1,734 件 ・ 関連度順
概要と使いどころ
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。
Skill for creating and managing a Design System using Tailwind CSS and shadcn/ui. Use when defining design tokens, setting up theming with CSS variables, building a consistent UI component library, initializing a design system configuration, or wrapping shadcn/ui components into design system primitives.
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。
Provides comprehensive patterns for deploying Next.js applications to production. Use when configuring Docker containers, setting up GitHub Actions CI/CD pipelines, managing environment variables, implementing preview deployments, or setting up monitoring and logging for Next.js applications. Covers standalone output, multi-stage Docker builds, health checks, OpenTelemetry instrumentation, and production best practices.
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。
Work with fonts in the Shift font editor. Use for questions about the font, glyph, source, selection, or view open in the running Shift app (through its local MCP server), for reading, authoring, or compiling saved .shift documents and other font files with shift-cli, and for font concepts such as contours, components, masters, and variable fonts.
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。
When the user needs to decide what to build, cut, and defer for a first release or minimum viable version of a product or feature.
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 a consequential technical assumption with a small, falsifiable experiment before committing to an approach. Use when a plan depends on uncertain runtime, integration, or data behavior that inspection alone cannot establish. Not for preference interviews or routine implementation.
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 balance sheet (bilan) from a Well workspace. Use when the user asks for a balance sheet, bilan, assets/liabilities/equity, or financial position at a point in time.
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。
Base/variable split recommendations, accelerators, decelerators, quota retirement methods, SPIFs, cost of sales tracking.
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 run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.
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。
Audit and sync design tokens between the source of truth (tokens.json, Figma variables export) and the code that should consume them (Tailwind config, CSS custom properties, theme files). Finds drift -- hardcoded values, orphan tokens, out-of-date copies -- and fixes it in the right direction.
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 a light-only site or app to a real dark mode -- semantic token mapping, elevation-based surfaces, recalibrated shadows and imagery -- not a naive inversion. Handles Tailwind, CSS variables, and styled-components, plus the toggle, persistence, and no-flash boot.
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。
Reads a contract and generates redline suggestions with replacement language. Identifies unfavorable terms, missing protections, ambiguous language, liability exposure, IP risks, termination traps, and auto-renewal gotchas. Produces a contract-review.md with clause-by-clause analysis, risk ratings, tracked changes format, and negotiation talking points. Use when the user wants redline markup, contract markup, or suggested contract edits.
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。
Generate a complete, premium design system from a brand brief or an existing site — color tokens, type scale, spacing/radius/elevation, motion language, and component specs — exported as Tailwind config, CSS variables, and a usage doc. Built to look editorial and human-crafted, not template-generated. Use when starting a new product/site, unifying an inconsistent UI, or codifying a brand into reusable tokens.
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。
Extract color palettes from images, websites, or designs. Identifies dominant colors, generates complementary schemes, and exports in multiple formats (HEX, RGB, HSL, Tailwind, CSS variables). Use when users need color schemes from images, brand colors, or design system palettes.
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-implementation gate covering think-first, simplicity, surgical edits, and verifiable goals. Use when starting implementation to verify the approach.
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 applying a published cohort study's methodology to a different database. Extracts the design from the source paper, maps variables to the target database with a harmonization table, generates the analysis code and reports every forced deviation.
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 comparing an exposure-outcome association across countries with parallel national surveys (KNHANES, NHANES, CHNS). Harmonizes variables, runs parallel weighted analyses and builds comparison tables for 2-country (KR+US) or 3-country (KR+US+CN) designs.
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 one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.
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 designing a study that benchmarks AI systems against a human-expert panel, before data collection. Plans the arms, decoupled rubrics with anchors, planted calibration probes, reviewer panel, inter-rater reliability targets and LLM-as-judge versus human adjudication.
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 a tabular dataset (CSV, Excel, Parquet, Stata, SAS) needs a data dictionary. Profiles every variable (type, levels, range, missingness) into codebook.md and codebook.json and flags coded values of unknown meaning as [NEEDS DICTIONARY] instead of guessing.
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 Diffusion Planner for nuPlan autonomous-driving trajectory generation: prepare model-ready data, train or resume the diffusion model, configure closed-loop planning, and add differentiable collision or classifier guidance.
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。
Guides CUDA-accelerated cuRobo v2 robotics workflows for robot models, differentiable kinematics, collision-aware IK and motion planning, trajectory optimization, MPC, perception mapping, and motion retargeting.
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 CausalNex to learn causal structures, fit Bayesian networks, discretize features, generate synthetic graph data, and run inference, evaluation, and latent-variable workflows.
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, generate, write, or scaffold Terraform .tf HCL — resources, modules, providers, variables, outputs.
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。