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Check physical units, propagate measurement uncertainty through calculations, and format results with meaningful precision. Local tools also inspect Python code for unit and uncertainty mistakes and assess whether calculated magnitudes are physically plausible.

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• Convert units while carrying measurement uncertainty
• Build an uncertainty budget from calibration data
• Audit Python calculations for unit and uncertainty errors

Fit probabilistic models to single-cell measurements for integration across experiments, cell annotation, combined RNA and protein or ATAC analysis, and gene expression comparisons. The skill covers input requirements, training checks, reference mapping, and interpretation of model uncertainty.

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• Integrating single-cell RNA data across experimental batches
• Assigning cell labels using partial annotations or a reference
• Analyzing RNA with protein or ATAC measurements

Build Bayesian models in PyMC to estimate effects and quantify uncertainty. Fit regression or hierarchical models, check sampling diagnostics, assess predictions against observed data, predict new groups, and compare models using predictive performance.

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• Estimate regression effects with uncertainty
• Model differences across patients or groups
• Inspect sampling diagnostics and predictive checks

Create and review scientific figures with Matplotlib, Seaborn, or Plotly. The skill covers panel layouts, uncertainty, missing data, accessible color choices, and exported file checks, helping prepare figures for papers, presentations, or web publication.

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• Create multi-panel figures for a paper
• Show uncertainty and missing observations clearly
• Review palette contrast and color-independent cues

Calculate seawater carbonate chemistry from two independent measurements using PyCO2SYS. Produce pH, carbonate species, and mineral saturation results, convert laboratory conditions to ocean conditions, and propagate measurement uncertainty while recording calculation assumptions.

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• Calculate carbonate chemistry from paired seawater measurements
• Assess aragonite and calcite saturation
• Convert laboratory results to ocean temperature and pressure

Analyze campaign data across advertising platforms, check whether results are comparable, and assess costs and returns. Get budget test proposals with stated evidence, uncertainty, spending limits, and rollback criteria.

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• Compare Google Ads and Meta Ads performance
• Investigate campaigns with high acquisition costs
• Plan limited tests of budget changes

Design charts and dashboards that show data distributions, uncertainty, missing values, and sample sizes clearly. Choose visual forms that preserve meaningful variation and explain where averages, aggregation, or interval choices could mislead readers.

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• Choosing a chart to show a data distribution
• Presenting confidence intervals clearly
• Revealing variation hidden by dashboard averages

Turn interviews, support tickets, surveys, and product context into a single HTML workspace for decisions. Link findings to evidence, compare improvement options, show uncertainty, and outline experiments that help teams choose what to try next.

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• Combining interviews and support tickets into findings
• Comparing onboarding improvement options
• Sharing evidence and uncertainty with stakeholders

Forecast sales and other time series with Google’s TimesFM without task-specific training. Prepare regularly spaced CSV data, export predicted values and uncertainty ranges, add explanatory variables, and evaluate forecasts against held-out observations.

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• Forecast monthly sales or revenue from CSV data
• Export predicted values with uncertainty ranges
• Forecast with additional explanatory variables

Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.

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• Exploring distributions and relationships in tabular data
• Comparing group differences with box or violin plots
• Visualizing correlation matrices as heatmaps

Fit regression and time series models in Python, interpret coefficients, compare alternatives, and check model assumptions. Supports statistical diagnostics and forecasting, with confidence intervals and validation steps to assess uncertainty and predictive performance.

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• Fit regression models and interpret coefficients
• Compare models for binary outcomes or counts
• Forecast time series with prediction intervals

Review and improve statistical reporting in manuscript methods, results, and figure captions. It checks sample definitions, repeated measurements, tests, and uncertainty, then provides revised wording and identifies missing information or issues reviewers may raise.

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• Reviewing a manuscript’s statistical methods section
• Checking sample sizes and repeated measurements
• Aligning figure captions with reported statistics

Check that an actual app, API, or command-line tool works by exercising its real entry point with realistic inputs. Collect observable evidence such as outputs, logs, exit codes, or screenshots, and report remaining uncertainty.

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• Checking app behavior through the actual interface
• Verifying API behavior with realistic inputs
• Collecting command-line outputs and exit codes

Guide an AI partner’s approach to fast creative work and developing ideas. Drawing on Vega’s account of a long collaboration, it emphasizes honest uncertainty, adapting to the person’s pace, thoughtful disagreement, and keeping human ownership of achievements.

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• Develop ideas during a fast brainstorming session
• Adapt dialogue to a collaborator’s pace
• Build trust through honest uncertainty

Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.

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• Check whether price and return series are statistically stable
• Test long-term relationships between two assets
• Diagnose regression problems in factor research

Analyze data distributions, trends, unusual values, and differences between groups using suitable statistical methods. The skill guides interpretation of effect sizes, meaning the magnitude of differences, alongside uncertainty and business impact, while checking for misleading conclusions.

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• Summarize business metrics beyond averages
• Assess differences in A/B test results
• Investigate outliers and unusual time-series values

Turn structured documents and a research goal into four cards covering facts, explanations, risks, and suggested actions. Source references and generation details help readers trace the evidence and review uncertainty or overstated claims.

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• Separate facts and interpretations in research documents
• Review uncertainty and overstatements in financial reports
• Trace evidence behind legal document analysis

Opinionated Bayesian modeling workflow with PyMC and ArviZ. Contains critical guardrails (nutpie sampler, prior/posterior predictive checks, LOO-PIT calibration, prior sensitivity checks, 94% HDI, non-centered parameterizations, reproducible seeds) that agents won't apply unprompted — always consult before writing Bayesian model code. Trigger on: building probabilistic/Bayesian models, prior elicitation, MCMC inference, convergence diagnostics (divergences, R-hat, ESS), model comparison (LOO-CV, ELPD, stacking weights), hierarchical/multilevel models, count regressions, logistic regression with uncertainty, prior sensitivity analysis, reporting Bayesian results, or mentions of PyMC, ArviZ, InferenceData, credible intervals, posterior distributions, shrinkage, uncertainty quantification. Also trigger for model comparison, diagnosing sampling problems, choosing priors, or presenting stats to non-technical audiences.

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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.

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Use when a task needs the judgment of a Calibration Technologist/Technician — computing a Test Uncertainty Ratio (TUR) and deciding whether it meets the 4:1 target, building a measurement uncertainty budget (Type A/Type B, GUM-style) for a calibration, tracing a reference standard's chain of custody to NIST/SI, setting or adjusting a calibration interval from in-tolerance/out-of-tolerance history, or writing the accept/reject call and guard-banded pass/fail on a calibration certificate.

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Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

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Detects when Claude is uncertain but presenting answers confidently. Teaches Claude to recognize and communicate uncertainty clearly instead of masking it with confident language.

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Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.

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Knowledge base from the GAO Cost Estimating and Assessment Guide (GAO-20-195G). Use for program cost estimating: the four characteristics of a reliable estimate (comprehensive, well documented, accurate, credible) and the 18 best practices; the 12-step cost estimating process (purpose, plan, technical baseline, WBS, ground rules and assumptions, data, point estimate, sensitivity, risk/uncertainty, document, present, update); estimating methods (analogy, parametric, engineering build-up, learning curves); Monte Carlo risk/uncertainty analysis, confidence levels and contingency; auditing and validating an estimate against the characteristics; earned value management (EIA-748, BCWS/BCWP/ACWP, CPI/SPI/TCPI, EAC, PMB, IBR); and specialized techniques (software cost estimating, learning curves, Analysis of Alternatives, WBS templates, the Green Book internal-control framework). This is the cost-estimating companion to the GAO Schedule and Technology Readiness Assessment guides. Does not cover schedule estimating/risk in depth (see the GAO Schedule Assessment Guide), agency-specific cost models, or the full text of referenced standards (EIA-748, MIL-STD-881D, the Green Book) — these are named, not reproduced.

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