2,399 件 ・ 関連度順
概要と使いどころ
Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
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。
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional 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。
Completion-evidence route used before claiming work is complete, fixed, passing, committed, or PR-ready. Requires fresh verification output before success claims. Do not use for root-cause debugging, TDD implementation, test-report packaging, or review-feedback triage.
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。
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
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。
UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design 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。
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
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。
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
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。
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.
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。
4-phase structured analysis wrapper for vibe pre-routing (compatible with claude-code-settings think-harder semantics).
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。
Windows环境下Claude Code插件Hook执行错误的诊断与修复。当遇到hook error、cannot execute binary file、.sh regex误匹配、WSL/Git Bash冲突时使用。
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。
Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.
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 tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified.
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。
Test-first development route for TDD, writing failing tests first, RED -> GREEN -> REFACTOR, and behavior-changing feature/bug/refactor work. Do not use for already-failing test root-cause debugging, test-report packaging, or final completion evidence.
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 executing implementation plans with independent tasks in the current session
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。
Prepare and request a code review after implementation or before merge by assembling scope, requirements, git range, and reviewer instructions.
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。
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication 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。
Core cheminformatics toolkit for SMILES/SDF/InChI parsing, descriptors (MW, LogP, TPSA), fingerprints, ECFP/Morgan fingerprints, substructure search, 2D/3D generation, similarity, reactions, and datamol-style molecule standardization when no separate wrapper skill is routed.
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 prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
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。
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
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Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists or non-security ownership questions.
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 publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
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。
Intelligent file write error handler: diagnoses permissions, disk space, path length, file locks before retrying. Use when you encounter 'Error writing file', 'Permission denied', 'Access denied', 'No space left', or related file write failures.
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。
Codex-compatible Ralph loop runner with dual engines (compat local state loop + optional open-ralph-wiggum backend).
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。
高级报告生成专家，支持多格式输出、数据可视化和交互式报告生成。
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。