3,502 件 ・ 関連度順
概要と使いどころ
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specialized agents or to parallelize AI work.
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 UI/UX designs for quality, AI-generated slop, and accessibility. Use when reviewing designs, detecting slop patterns, validating WCAG compliance, checking design-token adherence, or reviewing responsive design across breakpoints.
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。
VP of Engineering advisor on org design, productivity, quality, delivery, and capacity planning. Use when scoring engineering org health, designing the eng org, planning capacity, or building the productivity dashboard.
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。
Review REST API designs for quality, consistency, and breaking changes. Lints OpenAPI specs, generates scorecards, and detects breaking changes between versions. Use when designing APIs, reviewing contracts, or managing API versioning.
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。
Publication-quality scientific figure generation with 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。
ISO 13485 medical device quality management compliance 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。
Eng manager-mode plan review. Scope challenge, architecture review, code quality review, detailed test coverage diagram, performance review. Produces test plan artifact for downstream QA. Optional cross-model outside voice.
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。
Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics. Per-person breakdowns with praise and growth areas. Modes: default 7d, 24h, 14d, 30d, compare, global cross-project. Shipping streaks and tweetable summary.
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。
Designs production-grade RAG systems with document chunking, embedding generation, vector store configuration, hybrid search pipelines, reranking, and retrieval quality 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。
Quality gate 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。
Review the agent's own recent outputs for quality and accuracy
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。
Multi-pass code review orchestration with parallel reviewers for quality, security, performance, and test coverage
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。
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or 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。
Wrap up the current session: verify quality gate passed, remind user to commit, archive completed tasks, and record session progress to the developer journal. Use when done coding and ready to end the 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。
Translate marketing content using the brand glossary and do-not-translate terms, with quality scoring. "translate this into Japanese"
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。
Score translated content for publication: quality, brand voice, market compliance. "is this German translation ready to publish"
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 a batch of content in one run: ranked quality report, systemic issues. "score our whole content library"
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 all language versions of a site for parity and quality. Tags only → hreflang-check. "is the German site missing pages"
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 powerhouse that generates production-ready mega-prompts for any role, industry, and task through intelligent 7-question flow, 69 comprehensive presets across 15 professional domains (technical, business, creative, legal, finance, HR, design, customer, executive, manufacturing, R&D, regulatory, specialized-technical, research, creative-media), multiple output formats (XML/Claude/ChatGPT/Gemini), quality validation gates, and contextual best practices from OpenAI/Anthropic/Google. Supports both core and advanced modes with testing scenarios and prompt variations.
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。
Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
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。
Discover and recommend **combinations** of agent skills to complete complex, multi-faceted tasks. Provides two recommendation strategies — **Maximum Quality** (best skill per subtask) and **Minimum Dependencies** (fewest installs). Use this skill whenever the user wants to find skills, asks "how do I do X", "find a skill for X", or describes a task that likely requires multiple capabilities working together. Also use when the user mentions composing workflows, building pipelines, or needs help across several domains at once — even if they only say "find me a skill". This skill supersedes simple single-skill search by decomposing the task into subtasks and assembling an optimal skill portfolio.
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。
Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
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 wants to build an evaluation system for an LLM/agent application but doesn't know where to start — they have traces, prompts, RAG pipelines, or nothing at all. Also use when the user mentions evaluation, eval, benchmarking, testing LLM quality, measuring agent performance, assessing RAG accuracy, or wants to compare prompts/models. This skill is the entry router: it asks diagnostic questions then recommends which sub-skill (local workflow) to use next.
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。