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Automatically generate regression tests for Java codebases by analyzing changes between old and new code versions. Use when users need to: (1) Generate tests after refactoring or code changes, (2) Ensure previously tested behavior still works in new versions, (3) Cover modified or newly added code paths, (4) Migrate existing tests to work with updated APIs or signatures, (5) Maintain test coverage during code evolution. Supports JUnit and TestNG frameworks with unit tests, parameterized tests, and exception testing patterns.

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 formal specifications (definitions, predicates, invariants, pre/post-conditions) in Isabelle/HOL or Coq from informal requirements, source code, pseudocode, or mathematical descriptions. Use when users need to: (1) Formalize algorithms or data structures, (2) Create function specifications with contracts, (3) Generate predicates and properties for verification, (4) Translate informal requirements into formal logic, (5) Specify invariants for loops or data structures, or (6) Create formal definitions for mathematical concepts. Supports both Isabelle/HOL and Coq equally.

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。

Automatically generate clear, actionable issue reports from failing tests and repository analysis. Analyze test failures to understand expected vs. actual behavior, identify affected code components, and produce well-structured Markdown reports suitable for GitHub Issues or similar trackers. Use when a test fails, when debugging issues, or when the user asks to create an issue report, generate a bug report, or document a test failure.

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 configuration files for applications, services, and infrastructure. Use when: (1) Setting up new projects (package.json, requirements.txt, tsconfig.json), (2) Creating Docker or Kubernetes configurations, (3) Configuring CI/CD pipelines (GitHub Actions, GitLab CI, CircleCI), (4) Setting up web servers (Nginx, Apache), (5) Defining infrastructure as code (Terraform, CloudFormation), (6) Generating linter/formatter configs (ESLint, Prettier, Black). Provides templates and custom-generated configs for diverse tech stacks.

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。

Automatically generates executable tests that reproduce reported bugs from issue reports and code repositories. Use when users need to: (1) Create a test that reproduces a bug described in an issue report, (2) Generate failing tests from bug descriptions, stack traces, or error messages, (3) Validate bug reports by creating reproducible test cases, (4) Convert issue reports into executable regression tests. Takes a repository and issue report as input and produces test code that reliably triggers the reported bug.

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。

Automatically repair buggy code and generate comprehensive tests for Python, Java, and C++ programs. Use when users need to fix logic errors or runtime errors in functions, modules, or repositories. Accepts specifications via natural language descriptions, existing test cases, or input/output examples. Generates corrected code, creates or updates tests to verify correctness and prevent regressions, and produces a detailed report explaining the bug, fix, and testing strategy. Triggers on requests like "fix this bug", "repair this code", "debug this function", or "this code is broken".

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 Dockerfiles, Docker Compose configurations, and Kubernetes manifests for containerizing applications. Use when: (1) Creating Dockerfiles for Node.js, Python, Java, Go, or other applications, (2) Setting up multi-service environments with Docker Compose, (3) Generating Kubernetes deployments, services, and ingress configurations, (4) Optimizing container images for production, (5) Implementing containerization best practices. Provides both ready-to-use templates and custom-generated configurations based on project requirements.

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 abstract Control Flow Graph (CFG) representations of programs showing loops, branches, and function calls for static analysis or verification. Use when users need to: (1) Visualize program control flow structure, (2) Generate CFGs for static analysis tools, (3) Create control flow abstractions for formal verification, (4) Analyze program paths and reachability, (5) Document program structure. Supports both function-level (intraprocedural) and program-level (interprocedural) analysis with multiple output formats (textual, DOT/Graphviz, JSON).

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 code fixes and patches from bug reports, failing test cases, error messages, and stack traces. Use this skill when debugging code, fixing test failures, addressing GitHub issues, resolving runtime errors, or patching security vulnerabilities. Analyzes the bug context, identifies root causes, and generates precise code patches with explanations and validation steps.

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 automated game tests for gameplay systems. Use when the user says "automate tests" or "generate tests"

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 optimized project-context.md for AI agent consistency. Use when the user says "project context" or "generate context"

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 sprint status tracking from epics. Use when the user says "run sprint planning" or "generate sprint 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。

This skill generates, creates, or builds brand voice guidelines from source materials. It should be used when the user asks to "generate brand guidelines", "create a style guide", "extract brand voice", "create guidelines from calls", "consolidate brand materials", "analyze my sales calls for brand voice", "build a brand playbook from documents", "synthesize a voice and tone guide", or uploads brand documents, transcripts, or meeting recordings for brand analysis. Also triggers when the user has a discovery report and wants to convert it into actionable guidelines.

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。

Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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 user asks to generate JeecgBoot CRUD code, create a new module, add/modify fields on existing module, or says "代码生成", "生成代码", "创建模块", "新增功能", "建表", "加字段", "加一个字段", "增加字段", "新增字段", "修改字段", "删除字段", "generate code", "new entity", "add field"

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。

JeecgBoot UniApp3 移动端 CRUD 代码生成器。Use when user asks to generate UniApp3/mobile/APP CRUD pages, create mobile list/form pages, or says '生成移动端代码', '生成APP页面', 'uniapp代码生成', '移动端CRUD', '生成uniapp页面', '手机端页面', '移动端模块', 'generate uniapp code', 'mobile CRUD', 'uniapp page', 'APP端代码', '生成小程序页面', '生成H5页面'. Also triggers when user mentions generating mobile pages for an existing backend module, or wants to add mobile support to JeecgBoot modules. Even if user just says '给XX模块加个移动端页面' or '做个手机端的XX功能', this skill should trigger.

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 user asks to generate JeecgBoot CRUD code, create a new module, add/modify fields on existing module, or says "代码生成", "生成代码", "创建模块", "新增功能", "建表", "加字段", "加一个字段", "增加字段", "新增字段", "修改字段", "删除字段", "generate code", "new entity", "add field"

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 user asks to create/generate/edit/modify a BPM workflow, design a Flowable BPMN process, or says "创建流程", "生成流程", "新建流程", "设计流程", "画流程", "审批流程", "工作流", "BPM", "BPMN", "create flow", "create process", "new workflow", "generate workflow". Also triggers when user describes an approval chain like "先经理审批再HR审批" or mentions process nodes like "开始→审批→网关→结束". Also triggers for OA application creation: "创建OA应用", "创建审批单", "创建报销单", "创建请假单", "做一个OA表单带流程", "一键创建表单和流程", "create OA app", "create approval form with workflow". Also triggers for ANY operation on existing processes: "编辑流程", "修改流程", "删除监听器", "添加监听器", "删除节点", "添加节点", "修改节点", "配置节点", "流程中的", "edit process", "modify process", "delete listener", "add listener", "remove listener", "add node", "delete node", "configure node". Key rule: whenever user mentions a specific process name (like "网关测试") with any modification intent, this skill MUST be invoked FIRST before any manual API exploration.

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 speech audio from text using Qwen3 TTS, or clone a voice from reference audio. Triggered when the user wants to convert text to speech, generate audio, read text aloud, or clone/mimic a voice. Supports multiple speakers, English and Chinese, and emotion/style control.

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, modify, and style React components from natural language using the Thesys SDK. Guides schema-driven UI generation, theme customisation, tool calling integration, and deployment to Vite, Next.js, or Cloudflare Workers. Use when the user says "generate UI", "create a component", "build an interface", "Thesys", "generative UI", or asks to turn a description into a React component.

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 implementation code from an approved design blueprint or verbal requirements. Composes context anchoring, architecture, clean code, DDD, security, and test quality into an inside-out implementation workflow. Use when moving from design to code, implementing approved contracts, or when the user says 'implement', 'code this', 'build it', 'forge the code', or 'generate the code'.

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 enhancing, upscaling, restoring, denoising, sharpening, deinterlacing, stabilizing, motion-deblurring, colorizing, SDR-to-HDR converting, or frame-rate/slow-motion converting video (and secondarily images) with Topaz Labs — whether cleaning up AI-generated video, compressed UGC, or old archival footage, or building a generate-low-res-then-upscale delivery pipeline. Covers choosing the right named Topaz model for a specific footage problem, setting parameters, driving the Topaz Platform (Video/Image) REST API's async job lifecycle, judging when enhancement helps versus when it introduces artifacts (over-smoothing, temporal shimmer, face hallucination), and QA of enhanced output. Do not use for originating new video content, non-Topaz upscalers, or purely editorial cuts.

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 and iterate music with ElevenLabs Eleven Music for production deliverables. Use when planning, prompting, API-calling, editing, inpainting, reviewing, or licensing AI-generated songs, instrumental beds, ad music, soundtrack cues, video-to-music scores, stems, or music assets using the ElevenLabs Music API or ElevenCreative Music product.

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。

Produce 3D assets with Meshy (meshy.ai) through its REST API and web platform — text-to-3D, image-to-3D, multi-image-to-3D, AI texturing/PBR, remesh/topology control, UV, and auto-rigging/animation. Use this skill when an agent must generate a mesh from a prompt or reference image, control polygon count and topology, apply or restyle textures, rig a humanoid for animation, export to GLB/FBX/OBJ/USDZ/STL for Unity/Unreal/Blender, drive Meshy's asynchronous job API (auth, polling, streaming, webhooks, credits, rate limits), review generated meshes for game/film/print readiness, or decide whether Meshy is the right tool at all. Not for editing an existing hand-modeled asset's geometry, hard-surface CAD with exact dimensions, or guaranteed-watertight manufacturing parts.

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。