use when the user asks about churn, retention, customer lifetime value, cohort analysis, or win-back strategies.

Evaluates machine learning classifiers on their ability to distinguish signal from background in particle physics simulations, measuring how well algorithms rank signal events above background ones using the AUC metric.

Hunt for supply chain compromise indicators including trojanized software updates, compromised dependencies, unauthorized code modifications, and tampered build artifacts.

Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations. Use when the user requests churn analysis or provides relevant inputs for this workflow.

DeFi Primitives

Checklist of empirical robustness tests for finance/economics papers

Evaluates US market bubble risk through quantitative data analysis using the Minsky/Kindleberger framework, scoring indicators like Put/Call ratio, VIX, margin debt, breadth, and IPO data.

Analyzes earnings-season implied volatility: historical IV crush, implied move forecast, IV Rank strategy tag, and a priced Iron Condor quote ready to trade.

Builds a 3D volatility surface for any optionable ticker, mapping implied volatility across strike price and time to expiration to identify cheap, expensive, or anomalous options.

Convene a four-voice council of advisors to surface structured disagreement and tradeoffs for ambiguous decisions, go/no-go calls, and multi-path choices.