'ising model' 검색 결과

Calculate advertising costs, revenue relative to ad spend, break-even targets, and budget forecasts. The skill makes inputs and formulas explicit, checks inconsistent data, and compares base, downside, and upside scenarios when uncertainty affects the decision.

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• Calculating advertising acquisition costs and returns
• Setting break-even targets for advertising
• Comparing budget and revenue forecasts across scenarios

Design a ten-page investor pitch deck covering the problem, solution, market, product, business progress, and fundraising request. Use a white background, a blue-to-purple gradient opening, and bar charts with prominent figures to present the business story.

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• Planning an investor fundraising presentation
• Presenting market size and business progress
• Organizing the business model and fundraising request

Turn a complete mathematical modeling competition paper into an English conference manuscript. Reorganize the research argument, track evidence, and prepare figures using an included IEEE LaTeX template, delivering source files and a PDF when compilation succeeds.

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• Turning a competition paper into a conference manuscript
• Reorganizing research into an English paper
• Tracking evidence behind figures and conclusions

Financial leadership advisor on financial planning, fundraising, investor reporting, and unit economics. Use when building a financial model, preparing for fundraising, calculating unit economics, or managing cash runway.

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短视频投流 (short-video paid advertising (China platforms)) Master OS — automated mastery of short-video paid advertising (China platforms): top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on short-video paid advertising (China platforms) problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「投流」「巨量引擎」「千川」「信息流」「创意素材」

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Run a Duck Critic producer-critic loop: you (main) keep producing the plan/code/tests and gate your own work at checkpoints with a different-model critic, revising until it passes. Use when asked for rubber duck, ラバーダック, 別モデルレビュー, second opinion, critic, or a different-model code review, design review, plan critique, or review by another model/agent harness. Not for readability refactoring or code cleanup alone.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, creative concept diversity for Meta Advantage+ (why six variants of one ad are not a diverse library, and what to brief instead), creator-made ad creative on a spend-share deal, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions — starting with whether the form fill or booked meeting fires on a confirmed success at all (inline AJAX forms, iframe embeds, Calendly event_scheduled vs a picked slot), proved by a test conversion that arrives. Also triggers on: conversions stopped, zero conversions, spend but no conversions, too many conversions, double counting, conversion tracking not working. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, u

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Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. Also triggers on: PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Monte Carlo simulation for statistical mechanics — Ising model, Metropolis-Hastings, Wolff cluster algorithm, observables (magnetization, susceptibility, specific heat), finite-size scaling, and critical phenomena analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

영업·제휴·투자 유치 연락 대상을 조사하고 우선순위를 정합니다. 공통 지인을 통한 소개 경로를 찾고 상대의 근황과 발신자의 문체에 맞춰 이메일과 소셜 미디어 연락문을 작성합니다.

• 특정 업계의 잠재 고객 찾기
• 제휴 대상과 투자자 후보 우선순위 설정
• 공통 지인을 통한 소개 경로 조사

Create a new course page by copying an existing page and replacing its content while preserving the layout and styling. Check for leftover source text, then generate narration and verify that its audio matches the new subject.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a new page using an existing layout
• Reuse a page from an imported slide deck
• Split lengthy content across matching pages

Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measuring diffusion denoising latency
• Ranking slow operations from profiler traces
• Checking whether existing optimizations address a bottleneck

Review board memos and strategy briefs with available Claude, Codex, and Gemini models. Compare concerns, supporting arguments, and votes, then turn disagreements into questions to resolve before committing to a major business decision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing fundraising terms before signing
• Checking the reasoning behind a major strategic pivot
• Examining an acquisition memo before a decision

Helps startup leaders make strategic data decisions: assess permission to train AI on customer data, choose data architecture, evaluate customer data as a business asset, and sequence hiring around the decisions the business needs to make.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing customer data for AI training
• Choosing data architecture and what to build or buy
• Evaluating data assets before fundraising or M&A

Check paper numbers, comparisons, and evaluation scope against raw experiment results. A fresh reviewer from a different model family examines only source evidence, then produces reports identifying rounding issues, inconsistent settings, aggregation errors, and unsupported claims.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify paper numbers against raw results before submission
• Recalculate claimed percentage improvements
• Check run counts and experimental settings behind comparisons

Build Laravel applications with authenticated APIs, interactive interfaces, and background jobs. The skill guides database relationships, query optimization, and feature tests, using tools such as Sanctum for authentication and Livewire for interfaces.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an authenticated Laravel API
• Optimize database queries and relationships
• Set up background jobs with Horizon

Audit how advertising platforms, GA4, and app measurement tools assign credit for conversions. Compare event definitions, reporting windows, and duplicate handling to explain discrepancies and produce an evidence-based measurement improvement plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating discrepancies between Meta and Google conversions
• Checking GA4 against advertising platform reports
• Reviewing app conversion measurement

Use this skill when producing a polished, Commonly-branded deliverable (.docx brief / memo, .xlsx data matrix, .pptx deck) and you do not have specific brand guidance from the user. Trigger on: 'write me a brief', 'one-pager', 'memo', 'data sheet', 'status matrix', 'short deck', 'summary deck', 'closing slide', 'final deliverable'. Routes to `officecli merge` with one of three pre-built starter templates that already carry the Commonly palette, fonts, and structure — so you populate content with one merge call instead of 30 individual `officecli set` commands. DO NOT use for fundraising decks (use `officecli-pitch-deck`), academic papers (use `officecli-academic-paper`), or financial models (use `officecli-financial-model`).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

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Map systemic forces shaping a wellbeing concern without individualising the problem. Uses Bronfenbrenner's ecological model and social determinants to generate structural interventions.

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Configuring Google Consent Mode v2 for privacy-compliant measurement and advertising. Covers default and update commands, consent state mapping to GA4 and Google Ads, conversion modeling with cookieless pings, and EEA requirements effective March 2024.

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Optimize paid advertising campaigns across Google Ads, Meta, TikTok, LinkedIn, and other platforms. Use when tasks involve bid optimization, audience targeting, creative testing, ROAS improvement, attribution modeling, budget allocation, campaign structure, retargeting strategies, lookalike audiences, or reducing customer acquisition cost. Covers multi-platform campaign management and creative performance analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.