「quality」の検索結果

Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.

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。

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task 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。

Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.

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。

Post-implementation quality gate that reviews changes, fixes small issues (scout rule), creates tasks for medium issues, and generates analysis for large issues.

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。

Post-implementation quality gate that reviews changes, fixes small issues (scout rule), creates tasks for medium issues, and generates analysis for large issues.

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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

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 high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.

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。

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training 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。

Opinionated constraints for building better web interfaces with coding agents. Use this skill whenever the user asks you to build, review, or fix a UI — landing pages, dashboards, components, or any frontend code — and whenever you write Tailwind CSS, motion/react animations, accessible component primitives (Base UI, React Aria, Radix), or review agent-generated interface code for quality, accessibility, and performance. Also trigger for "make the UI better", "review my interface", "clean up this component", or any frontend work in React, Next.js, or plain HTML/CSS.

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。

Manages brand identity, visual design system, and creative quality for MMIND.ai. Generates complete image prompts for Nano Banana Pro (Gemini), enforces the strict 5-color palette, flat-vector SaaS style, and typography standards. Use when user needs graphic prompts, brand consistency checks, visual style guidance, design briefs, or creative QA for any marketing asset.

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 reviewing PRs/diffs/branches/documents for .NET quality, correctness, performance, security, data access, messaging, and observability. Includes adversarial critical-thinking mode for skeptical/cynical review requests. Keywords: code review, review PR, review diff, critical review, cynical review, adversarial review, production readiness, low GC, AOT, security 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。

Create Chinese PPT pages through a prompt-to-image-to-editable-PPT workflow. Use when the user wants 中文PPT图片提示词, PPT image prompts, 16:9中文PPT页面图片, 直接生成PPT图片, 根据提示词生成图片, 把图片/PDF/截图复刻成可编辑PPT, 转成可编辑PPTX, 按图片完全复刻PPT, or full production from topic/source material to generated page images and editable PowerPoint slides with editable text, cards, tables, lines, callouts and high-quality icon/image assets.

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 balancing eng-team velocity vs quality vs burnout — on-call load, focus fragmentation, reorg shock. Triggers on 'team is burning out', 'why is velocity dropping'.

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 to iterate a change until tests/quality checks pass — bounded run→revise→re-run gated by a numeric threshold, then a judge confirms. Triggers 'iterate to green', 'keep fixing until 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。

Use when PHPStan, Rector, or ECS output appears — "phpstan says mixed", type errors, "fix code style", "run rector" — even when Eloquent/Laravel/model code is also mentioned.

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 diff builds more than the task needs — code that should not exist, a dependency the platform already covers, or a clever form where a flat one reads better. Deletion-hunting, not 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。

Use when a diff needs a readability review — naming, single-responsibility, DRY, dead code, mismatch with codebase conventions — dispatched by /review-changes, /do-and-judge, /judge.

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 working with GitHub Actions — workflow YAML, quality gates, test matrices, deployment triggers, reusable workflows — even when the user just says 'my CI is failing' or 'add a check'.

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 says 'refactor this', 'rename class', or 'move method'. Safely refactors code in any language — finds all callers, updates downstream dependencies, verifies via quality tools.

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 the user says "review this", "check my code", or wants feedback on changes. Reviews for correctness, quality, security, and coding standards.

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。

Avalia LLMs em mais de 60 benchmarks acadêmicos (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use quando benchmarking de qualidade de modelo, comparação de modelos, relatório de resultados acadêmicos ou rastreamento de progresso de treinamento. Padrão da indústria usado por EleutherAI, HuggingFace e laboratórios principais. Suporta HuggingFace, vLLM, APIs.

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。

Curação de dados acelerada por GPU para treinamento de LLM. Suporta texto/imagem/vídeo/áudio. Recursos incluem deduplicação fuzzy (16× mais rápida), filtragem de qualidade (30+ heurísticas), deduplicação semântica, redação de PII, detecção NSFW. Escala em GPUs com RAPIDS. Use para preparar datasets de treinamento de alta qualidade, limpar dados web ou deduplicar grandes corpora.

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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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。

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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。