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Use when evaluating Seth Klarman-style deep value and distressed opportunities: extreme margin of safety, forced sellers, liquidity discounts, complexity, downside protection, and patience.
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Use when evaluating a business through Buffett-style ownership, owner earnings, durable moat, management quality, capital allocation, and margin-of-safety judgment.
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Generator economic dispatch and cost optimization for power systems. Use when minimizing generation costs, computing optimal generator setpoints, calculating operating margins, or working with generator cost functions.
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When provisioning, setting a limit, or committing an estimate under uncertainty, size a buffer to residual error and the cost of breach—not to the optimistic edge.
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Use when the contribution of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript relative to the macro frontier is fuzzy, contested, or undersold. Frames the marginal contribution against quantitative-theory and identified-empirical literatures; it does not fix the identification or the model itself.
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Use when the marginal contribution of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript is fuzzy or undersold and must be staked precisely against prior applied-micro work. Positions the contribution; it does not fix the identification (aeja-identification) or the prose (aeja-writing-style).
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Finds the price buyers will actually pay. Reads the consumer panel's four price answers per buyer into a Van Westendorp price-sensitivity range (too cheap, a bargain, getting expensive, too expensive), sets it against what competitors really charge and against the CFO's margin, and recommends a price, a price ladder (good / better / best) and an opening offer to test. Use when the user says "how much should I charge", "price this", "is $X too expensive", "pricing strategy", "what price", or after the consumer panel.
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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.
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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Decides whether and how to polish a draft genome assembly to raise consensus accuracy (QV) with read-type-matched tools - Racon and medaka (ONT consensus), dorado polish, Polypolish and pypolca (Illumina, repeat-aware), Pilon (legacy short-read), NextPolish/NextPolish2, Hapo-G (haplotype-aware), ntEdit, and DeepPolisher/PEPPER-Margin-DeepVariant for human. Covers the do-not-polish-HiFi rule, the medaka basecaller-model footgun, held-out Merqury QV as the only honest stop signal, and the haplotype-collapse trap. Use when correcting homopolymer indels or residual SNPs in a long-read assembly, deciding if a HiFi assembly needs polishing, or choosing an ONT vs hybrid vs short-read polishing chain.
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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.
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Computes sample size and power for clinical trials including continuous, binary, and time-to-event endpoints; superiority, non-inferiority, and equivalence designs; FDA 2016 non-inferiority margin selection with M1/M2 framework; Schoenfeld 1981 and Lakatos 1988 for survival; Schuirmann TOST and 80-125% bioequivalence; minimum clinically important difference (MCID) vs δ distinction. Use when justifying trial size in protocol or SAP per CONSORT 2025 item 16a.
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Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.
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Compare and optimize shipping rates across carriers and fulfillment methods. UPS, FedEx, USPS, DHL rate comparison, zone optimization, and shipping strategy to maximize margins.
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POD integration — Printful, Printify, mockups, niche selection, margin optimization
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Product research for dropshipping businesses. Identify profitable products with reliable suppliers, healthy margins, and manageable competition. Evaluates shipping times, return risk, and marketing viability.
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Offsite Ads analysis — when to opt out, ROI tracking, fee impact on margins, attribution
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Cross-platform PPC strategy planner for ecommerce businesses. Analyzes your product and margins, recommends the right advertising platforms (Google Ads, Meta Ads, TikTok Ads), calculates ROAS targets, allocates budget across channels, and generates platform-specific campaign briefs with ad copy and creative direction. Two modes: (A) Build — design a multi-platform ad strategy from scratch, (B) Optimize — audit existing cross-platform campaigns and reallocate budget. Works for Shopify, WooCommerce, standalone stores, and marketplace sellers expanding to external traffic. No API key required.
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E-commerce growth strategy advisor. Diagnoses current business health using unit economics (CAC, LTV, AOV, contribution margin), identifies the highest-impact growth opportunities across 5 levers (traffic, conversion, AOV, retention, expansion), and builds a prioritized 90-day growth roadmap. Uses the Ansoff Matrix adapted for e-commerce to evaluate market penetration, channel expansion, product expansion, and new market entry. Includes multichannel readiness assessment (Amazon, Walmart, TikTok Shop, Etsy, DTC/Shopify/Shopify) and product line expansion analysis. No API key required. Use when: (1) planning next phase of e-commerce growth, (2) deciding whether to expand to new channels or products, (3) diagnosing why growth has stalled, (4) prioritizing what to fix or build next.
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Build an evidence-based competitive pricing strategy for ecommerce products. Use when a seller asks how to position a price against competitors, set regular and promotional prices, protect contribution margin, design bundles or price tiers, respond to competitor moves, or coordinate prices across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for automated repricing implementation or claims of a mathematically proven optimal price without sufficient data.
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Analyze financial data, calculate financial ratios, and generate analysis reports. Use when the user asks about revenue, costs, profits, margins, ROI, financial metrics, or needs financial analysis of a company or project.
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Design targeted interventions that strengthen belonging and inclusion for specific classroom dynamics. Use when students feel isolated, cliques form, or new students need integrating.
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Operational billing execution for legal matters. Monthly bill prep and billing instructions, LC invoice review and disbursement treatment, client billing query responses, cashflow modelling (LC payment obligations vs client receipts), and leverage and burn analysis (staffing mix, predicted total cost, margin trajectory). Trigger on: 'prepare the bill', 'billing instruction', 'end of month billing', 'LC invoice', 'local counsel invoice', 'pass through as disbursement', 'client querying the invoice', 'billing dispute', 'cashflow gap', 'when will we get paid', 'LC payment due', 'leverage analysis', 'staffing mix', 'predicted total cost', 'burn rate by grade', 'are we on track', 'what will this matter cost'.
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