「calc」の検索結果

Calculate TAM, SAM, and SOM for new market segments using top-down and bottom-up methods

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。

ローカルに保存したSlackの会話を検索し、期間を絞ったメッセージ確認や件数集計を行います。データの鮮度を調べ、必要に応じて同期やスレッド取得も扱います。

• Slackの過去の会話を探したいとき
• 直近7日間のメッセージ確認
• 保存済みメッセージの件数集計や順位確認

複数のEVMチェーンでトークン残高や価格の桁がずれる問題を防ぐため、小数桁数の実行時取得、チェーン別キャッシュ、精度を保つ計算と単位統一を扱うスキル。

• トークン残高の桁ずれを調べたいとき
• 複数EVMチェーンの数量比較
• ブリッジ後の小数桁数の確認

PDF解析や画像分析などの処理結果を、ファイル内容に基づいて保存・再利用するスキル。移動や名前変更に影響されず、内容が変われば再処理する仕組みを設計します。

• PDFの抽出結果を再利用したいとき
• 移動したファイルの結果を再利用
• 既存の処理関数にキャッシュを追加

企業買収が一株当たり利益に与える影響をExcelで試算するスキル。買収価格、資金調達、相乗効果の前提を整理し、条件別の比較表と説明用の要約を作成します。

• 買収による一株当たり利益の試算
• 現金・株式の支払い条件の比較
• 買収価格と相乗効果の条件別比較

将来のキャッシュフローを現在価値に換算し、企業価値と1株当たりの評価額を算出するExcelを作成します。前提を変えた再計算やシナリオ比較、感度分析も扱います。

• 企業評価用のExcelを作りたいとき
• 弱気・基本・強気のシナリオ比較
• 前提変更による評価額の変化確認

長い入力を扱うTransformerの学習・推論で、注意機構の計算を高速化し、GPUメモリ使用量を抑えるスキル。導入から性能測定、出力の比較まで案内します。

• 長い入力での学習を高速化したいとき
• 長文推論のメモリ使用量を抑えたいとき
• 処理速度とモデル出力の比較

鍛えたい筋肉や器具に合う運動を探し、食品の栄養成分を調べるスキル。消費カロリーや体脂肪率の推定、減量・増量に向けた栄養配分にも使えます。

• 使える器具に合う運動を探したいとき
• 食品のカロリーやタンパク質の確認
• 減量・増量に向けた栄養配分

Use when evaluating test quality on modules containing business logic, calculation utilities, or state machines to determine whether the tests provide genuine defect detection.

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。

Create, read, edit, fix, or clean spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Use whenever a build task's artifact kind is spreadsheet, or the task names a spreadsheet file and wants something done to it or produced from it, including restructuring messy tabular data into a proper workbook. Covers openpyxl generation, formulas and recalculation, editing existing workbooks, and the validation gates. Not for tasks whose deliverable is a document, report, or web page that merely contains a table.

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 creating, editing, or inspecting a spreadsheet or workbook (Excel, Google Sheets, or CSV), or when the task calls for a reusable budget, model, tracker, or structured data the user can sort, calculate, or update. Also use for questions that require inspecting an existing workbook; not for quick arithmetic or a small one-off table in chat unless requested as a spreadsheet.

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。

Port an existing Remotion (React) composition to HyperFrames HTML. Use ONLY when the user explicitly asks to port/convert/migrate/translate a Remotion source. Do NOT use: (a) authoring a new HyperFrames composition; (b) Remotion mentioned in passing; (c) Remotion code shared as reference only; (d) "same video as my Remotion one" without explicit migrate request — treat as fresh build. Doubt → `/general-video`. One-way, Remotion-only: no reverse export (HyperFrames→Remotion or any framework), no non-Remotion source (After Effects, Framer Motion, plain React/CSS) → out of scope, re-create via `/general-video`. Flags unsupported patterns (useState, useEffect, async calculateMetadata, third-party React libs, `@remotion/lambda`) and recommends runtime interop over lossy translation. Unsure whether to port vs. build fresh, or only a passing Remotion mention? → /hyperframes.

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。

This skill calculates key financial ratios and metrics from financial statement data for investment analysis

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。

Performs exact symbolic mathematics with SymPy for algebra, calculus, equation solving, symbolic linear algebra, physics, and lambdify or LaTeX code generation. Use when a task needs symbolic results, explicit assumptions, or exact arithmetic; use NumPy or SciPy for purely numerical workloads.

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。

Calculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Also handles requests that only mention an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.

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。

Tracks physical units and propagates measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibility checks using dimensionless groups (Reynolds, Peclet, Damkohler, Knudsen, Biot, Womersley), characteristic scales such as diffusion time or Debye length, and observed magnitude ranges. Trigger on "is this number physically reasonable", "sanity check these units", "what regime is this flow in", or a result that looks off by orders of magnitude.

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。

Computes finite-temperature CALPHAD equilibria, phase fractions, and phase compositions from thermodynamic TDB databases using pycalphad. Use for alloy phase stability, equilibrium temperature sweeps, tie lines, lever-rule checks, or reproducible phase-fraction calculations with explicit components and mole-fraction conditions.

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。

Build comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups. Use this skill when creating financial projections, calculating burn rate or runway, modeling fundraising scenarios, or preparing investor-ready financials for a seed or Series A raise.

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。

Calculate TAM/SAM/SOM for market opportunities using top-down, bottom-up, and value theory methodologies. Use this skill when sizing markets, estimating addressable revenue, validating market opportunity for a new venture, or building investor-ready market analysis for a startup pitch or business plan.

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。

Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.

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。

Comprehensive Power BI DAX formula optimization prompt for improving performance, readability, and maintainability of DAX calculations.

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。

Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel.

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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.

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。

Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.

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。