3,869 件 ・ 関連度順
概要と使いどころ
Use when evaluating US stocks through Serenity / @aleabitoreddit's AI and semiconductor supply-chain lens: upstream chokepoints, photonics/CPO bottlenecks, hyperscaler capex, institutional rotation, dilution risk, and thesis validation.
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 evaluating stocks through Feng Liu's weak-hand (弱者体系) reverse investing lens: assume you have less information, seek asymmetric risk/reward, buy at points of maximum pessimism, and manage position by probability.
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 evaluating Carl Icahn-style activist investments: undervalued assets, poor governance, capital allocation fixes, buybacks, asset sales, board pressure, and control catalysts.
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 evaluating Seth Klarman-style deep value and distressed opportunities: extreme margin of safety, forced sellers, liquidity discounts, complexity, downside protection, and patience.
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 evaluating Walter Schloss-style cigar-butt value: cheap stocks, balance sheets, diversification across many bargains, and avoiding glamorous stories.
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 evaluating crypto networks through a Placeholder-style lens: open networks, token investment frameworks, network participation, and long-duration crypto network ownership.
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 evaluating crypto investments through a Multicoin-style thesis lens: high-conviction sector bets, performance chains, token value capture, and crypto-native market structure.
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 evaluating a decision through Charlie Munger-style mental models: inversion, incentives, quality filters, multidisciplinary reasoning, and concentrated judgment.
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 evaluating Nick Sleep-style long-term compounders: scale economies shared, customer obsession, low turnover, long runway, owner culture, and value creation through lower prices.
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 evaluating Paul Tudor Jones-style macro trend trades: major turning points, trend confirmation, risk control, stop discipline, liquidity, and not losing big.
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 evaluating Ed Seykota-style systematic trend following: mechanical rules, long-term trend capture, small losses, position sizing, whipsaw tolerance, and emotional discipline.
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 evaluating Li Lu-style value investments: circle of competence, long-term compounding, deep research, conservative downside, China/global quality businesses, and concentrated ownership.
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 this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.
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 this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Explore an existing PostgreSQL database to understand its objects and relationships - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates - Plan or execute safe schema migrations with zero downtime **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration
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 this skill when the user wants to build a financial model — 3-statement model, DCF valuation, LBO, SaaS unit economics, sensitivity / scenario analysis, debt schedule, or fundraising projections — in Excel. Trigger on: 'financial model', '3-statement model', 'P&L + BS + CF', 'DCF', 'WACC', 'NPV', 'terminal value', 'LBO', 'debt schedule', 'cash sweep', 'MOIC', 'IRR / XIRR', 'sensitivity table', 'scenario analysis', 'ARR model', 'unit economics', 'CAC / LTV', 'cap table forecast'. Output is a single formula-driven .xlsx. This skill is a scene layer on top of officecli-xlsx — it inherits every xlsx v2 rule (4-color code, visual floor, number formats, cache-drift, Known Issues, Delivery Gate minimum cycle). DO NOT invoke for a simple budget tracker, CSV dump, or operational KPI sheet — route those to officecli-xlsx base.
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。
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.
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。
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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。
Convert PDF documents to Markdown using marker_single. Use when Claude needs to extract text content from PDFs while preserving LaTeX formulas, equations, and document structure. Ideal for academic papers and technical documents containing mathematical notation.
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。
Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.
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 this skill when simulating quadrotor physical dynamics — mapping desired thrust/moments to individual motor RPMs via a propeller allocation matrix, applying first-order motor lag, and integrating the nonlinear equations of motion (translational and rotational) using RK45.
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。
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
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。
Multi-hop evidence search + structured extraction over enterprise artifact datasets (docs/chats/meetings/PRs/URLs). Strong disambiguation to prevent cross-product leakage; returns JSON-ready entities plus evidence pointers.
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 this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
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 this skill when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluating simulation results.
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。