'margin' 검색 결과

Interpret statistical models in R and Python using predictions, comparisons, and marginal effects, which describe how outcomes change with predictors. Get guidance on choosing quantities, evaluating scenarios, summarizing groups, measuring uncertainty, and testing hypotheses.

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• Calculate predicted probabilities for chosen conditions
• Estimate how outcomes change with a predictor
• Compare treatment effects across groups

Tracks margin requirements with initial/variation margin, portfolio margin optimization, and collateral eligibility analysis. Use when managing margin, optimizing collateral, or analyzing portfolio margin impact.

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Review a deal before signing by scoring its margin and risks, flagging problematic contract terms, and recommending named human approvers. It prepares a consistent review packet for discount requests and policy exceptions without automatically approving them.

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• Reviewing discounts above a sales representative's authority
• Flagging risky contract changes before legal review
• Preparing a margin breakdown for CFO review

Plan pricing for AI products by comparing charges based on usage, tasks, or results. Use customer value and computing costs to shape pricing tiers, usage limits, hybrid plans, and margin improvement strategies.

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• Choosing how to charge for an AI product
• Designing pricing tiers and usage limits
• Reviewing low margins

Manage Hyperliquid perp positions and margin from the desk computer - read positions and margin, set leverage and cross/isolated mode, add isolated margin, understand margin tiers and liquidation price, close a position with a reduce-only IOC, and clean up orphaned orders. Write actions are Execution Trader only, on an approved ticket. Use for leverage changes, closes, protection checks and margin questions.

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Amazon profit margin calculator for sellers. Calculate cost breakdowns, profit margins, break-even points, and get pricing recommendations. Supports single product analysis and batch calculations. Input product cost, shipping, FBA fees, and get instant profitability analysis. No API key required. Use when: (1) evaluating new product profitability before sourcing, (2) diagnosing existing product margins, (3) adjusting pricing strategy, (4) analyzing multiple SKUs at once.

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CVP（Cost-Volume-Profit）分析・損益分岐点分析スキル。固定費・変動費の構造分析、 限界利益率の算出、損益分岐点売上高/数量の計算、安全余裕率の評価、 目標利益達成に必要な売上高のシミュレーションを行う。多品目分析にも対応。 Use when: 損益分岐点を知りたいとき、新規事業や価格変更の採算シミュレーション、 固定費削減・変動費率改善の効果試算、What-if分析に使用。 Triggers: "損益分岐点", "CVP", "break-even", "限界利益", "contribution margin", "安全余裕率", "margin of safety", "固変分解", "変動費率"

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Create an HTML presentation diagnosing margin recovery for a regional grocery chain. Organize the central recommendation, contributing factors, priorities, and roadmap into a decision deck, using a coordinated sage, cream, and burnt-orange visual style.

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• Presenting a grocery chain's margin-recovery diagnosis
• Explaining initiative priorities to a steering committee
• Creating an advisory deck with a warm visual style

Assess an industry's five competitive forces using documented evidence, including AI substitutes and supplier dependencies. Identify where profits concentrate and what pressures margins, then connect those findings to a market entry or strategy decision.

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• Evaluating whether to enter a market
• Investigating structural causes of shrinking margins
• Assessing the threat of AI substitutes

Builds estimates, quotes, and bids for trades and field-service businesses (HVAC, plumbing, electrical, roofing, landscaping, painting, cleaning, remodeling, general contracting, IT/AV installs). Turns a scope into line items with waste factors, burdened labor, materials, equipment, subs, permits, mobilization, contingency, overhead, profit, and tax, then writes a client-ready xlsx (live formulas) and PDF plus an internal cost sheet, with good/better/best options, add-ons, a payment schedule, exclusions, and assumptions. Use whenever someone asks for a quote, estimate, bid, proposal price, or job price, asks "how much should I charge for this job", wants to check a price or margin on a job, or is confused about markup vs margin. Computes deterministically with a script instead of mental math, because models routinely treat a 30% markup as a 30% margin and drop labor burden, waste, and mobilization.

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AI Pricing Skill workflow skill. Use this skill when the user needs When the user wants to price an AI product, choose a charge metric, design pricing tiers, or optimize margins. Also use when the user mentions 'AI pricing,' 'usage-based pricing,' 'consumption pricing,' 'outcome pricing,' 'BYOK,' 'bring your own key,' 'per-seat pricing,' 'pricing tiers,' 'AI margins,' 'cost per token,' or 'pricing model.' This skill covers pricing strategy, packaging, and margin management for AI-native products. Do NOT use for technical implementation, code review, or software architecture and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Audit and improve unit economics — LTV, CAC, payback period, gross margin, contribution margin. Use when asked to "are our unit economics good", "what is our LTV/CAC", or "how do we improve gross margin".

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Content-addressed baseline-archive epoch partitioning. Partition baseline_archive.jsonl + the self-improving hub's baseline serving by a HASH of the full baseline-production+measurement spec — when the spec (margin_rule, fitness/margin logic version, the 4 roles' model+source, rubric/dim-set, bench, seed-pool identity) changes, the hash changes → a new epoch series (like seed-gen gen-*). Triggered by "baseline epoch", "baseline 아카이빙", "epoch partition", "spec hash", "content-addressed", "margin_rule namespace", "production logic 구분", "baseline 하위 서빙" keywords.

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Review CSS spacing, borders, and positioning for conversion to logical properties, which follow writing direction. Check rendered layouts across screen sizes and interaction states to support right-to-left languages and vertical text.

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• Adapting layouts for Arabic or Hebrew
• Replacing fixed left and right spacing in CSS
• Reviewing layouts for vertical writing

Assess market sentiment using fear and greed measures, options activity, margin trading, foreign investment flows, and social media discussion. Combine indicators on a common scale to produce a dashboard and explain signs of excessive optimism or fear.

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• Create a market sentiment dashboard
• Compare Put-Call Ratio with VIX
• Review Chinese A-share margin activity and foreign investment flows

Coordinate month-end reconciliation, owner sign-off, and a 30/60/90-day cash forecast based on closed books. Combine the results into a reporting packet with financial explanations and open items, with approval required before external delivery.

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• Reconcile monthly books against payment data
• Explain changes in revenue and margin
• Refresh cash forecasts using closed-month figures

Design company discount rules across contract value, duration, payment terms, and strategic value. Build a discount matrix, define approval routes and required commitments for exceptions, and check the policy for gaps, contradictions, and margin violations.

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• Create a company's first formal discount policy
• Revise discount rules after margin requirements change
• Define approval routes and commitments for exceptions

Evaluate prospective business partners using evidence of their customer relationships and sales contributions. Recommend a partnership tier, build a 90-day joint sales plan, and compare revenue sharing with direct-sale margins, including conditions for ending the partnership.

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• Evaluating a prospective reseller or OEM partner
• Designing partnership tiers and qualification criteria
• Planning the first 90 days of joint sales

Create multi-page PDFs with headings, tables, and consistent styling using reportlab, a Python library for building PDF documents. Run code directly through run_shell to control margins and page breaks when shell_agent document creation is unreliable.

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• Creating multi-page reports with tables
• Adjusting document margins and page breaks
• Generating PDFs when shell_agent creation fails

Build a reproducible Stata analysis from data cleaning through modeling, diagnostic checks, and robustness tests. Generate runnable .do files, regression tables, and figures for empirical papers, with additional workflows for epidemiology and machine learning approaches to causal inference.

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• Building a reproducible Stata analysis pipeline
• Comparing regression specifications in a paper table
• Checking causal estimates with robustness tests

Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF via `bartCause` / `bcf`, matrix completion via `MCPanel`, CATE distribution + policy tree via `policytree`, off-policy evaluation, conformal causal via `conformalInference` / `cfcausal`, fairness audit via `fairmodels`, DAG learning via `pcalg` / `bnlearn` / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".

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Use when the user asks to "set up conversion values so tROAS optimizes profit not orders", "map margin onto my purchase value", "build value rules for lead / phone / signup conversions", or "stop bidding to revenue when I care about profit"; defines and QAs the conversion VALUE model — per-conversion values, margin/net-value adjustment, static-vs-dynamic value rules, proxy values for non-revenue actions, and a value-vs-count sanity check — as a value-model spec plus a pre-launch value QA sheet. Not for whether the tag fires or UTMs are clean — use conversion-signal-qa; not for cross-platform double-count de-dup — use attribution-reconciler; not for scoring R1/R2 — that is a scored veto in ad-account-auditor. 付费广告转化价值建模/利润出价/价值规则QA

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Establishes what something actually costs — fixed, variable and mixed cost behavior, absorption versus variable costing, job-order, process and activity-based methods, breakeven and operating leverage, and price/volume/mix decomposition. Use this to set a price floor, explain why revenue or margin moved, decide make versus buy, judge whether a product line earns its place, or work out how an allocation basis is distorting a reported margin.

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Extract locational marginal prices (LMPs) from DC-OPF solutions using dual values. Use when computing nodal electricity prices, reserve clearing prices, or performing price impact analysis.

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