'math' 검색 결과

Use when the user asks to "pick a bid strategy", "set a tCPA/tROAS target", or "plan the learning-phase entry"; produces a bid-strategy choice (tCPA / tROAS / max-conversions / manual CPC), the starting target math, a portfolio grouping map, and a learning-phase entry plan. Not for splitting the budget across campaigns — use budget-optimizer; not for in-flight pacing/scale moves — use budget-pacing-monitor; not for scoring the account — use ad-account-auditor. 出价策略/tCPA目标/tROAS/学习期

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Use when platform-reported conversions disagree with GA4/ecommerce, when you suspect Meta and Google are double-counting the same sales, or for a standing (monthly) reconciliation workbook that de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test. Not for the point-in-time R2 veto or RQS gate — use ad-account-auditor; not for the ROI/ROAS ratio math itself — use roi-calculator; not for organic dark-social share attribution or GA4 direct-traffic decomposition — use dark-social-attributor. 付费广告归因对账/去重/增量

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Optimize and convert Zenn Flavored Markdown (ZFM) articles to Dev.to (Forem) compatible Markdown. Use this skill whenever the user wants to post a Zenn article to Dev.to, needs to convert markdown for cross-posting, or asks to "optimize for dev.to". It handles front matter conversion, Liquid tag embeds, KaTeX math blocks, callout transformations, and general formatting adjustments required by Dev.to.

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Schedule meetings, parse meeting notes, extract action items. Three distinct intents covered. Invoke when user mentions a specific meeting (past or future) by name or context. NOT for general calendar checks (use direct calendar.py), NOT for time-zone math. Triggers: "schedule with X", "minutes of the X meeting", "action items from yesterday's meeting", "prep me for meeting with X".

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Search and retrieve preprints from arXiv via the Atom API. Use this skill when searching for papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, or economics by keywords, authors, arXiv IDs, date ranges, or categories.

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Japanese algorithmic and generative art skill covering traditional pattern generation (wagara), calligraphy simulation, zen garden generators, and mathematical patterns from Japanese art. Triggers on requests for 和柄生成, ジェネラティブアート, アルゴリズミックアート, 伝統紋様パターン, Japanese generative art.

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Set up and run reinforcement learning with verifiable rewards (RLVR/GRPO) for math, code, or custom environments using the Tinker API. Use when the user wants to do RL training, GRPO, reward-based optimization, or train with verifiable rewards.

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RSA ve crypto için kafes (lattice) tabanlı saldırılar — Coppersmith, LLL/BKZ, Boneh-Durfee, Hastad broadcast, Franklin-Reiter, stereotyped message

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Provides cryptography attack techniques for CTF challenges. Use when attacking encryption, hashing, signatures, ZKP, PRNG, or mathematical crypto problems involving RSA, AES, ECC, lattices, LWE, CVP, number theory, Coppersmith, Pollard, Wiener, padding oracle, GCM, key derivation

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Manim CE animations: 3Blue1Brown math/algo videos.

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Busabase-backed App-in-Skill control-panel/workspace for a deal analyst to model revenue-based-financing (RBF) deals for SME businesses (retail/F&B chain stores). Use when the user invokes $kelly-revshare-simulator or /kelly-revshare-simulator, wants to model a revenue-share or merchant-cash-advance deal, project cash flow and repayment, compute a Cash-Flow Payout Multiple, effective annualized merchant cost, or compare/underwrite multiple financing scenarios. Pure deterministic math, no external calls, no real trading/payment side effects. Scenario create/edit/delete and the underwriting decision are direct writes made by the analyst, not a review/approval queue.

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Pick the right y-axis unit when creating or updating a TrendsQuery insight via `posthog:insight-create` or `posthog:insight-update`. Use when the agent is about to add a `formula` purely to convert units (e.g. dividing seconds by 60 to display minutes), when a `math_property` is a duration, currency, ratio, or large count, or whenever the user mentions "format the y-axis", "duration", "seconds", "minutes", "hours", "milliseconds", "ms", "percentage", "currency", "decimals", "axis label", or "axis unit" in the context of a graph insight.

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Strategic clinical trial design feasibility assessment. Analyzes 6 dimensions (endpoint, population, comparator, effect size, duration, regulatory pathway) using precedent trials and FDA guidance. Produces enrollment projections, endpoint recommendations, and approval-pathway analysis. Use for trial-protocol design, power/sample-size estimation, comparator selection, and FDA submission strategy. Driven by precedent-based reasoning rather than first-principles math.

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Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

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Solve ordinary differential equations (initial and boundary value problems). Supports stiff/non-stiff systems, event detection, Hamiltonian/symplectic integration, parameter sweeps, and phase space analysis. Use for any ODE system in physics, engineering, or applied math.

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Blender Python API リファレンス。bpy / bmesh / mathutils / gpu / bpy_extras モジュール、 bpy.data / bpy.context / bpy.ops / bpy.types / bpy.props / bpy.msgbus / bpy.path コア API。 Mesh / Modifier / Geometry Nodes / Armature / Keyframe / Driver / Shape Key / NLA、 Cycles / EEVEE / Shader Nodes / Rigid Body / Cloth / Fluid / Compositor Nodes / AOV、 headless CLI レンダリング、add-on 開発、Extensions システム、MCP Server (Model Context Protocol)。

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最適化結果を非技術者にも伝わる改善提案書にまとめる

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最適化モデルの運用設計。自動化・監視・フォールバックを定義する

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追加データの依頼書を生成する。何がなぜ必要かを非技術者向けに説明

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受け取ったデータから最適化問題の構造を把握し、仮説を立てる。最も重要なスキル。

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ボトルネックに合わせた改善策を設計・実行・検証する

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実行可能性（feasibility）を確認する。問題の複雑度に応じて一発解きor段階的解法を選ぶ

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Infers person-to-person transmission from pathogen genomes using outbreaker2 (Campbell 2018), TransPhylo (Didelot 2017), phybreak (Klinkenberg 2017), BadTrIP (De Maio 2018), SCOTTI (De Maio 2016), BEASTLIER (Hall 2015), and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific SNP thresholds (NOT a universal cutoff -- TB <=12 SNPs / Walker 2013; MRSA <=15 / Coll 2017; C. difficile <=2 / Eyre 2013; Klebsiella <=21 / Snitkin 2012), models within-host diversity and transmission bottlenecks (Worby-Lipsitch-Hanage 2014; McCrone 2018; Sobel Leonard 2017; Lythgoe 2021 SARS-CoV-2), integrates contact-tracing data, distinguishes generation interval from serial interval (Britton & Scalia Tomba 2019; Ali 2020), attributes source via Bayesian source attribution (Mather 2013 DT104; islandR), and reconciles transmission-network reconstruction with epi metadata. Use when investigating outbreaks for who-infected-whom, defining SNP-cluster outbreak definitions, accounting for unsampled intermediates, choosing between outbreaker2 (rich epi data) and TransPhylo (genomic-only after dated phylogeny), running source attribution between host populations, calling HIV-TRACE thresholds appropriate to the local subtype, or distinguishing recent transmission from reactivation in TB / chronic HIV.

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Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.

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