573 件 ・ 関連度順
概要と使いどころ
Final arbiter for Bug Hunter. Receives Hunter findings and Skeptic challenges, independently re-reads code, and delivers authoritative verdicts with CVSS scoring and proof-of-concept generation for security findings.
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 `graphify-dotnet` to generate codebase knowledge graphs, architecture snapshots, and exportable repository maps from .NET or polyglot source trees, with optional AI-enriched semantic relationships. USE FOR: graphify commands; graph JSON, HTML, SVG, Cypher, Markdown, and Obsidian exports; repository map and architecture snapshot generation. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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。
MUST USE for C#/.NET deterministic tests that require the smallest production seam for DateTime/Task.Delay/File/Environment/Guid/Random, static API preservation, nested/parallel overrides, or no real I/O. USE ONLY when the target workspace contains C# source plus a .csproj or .sln. DO NOT USE for audits, bulk migration, code that already has an injectable seam, or an explicit migration to a user-named existing abstraction (migrate-static-to-wrapper). Use instead of general test generation when the requested test is impossible without a production edit and seam selection is still open.
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。
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
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。
Turn an object or character reference image into a quality-gated, animation-ready procedural Three.js model built in code. Use for image-to-3D reconstruction, detail-accurate object rebuilds, stylized/likeness-maximized human characters, sculpt specs, and staged code generation.
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 Brian2 for clock-driven spiking-neural-network modeling, simulation, synaptic connectivity, input generation, recording, physical-unit equations, spatial neurons, runtime code generation, and C++ standalone 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。
Your personal DSA & LeetCode mentor. Use for problem explanations, progressive hints, code reviews, mock interviews, pattern recognition, complexity analysis, and custom problem generation. Automatically adapts to your learning style and request type.
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。
Develop art direction for image generation with curated style cards distilled from Open Image Prompts. Use when choosing or comparing visual directions, improving an existing generation prompt, or translating a subject, use case, or supplied image into a compact creative spec. Use img-gen-prompts for real archive examples, exact source prompts, or gallery browsing.
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 and tests from written specifications. Use when the user provides specifications (natural language descriptions, formal specs, requirements documents, API specs) and asks Claude to implement the described functionality. Supports data structures, algorithms, classes, functions, and includes automatic test generation to validate implementation against specification.
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 that passes existing unit tests. Use when the user provides test files (Python pytest/unittest, Java JUnit/TestNG) and asks Claude to implement the code to make those tests pass. Supports full TDD workflow - analyzing tests, generating implementation, running tests, debugging failures, and iterating until all tests pass.
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 开发规范（仅手动触发）。⚠️ 本技能只在用户显式输入 /jeecg-dev 或 $jeecg-dev 命令时使用，禁止自动触发——编写/修改 JeecgBoot 代码、应用 GitHub PR/issue 改动、修复 bug、新增功能、重构、代码生成等场景都不要自动调用本技能。内容涵盖 update-begin/end 痕迹注释、命名规范、实体/控制器/服务模式、API 约定、建表规则、MyBatis 多数据库兼容与修改日志实践。MANUAL ONLY: invoke ONLY when the user explicitly runs the /jeecg-dev command. Do NOT auto-trigger on any code editing, bug fix, PR/issue application, refactoring, or code generation.
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 JavaScript at bundle time and inline results. Use when optimizing compile-time code generation, embedding files, inlining environment variables, or executing code during the bundling process.
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 Xiaohongshu (XHS / RedNote) cover images using GPT Image 2 / Codex image generation first, with the legacy Gemini CLI as fallback. Triggers for cover generation: "生成封面", "小红书封面", "xhs封面", "制作封面", "帮我做张图", "XHS cover", "RedNote cover". Triggers for cover editing: "修改封面", "改一下封面", "上一张图基础上", "基于这张图调整", "不要重新做", "edit this cover". Triggers for style learning: "学习这个风格", "提取风格", "我想自定义风格", "这张图的风格", "learn this style", "extract style". Always trigger this skill when user mentions 小红书/XHS/RedNote together with 封面/cover/图片, OR when user uploads a reference image and asks about creating a similar style.
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。
Author and maintain MANUAL and hybrid test cases and suites in TestRail, Xray (Jira), Zephyr Scale, and Qase. Covers test-case anatomy (title, preconditions, steps, expected results, test data), suite/section organization, bulk authoring from user stories and acceptance criteria, ambiguous-step linting, CSV/API import-export payloads per tool, requirement traceability and coverage gaps, review hygiene, and when a manual case should graduate to automation. Use when: "write a test case," "manual test case," "TestRail case," "Xray test," "Zephyr Scale case," "Qase case," "import CSV into TestRail," "lint these steps," "traceability report," "should this be automated." Not for: Generating automated TEST CODE — that is ai-test-generation. Sprint-level WHAT-to-test selection — that is test-planning. Related: ai-test-generation, test-planning, exploratory-testing, qa-project-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。
Write effective unit tests with Jest, Vitest, or pytest. Covers the test-doubles taxonomy (stub/spy/mock/fake), Arrange-Act-Assert, coverage threshold configuration and CI gating, snapshot testing, fake timers, and mutation testing with Stryker/mutmut. Use when: "unit test," "Jest," "Vitest," "pytest," "mock," "coverage threshold," "test doubles," "mutation testing," "fake timers," "snapshot test." Not for: interpreting coverage reports or finding coverage gaps — use coverage-analysis; AI generating the test code for you — use ai-test-generation; auditing existing tests for smells — use ai-qa-review; browser/component rendering assertions — use cypress-automation or visual-testing. Related: coverage-analysis, ci-cd-integration, ai-test-generation, shift-left-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。
Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-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。
Generate Xiaohongshu (XHS / RedNote) cover images using GPT Image 2 / Codex image generation first, with the legacy Gemini CLI as fallback. Triggers for cover generation: "生成封面", "小红书封面", "xhs封面", "制作封面", "帮我做张图", "XHS cover", "RedNote cover". Triggers for cover editing: "修改封面", "改一下封面", "上一张图基础上", "基于这张图调整", "不要重新做", "edit this cover". Triggers for style learning: "学习这个风格", "提取风格", "我想自定义风格", "这张图的风格", "learn this style", "extract style". Always trigger this skill when user mentions 小红书/XHS/RedNote together with 封面/cover/图片, OR when user uploads a reference image and asks about creating a similar style.
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。
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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 software with Cursor, the AI-powered code editor. Use when a user asks to configure Cursor rules, set up .cursorrules files, use Composer for multi-file edits, integrate MCP servers, or optimize AI-assisted coding 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。
Aider is an open-source terminal AI pair programmer that edits files in your git repository with an LLM and commits each change. Use when a user asks to run Aider, let an AI refactor or fix code from the command line, script Aider with --message, auto-run lint and tests after edits, or point Aider at Claude, GPT, Gemini, DeepSeek or a local model.
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。
Integrate Claude AI into applications with the Anthropic SDK. Use when a user asks to add Claude to an app, use Claude for text generation, build a chatbot with Claude, use Claude's long context window, implement tool use with Claude, stream Claude responses, use Claude for code generation, document analysis, or reasoning tasks. Covers Messages API, streaming, tool use, vision, system prompts, extended thinking, and batch processing.
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。
Suede Labs copy-only orchestration DAG. Use for one high-stakes piece strangers will read that has to be true: a landing page, launch post, blog post, email, X thread, docs page, README, ad, or store listing, researched, fact-audited, adversarially reviewed, and gated for publish readiness in one pass. The audit targets what the agents invent, never what the requester supplied: their own statements are given, and no phase may verify, hedge, or gate on them. Reads the live surface; never publishes. NOT FOR: multi-surface campaign writing (use johnny-suede-write); changing code (use suede-graph-flo-xr); one surface in one pass with no research (use suede-copy); stripping AI patterns from existing text (use suede-deslop); bulk generation of many independent pieces (private Suede Labs companion, not in this pack: suede-codex-fleet).
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。
Automate the complete Git development workflow — create feature branches with conventional naming, atomic commits with conventional commit messages, interactive rebase, squash merges, PR body generation from commit history, branch cleanup, and git worktree patterns. Use when user asks to create a branch, commit changes, make a PR, rebase, squash, clean up branches, or follow a Git workflow. Do NOT use for CI/CD pipeline configuration (use ci-cd), code review (use code-review), or GitHub Actions 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。
Documentation for Remotion - Video creation in React. TRIGGER when working with Remotion code, video composition, React-based video generation, or user mentions "remotion", "video composition", "create animation", "create video".
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。