4,596 件 ・ 関連度順
概要と使いどころ
Use when a task needs the judgment of a manuscript or content editor — diagnosing structural problems in a draft, writing a developmental edit letter, running a line-edit or copyedit pass, deciding what to cut to hit a word-count target, or triaging conflicting author feedback.
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 task needs the judgment of a doctoral-level Clinical or Counseling Psychologist — building a psychological test battery to answer a referral question, interpreting a WAIS/MMPI/PAI profile against validity indices, working a differential diagnosis (e.g. ADHD vs. anxiety-driven inattention, depression vs. early dementia), or writing a feedback-session-ready diagnostic formulation report.
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 task needs the judgment of an audiovisual equipment installer/repairer — diagnosing an EDID or HDCP handshake failure between a source and a display, sizing a display's screen size and brightness for a room's viewing distance and ambient light, specifying an HDMI/HDBaseT/AVoIP signal run against its rated distance ceiling, designing conference-room speaker placement and acoustics, or drawing the install-vs-programming boundary with a control-system (Crestron/Extron/AMX) integrator.
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 task needs the judgment of an Art Director — commissioning and choosing between divergent creative concepts for a campaign, triaging client feedback into concept vs. execution notes, pressure-testing a concept against a production budget/timeline before it's locked in, or managing a shoot/production budget across a multi-deliverable campaign.
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。
Spring Boot 4 data layer implementation for Domain-Driven Design. Use when implementing JPA or JDBC aggregates, Spring Data repositories, transactional services, projections, or entity auditing. Covers aggregate roots with AbstractAggregateRoot, value object mapping, EntityGraph for N+1 prevention, and Spring Boot 4 specifics (JSpecify null-safety, AOT repositories). For DDD concepts and design decisions, see the domain-driven-design skill.
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-agent review resolution with trust-but-verify methodology. Accepts a PR number/URL or a local code-review report file. Extracts review comments, verifies each with parallel agents using confidence scoring (0-100), filters false positives, presents a triage report for approval, then applies fixes and replies. Use when user runs /resolve-review, mentions "resolve PR comments", "address review feedback", "fix PR suggestions", "handle review comments", or "resolve review 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。
Manage Volcengine's AI-native BaaS platform (Supabase edition / AIDAP / 火山引擎 AI 原生 BaaS 平台 Supabase 版) — a Volcengine-operated distribution that differs from official Supabase. Use when the user asks to create, inspect, or manage Volcengine Supabase or AIDAP resources — workspaces, branches, computes, SQL queries, schema changes, Auth, Realtime, Edge Functions, Storage buckets, frontend static-site hosting (Pages), API keys, connection info, or TypeScript type generation — or when a Volcengine deployment selects AIDAP as its database provider. Also trigger on Chinese prompts mentioning "火山引擎 Supabase" or "火山 Supabase". Operations run through the byted-supabase-cli command-line tool (installed via `npm i -g @byted-supabase/cli`; this is NOT the official `supabase` CLI). Do NOT use it for general database discussions, non-Supabase services (RDS MySQL, Redis), or pure client-side coding unrelated to Supabase backend 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。
Guidance for Tauri v2 haptics plugin with feedback patterns and graceful fallback.
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。
Analyzes code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback. Use when reviewing pull requests, conducting code quality audits, identifying refactoring opportunities, or checking for security issues. Invoke for PR reviews, code quality checks, refactoring suggestions, review code, code quality. Complements specialized skills (security-reviewer, test-master) by providing broad-scope review across correctness, performance, maintainability, and test coverage in a single 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。
Guide for writing idiomatic Rust code based on Apollo GraphQL's best practices handbook. Use this skill when: (1) writing new Rust code or functions, (2) reviewing or refactoring existing Rust code, (3) deciding between borrowing vs cloning or ownership patterns, (4) implementing error handling with Result types, (5) optimizing Rust code for performance, (6) writing tests or documentation for Rust projects.
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。
Orchestrate work as a cyclic directed graph of subagent-executed nodes with transition criteria on edges, budgeted cycles, and per-node gates (runtime verification, metric, or artifact). Use when the user says "workgraph", "run this as a graph", "graph this work", or when a goal has parallel branches, feedback loops, or mixed gate types that a linear loop cannot express. Every node is sized to a 64k context budget, so small goals run as a two-node do/verify graph and large goals scale out without caps. For clock-driven or metric-driven single loops, timeboxed-iterating and autoresearch remain lighter.
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。
Orchestrate AI coding agents (Claude Code, Codex, OpenCode) in isolated sandboxes using the @ai-hero/sandcastle SDK. Use when the user needs to (1) run agents AFK in Docker/Podman containers, (2) build multi-agent pipelines with plan-execute-review patterns, (3) run parallel agents on separate worktrees, (4) create iterative agent loops with maxIterations, (5) extract structured output from agent runs, (6) set up sandcastle in a new or existing project, or (7) write prompt files with template args and shell expressions.
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。
Observes runtime behavior of binaries via QEMU emulation, GDB debugging, and Frida hooking — syscall tracing, breakpoints, memory inspection, and function interception. Use when running, tracing, or debugging a binary, verifying hypotheses from static analysis, or watching actual syscalls, network calls, or file access; requires human approval before any execution.
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。
Installs and configures reverse engineering tools including radare2, Ghidra, GDB, QEMU, Frida, and cross-compilation toolchains for ARM64, ARMv7, x86_64, and MIPS. Use when a tool command fails, a required tool is missing, or you need to set up a fresh analysis environment.
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 production-grade Kubernetes manifests — Deployments with probes and security contexts, Services, Ingress with TLS, HPA, PDB, NetworkPolicy, ConfigMaps, Secrets — with optional Helm charts, Kustomize overlays, Istio mesh, and ArgoCD GitOps
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。
Full-spectrum product research pipeline. Runs competitive analysis, technology trend scouting, user feedback analysis, and feature ideation. Triggers: on: research, competitive research, market research, feature discovery, what should we build 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。
Audit factory production scheduling systems for finite capacity planning, Drum-Buffer-Rope (DBR) Theory of Constraints implementation, dispatching rules (EDD, SPT, critical ratio), sequence-dependent changeover optimization, MRP integration.
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 mobile UX implementation against platform conventions -- navigation patterns, gesture handling, pull-to-refresh, infinite scroll, skeleton screens, haptic feedback, adaptive layouts, deep linking, and accessibility..
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 a production-grade Industrial IoT protocol bridge — OPC UA ↔ MQTT Sparkplug B, Modbus → MQTT, or direct PLC (Rockwell/Siemens/Beckhoff) → cloud (AWS IoT Core / Azure IoT Hub / GCP IoT / self-hosted HiveMQ)..
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。
Analyze program impact measurement software for logic model completeness, indicator tracking rigor, data collection methodology, causal attribution modeling, cost-effectiveness analysis, beneficiary feedback integration, and funder reporting 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。
Feature discovery agent — mines project docs, memory, user feedback, and competitor gaps to surface the highest-leverage features to build next. Outputs a prioritized feature backlog.
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 maintain a multi-channel product feed across Google Merchant Center, Meta Catalog (Instagram + Facebook Shops), TikTok Shop, Pinterest Catalogs, Bing Shopping, and Snapchat Catalogs from a single product source of truth (Shopify, BigCommerce, WooCommerce, custom DB)..
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 a production-grade OEE (Overall Equipment Effectiveness) measurement pipeline for a manufacturing line — data ingestion from MQTT/OPC-UA/Modbus/CSV, A×P×Q calculation engine, Six Big Losses categorizer, MTBF/MTTR/TEEP metrics, Grafana or Streamlit 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 custom components and layouts against shadcn design patterns, visual style conventions (Vega, Nova, Maia, Lyra, Mira), composition rules, design token usage, and Radix/Base UI composition patterns. Use PROACTIVELY after writing or modifying any custom UI component, page layout, or variant of a shadcn component. Use when the user says "review this component", "check my spacing", "does this follow shadcn patterns", "audit this layout", "is this shadcn-idiomatic", or shares a component for feedback. Catches spacing drift, hardcoded colors, missing data-slot attributes, inconsistent theme application, and composition anti-patterns that shipping-fast-with-AI tools often miss.
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。