'margin' 검색 결과

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create concise tutorial websites with task-oriented chapters, reusable templates, and coherent examples with matched highlights and margin notes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan and review paid-media budgets, bidding, pacing, marginal return, targets, constraints, and allocation across supported platforms. Use for ad budget allocation, media budget, bidding strategy, scaling, spend pacing, ROAS target, or investment tradeoffs. Use ads-math for calculations and forecasts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

[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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Digital equity and trauma-informed design for marginalized populations. Activate on "accessibility", "offline-first", "trauma-informed", "reentry", "recovery population", "shared device", "unstable phone", "digital equity", "design justice", "low-literacy", "intermittent access". NOT for general UX, marketing optimization, or enterprise SaaS design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.