「margin」の検索結果

"Calculate and analyze promotional ROI including incremental sales lift, margin impact, and promo type comparison. Use this skill when the user needs to evaluate whether a promotion was profitable, compare promotion types, simulate promo scenarios, or optimize promotional spending — even if they say 'was this sale worth it', 'which promo type works best', 'are we giving away too many discounts', or 'plan our next promotion'.".

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"Perform break-even analysis to determine the sales volume or revenue needed to cover all costs. Use this skill when the user needs to calculate break-even point, assess margin of safety, evaluate operating leverage, or decide pricing and volume trade-offs — even if they say 'how many units do we need to sell', 'when will we be profitable', or 'what happens if we lower the price'.".

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"Analyze and design pricing strategies including cost-plus, value-based, competitive, penetration, and skimming approaches with psychological pricing techniques. Use this skill when the user needs to set or change prices, evaluate pricing models, understand price elasticity, or apply psychological pricing — even if they say 'how much should we charge', 'are we priced right', or 'our margins are too low'.".

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"Optimize advertising budget allocation across campaigns using marginal returns analysis. Use this skill when the user needs to distribute budget across multiple campaigns, optimize spend pacing, or maximize overall ROAS under budget constraints — even if they say 'how to split my ad budget', 'campaign budget optimization', or 'diminishing returns on ad spend'.".

日本語の概要は準備中です。原文の説明を表示しています。

Set up, format, fix, and clean up LaTeX regression and estimation tables in empirical economics or finance papers. Use when (a) creating a .tex, results.tex, main.tex, or preamble to display regression or estimation output from Python (pyfixest), Stata, or R — including any mention of estout, esttab, estauto, estwide, or table fragments; (b) formatting or repairing estout-style regression tables that overflow the right margin, have cramped or boxed notes, misaligned columns, or poor significance stars, or when asked to match a reference paper, make the note fill the table width, or choose among \estwide, \estauto, and landscape layouts; (c) diagnosing "Misplaced \omit" errors from \input-ed table fragments inside tabular environments. Covers \@@input, \estwide, \estauto, \estautoLT, \specialcell, \sym, minipage notes below tabulars, and the convert-and-verify workflow. Do NOT trigger for general LaTeX documents like beamer presentations, problem sets, letters, or CVs.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill whenever the user asks to run Stata commands, estimate econometric models, work with .dta files, run a .do file, generate Stata output, or do any statistical analysis where Stata is involved. Also trigger when the user mentions Stata variables, Stata syntax, or econometric tasks where Stata is the natural tool, including regressions, IV estimation, diff-in-diff, RDD, panel data, clustering, summary statistics, and margins. Stata runs through pystata on StataNow 19.5 BE; configure once with stata_setup, then drive everything with stata.run() and exchange data directly with pandas. Prefer this skill over subprocess calls or .do-file shelling for Stata work, including cases where the user does not say pystata.

日本語の概要は準備中です。原文の説明を表示しています。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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Use when a task needs the judgment of a rotary drill operator (driller) — diagnosing a drilling break or kick warning sign, running well-control shut-in and kill-mud-weight calculations, setting trip margin and swab/surge limits before pulling out of hole, optimizing weight-on-bit and RPM against mechanical specific energy, or reviewing a rig-floor near-miss.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a task needs the judgment of a dermatologist — triaging a pigmented lesion for melanoma risk, choosing a biopsy technique and reading the pathology in clinical context, staging a skin cancer to decide margins or Mohs referral, escalating psoriasis/eczema treatment by severity score, or managing isotretinoin risk under REMS/iPLEDGE rules.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a task needs the judgment of a Cutting/Slicing Machine Operator — calculating a portion weight overweight margin from measured weight variability to minimize giveaway while staying compliant, verifying a thickness setting against a new product lot's actual density rather than reusing a prior lot's setting, diagnosing declining cut quality as a blade-condition issue rather than compensating with thickness/speed, or adapting cut pattern to irregular input to improve yield.

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Use when a task needs the judgment of a Civil Engineer — sizing stormwater detention/drainage systems, checking foundation bearing capacity against a geotechnical report, verifying IBC/ASCE 7 code compliance for site civil work, reviewing construction RFIs against stamped drawings, or evaluating value-engineering changes for safety-margin impact.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a task needs the judgment of an Automotive Engineering Technician — setting up and instrumenting test equipment (strain-gauge bridges, thermocouples, load/torque sensors) to an engineer's written test plan, selecting DAQ sample rate and an SAE J211 CFC filter class for a vehicle test channel, verifying instrument calibration (torque wrench, thermocouple, load cell, shunt-cal check on a strain bridge) against a traceable standard before a test run, reducing dyno or road-load test data (SAE J1349 power correction, rainflow-counted durability damage fraction) into engineering units, and documenting test results against the engineer's target with stated margin. Distinct from an automotive engineer (owns the system-level design decisions — structure, powertrain sizing, suspension geometry, NVH) — this role installs the sensors, configures the DAQ, executes the instrumented test, and reduces the data under a design and test plan the engineer specified.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a task needs the judgment of an Aerospace Engineering and Operations Technologist/Technician — verifying an installed fastener's preload against a drawing's torque callout via the T=K·D·F relationship, reducing strain-gauge data from a structural proof-load test into stress and checking it against an FEA-predicted margin, selecting and executing an NDI method against a defect type and material, reducing a wind-tunnel test point's dynamic-pressure and Reynolds-number data, or ballooning and dispositioning an AS9102 first article inspection. Distinct from an aerospace-engineer (owns the design, structural analysis, and certification basis) — this role builds, instruments, tests, and inspects the hardware the engineer specified, closing the loop with measurement rather than deriving a new analysis.

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Use when a task needs the judgment of an Agricultural Grader/Sorter — calling a defect grade on a moving packing line, deciding whether a lot with a marginal QC sample gets accepted, hand re-sorted, downgraded, or held, choosing where to station a human grader relative to an optical sorter, or diagnosing why a lot's cull rate or customer chargebacks spiked.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a task needs the judgment of an Aerospace Engineer — reconciling a vehicle mass/weight budget against a certification limit, computing a structural margin of safety, choosing a means of compliance for an FAA certification basis, tracing a weight-growth overrun back to a design decision, or evaluating a propulsion/aerodynamics tradeoff against a range or payload requirement.

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Circuit simulation and pre-fabrication validation. Trigger on: "simulate", "run simulation", "spice", "check stability", "ripple analysis", "phase margin", "power supply simulation", "AC analysis", "transient analysis". Includes automated SPICE simulation via ngspice, RF chain analysis (scikit-rf), power/clock analysis (PyLTSpice), and PCB EM (openEMS FDTD). Run before ordering to validate performance before spending on prototypes.

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Audit a quoting or estimation system -- analyze labor hour estimation accuracy, material take-off completeness, pricing rule engines, markup and margin optimization, good-better-best tiered pricing, and quote-to-close win rates.

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Calculate and model paid-media CPA, CPL, CPC, CPM, ROAS, MER, break-even targets, contribution margin, LTV:CAC, impression-share opportunity, budgets, forecasts, and experiment economics. Use for PPC math, ad calculator, break-even analysis, ROAS calculator, CPA calculator, budget forecast, LTV CAC, or MER.

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Plan and review paid-media budgets, bidding, pacing, marginal return, forecasts, CPA, ROAS, MER, LTV:CAC, constraints, and allocation across supported platforms. Use for ad budget allocation, media budget, bidding strategy, scaling, spend pacing, budget forecast, ROAS target, or investment tradeoffs.

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Size the agent-browser Chromium window to fill its current screen, leaving a configurable bottom margin for the desktop panel. Use before any agent-browser automation that needs the full screen visible without elements overlapping or scrolling off (e.g. Xero, dense web apps).

日本語の概要は準備中です。原文の説明を表示しています。

[user] Business Legal Counsel / CLO level contract drafting, review, and legal risk assessment. Handles domestic Chinese contracts (中文), cross-border contracts, and offshore international contracts in Chinese and English. Jurisdictions: PRC law (Civil Code, Company Law, Anti-Unfair Competition Law), US law (UCC, common law), Hong Kong law (common law, HK ordinances). Combines relevant laws, regulations, and litigation case precedents in contract analysis. Generates Word (.docx) files with track changes and persuasive margin comments for contract review markup. Use when user mentions contracts, legal review, contract drafting, legal risk assessment, NDA, agreement, 合同, 法律, 审核, 起草, 合同审查, 法务, legal negotiation, due diligence, 尽职调查, 风险评估, 条款谈判, 反不正当竞争, or any contract-related task.

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Drafts SEC/FINRA-compliant Broker-Dealer Customer Agreements covering account opening, trading, margin, fees, risk disclosures, privacy, arbitration, and execution. Use when drafting or updating customer account agreements, brokerage agreements, or customer documentation for registered broker-dealers.

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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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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.

日本語の概要は準備中です。原文の説明を表示しています。