4,821 件 ・ 関連度順
ISO 13485 Quality Management System implementation and maintenance for medical device organizations. Provides QMS design, documentation control, internal auditing, CAPA management, and certification support.
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 high-converting landing pages with page structure, copy frameworks (PAS, AIDA, BAB), CTA strategy, SEO meta, and Core Web Vitals optimization. Use when creating landing pages, campaign pages, lead capture pages, or conversion 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。
Create and maintain the foundational context doc (positioning, ICP, competitive landscape, brand voice, customer language) that other marketing skills read first. Use when setting up context, defining the ICP, or setting brand 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。
Operations management across process optimization, efficiency, and continuous improvement. Use when designing workflows, building capacity plans, evaluating vendors, running Lean Six Sigma DMAIC projects, or optimizing cost-per-unit.
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。
Free tool marketing strategy covering idea evaluation, tool design, lead capture architecture, SEO landing pages, launch playbook, and ROI measurement for calculators, generators, checkers, graders, and interactive 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。
Map, measure and improve business processes — SIPOC and swimlane capture, cycle-time and bottleneck analysis, handoff diagnosis, and a payback-ranked improvement backlog. Use when a process is slow, error-prone, or crosses too many teams.
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。
Structured C-suite onboarding via a founder interview that captures company context into a persistent file used by all advisory skills. Use when setting up advisory context, refreshing stale context, or onboarding a new executive.
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。
Operations leadership advisor on business operations, process optimization, and scaling infrastructure. Use when designing operational processes, planning headcount capacity, optimizing vendors, or scaling operations.
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。
Help the user turn their Retentioneering ideas, friction reports, bug findings, or feature needs into high-quality upstream contributions: from capturing and validating the idea, through minimal reproductions and issue drafts, to preparing, testing, and submitting a pull request that follows this repository's conventions. Use when the user says they found a bug, wants a feature, wrote a workaround worth upstreaming, or asks how to contribute, open an issue, or make a PR to retentioneering-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。
Performance regression detection. Captures TTFB, FCP, LCP, bundle sizes, request counts. Regression thresholds: timing >50% or >500ms. Baseline comparison, trend tracking, budget compliance scorecard.
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。
YC Office Hours — product idea diagnostic before writing code. Two modes: Startup (6 forcing questions with rigorous pushback) and Builder (generative brainstorming). Outputs design doc. Includes landscape research, premise challenge, alternatives generation, visual wireframe sketching, and optional cross-model second opinion.
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-deploy monitoring. Watches live app via headless browser for console errors, performance regressions, page failures, visual anomalies. Alert on deltas not absolutes, transient tolerance (2+ consecutive checks). Default 10-minute monitoring window. Baseline capture mode.
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。
Builds Vue 3 components with Composition API, configures Nuxt 3 SSR/SSG, sets up Pinia stores, scaffolds Quasar/Capacitor mobile apps, and implements PWA features.
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。
Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations.
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。
Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.
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。
Integrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text search, nearby search, or autocomplete suggestions; retrieve detailed place information including hours, contacts, and addresses; monitor geographical boundaries with geofences; or track device locations. Covers authentication, SDK integration, and all Amazon Location Service capabilities.
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。
Diagnose and remediate cluster-wide HyperPod (EKS or Slurm) problems — creation / deployment failures (CloudFormation, EFA health check, lifecycle scripts, capacity), EKS access, node replacement, CloudFormation nested-stack errors, post-maintenance rollback state, dangling nodes, autoscaler conflicts. Includes `--validate` pre-flight. Read-only.
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。
Give an AI agent persistent, cross-session long-term memory through MindMemOS. Covers installing and authenticating the mindmemos CLI, the full command interface (add / search / get / update / delete / feedback / dreaming) with parameters and examples, guidance on which capability to use when, plus a Python SDK example. To wire memory into a specific agent host (OpenClaw, DeepSeek Harness, Codex, Claude, etc.), see references/.
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。
Design a retargeting strategy: audience windows, sequencing, frequency caps. "plan our remarketing campaigns"
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。
Comprehensive Scrum Master assistant for sprint planning, backlog grooming, retrospectives, capacity planning, and daily standups with intelligent context-aware reporting
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 testing web applications with visual verification - automates Chrome browser interactions, element selection, and screenshot capture for confirming UI functionality
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。