950 件 ・ 関連度順
概要と使いどころ
Deconstructs customer cohort behavior with retention curves, LTV progression, and vintage-over-vintage comparison analysis. Use when analyzing cohort data, assessing customer quality, or modeling lifetime value trajectories.
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Evaluates capital efficiency with burn multiple, net new ARR per dollar burned, and path to profitability analysis. Use when analyzing burn efficiency, assessing capital needs, or modeling runway scenarios.
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。
Evaluates bolt-on acquisitions for existing platforms with strategic fit, synergy quantification, and return contribution analysis. Use when screening add-on targets, building platform acquisition strategies, or modeling tuck-in economics.
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。
Evaluates convertible bond and mandatory convertible structures with equity sensitivity, credit floor, and Greeks analysis. Use when analyzing convertible offerings, pricing equity-linked instruments, or modeling conversion economics.
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。
Evaluates SAFE, convertible note, and KISS structures with conversion mechanics, caps, discounts, and MFN provisions. Use when analyzing convertible instruments, comparing SAFE vs note terms, or modeling conversion scenarios.
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。
Evaluates Dutch auction mechanics with optimal pricing strategy, odd-lot proration, and shareholder base analysis. Use when analyzing Dutch auctions, modeling tender strategies, or evaluating self-tender economics.
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。
Builds LP commitment pacing with deployment curves, distribution assumptions, and NAV projection for portfolio planning. Use when modeling commitment pacing, projecting LP cash flows, or planning new fund allocations.
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 for any cap-table number, mechanic, or date before a founder signs, from a single SAFE, note or warrant described in chat or a quick 'is this dilution reasonable?' check to any draft or signed SAFE, note, term sheet, option plan, AoA, Carta XLSX, or spreadsheet. A single question gets a short cited answer in about 2–3 minutes; the full report, about 5–10 minutes, runs only when the founder shares documents or asks for it. Source-cited deterministic math (YC, NVCA, Cooley GO) for SAFE/note conversion, priced-round dilution, anti-dilution, option pools, warrants, MFN chains, dual-class voting, QSBS and Israeli §102 dates, and Israeli ↔ Delaware flips. NOT for waterfall modeling, cumulative dividends, RSUs, 83(b), 409A, SPAC, warrant repricing, or pure term definitions.
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。
Security architecture and threat modeling. OWASP Top 10 2025 analysis, OWASP Agentic AI Top 10 (ASI01-ASI10), AI/LLM security patterns, supply chain security, modern API authentication (OAuth 2.1, DPoP, Passkeys/WebAuthn), vulnerability assessment, and security review for code and infrastructure.
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。
Fintech engineering expertise — payment processing (Stripe, Plaid), PCI DSS compliance, financial data modeling (double-entry bookkeeping), fraud detection patterns, bank-grade security (encryption, tokenization), open banking APIs, cryptocurrency/blockchain integration, regulatory compliance (KYC/AML), and idempotent financial transaction design. Use for payment systems, banking apps, trading platforms, and fintech infrastructure.
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。
Database design and optimization specialist. Schema design, query optimization, indexing strategies, data modeling, and migration planning for relational and NoSQL databases.
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 a mental and visual map of the target application before hunting — entry points, trust boundaries, data flows, and high-value functionality. Use when user has scope parsed and wants to plan WHERE to hunt rather than diving into recon.
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 after EZ_math_model writer creates paper.md and before quality audit when a long modeling paper needs less AI-like prose while preserving formulas, data, citations, tables, and figures.
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 EZ_math_model needs to convert paper.md to paper.docx, read a DOCX problem statement, preserve equations and tables, or package a modeling report as a Word document.
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 solving CUMCM, MCM, ICM, or other mathematical modeling tasks that need contest problem intake, model selection, Python solving, figures, paper writing, quality audit, and packaged deliverables.
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 the user explicitly asks EZ_math_model to create defense slides, academic presentation slides, Beamer slides, or PPT after the modeling paper has been packaged.
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 EZ_math_model needs to read, preview, clean, write, or summarize CSV, XLS, or XLSX attachments and result tables for modeling tasks.
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。
Database conventions and patterns. Use when working on database schemas, migrations, or data modeling.
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。
Export and optimize 3D models for FDM/SLA printing including STL/3MF export, mesh integrity verification, wall thickness checking, support generation, and slicing. Use when exporting from CAD or modeling software for 3D printing, verifying STL/3MF files are printable before slicing, troubleshooting models that fail to slice correctly, optimizing part orientation for strength or surface finish, or converting between model formats while preserving printability.
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 and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.
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。
Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
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 retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.
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 pinning down domain terminology, maintaining the domain model, or when a term conflicts or needs sharpening.
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 making an architectural decision, changing a public API, shipping a feature, or recording a codebase term. Not for domain language: use domain-modeling. Not for docs: use docs-writing.
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。