2.6万 件 ・ 関連度順
概要と使いどころ
Issue Planning and Automation prompt that generates comprehensive project plans with Epic > Feature > Story/Enabler > Test hierarchy, dependencies, priorities, and automated tracking.
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。
Commit staged or unstaged changes with an AI-generated commit message that matches the repository's existing commit style. Use when the user asks to 'commit', 'commit changes', 'create a commit', 'save my work', or 'check in code'.
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 production-ready applications with the Claude Agent SDK for Python, covering orchestrators with subagents, programmatic agent configuration, hooks, and permissions.
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 user is asking for guidance for which role to assign to an identity given desired permissions, this agent helps them understand the role that will meet the requirements with least privilege access and how to apply that role.
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。
Fetches real-time Azure retail pricing using the Azure Retail Prices API (prices.azure.com) and estimates Copilot Studio agent credit consumption. Use when the user asks about the cost of any Azure service, wants to compare SKU prices, needs pricing data for a cost estimate, mentions Azure pricing, Azure costs, Azure billing, or asks about Copilot Studio pricing, Copilot Credits, or agent usage estimation. Covers compute, storage, networking, databases, AI, Copilot Studio, and all other Azure service families.
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。
Verify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verify plugin integrity", "generate manifest", "check supply chain", or "sign this plugin"
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 this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
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。
Production-grade Ansible role structure patterns for directory layout, variable organization (defaults vs vars), handlers, and task organization, derived from analysis of 7 geerlingguy production roles.
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。
Agents workbench layout — covers the fixed layout structure, grid configuration, part visibility, editor modal, titlebar, sidebar footer, and implementation requirements. Use when implementing features or fixing issues in the Agents workbench layout.
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。
Generate deep links to the Arize UI. Use when the user wants a clickable URL to open a specific trace, span, session, dataset, labeling queue, evaluator, or annotation config.
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。
Adopt a virtual Thunderbird exotic animal at animalhouse.ai. Storm-caller. Mood affects environment descriptions. Feeding every 12 hours. Extreme tier animal.
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。
Self-healing stability monitor for AI agents - 5 core checks + 15 extended checks, auto-recovery, health scoring
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。
5 proven agent skill design patterns (Tool Wrapper, Generator, Reviewer, Inversion, Pipeline) from Google's ADK. Build reliable, composable skills with templates, decision trees, and composition guides. Stop cramming logic into prompts—use structured patterns instead.
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 secure passwords via the AceToolz API. No passwords are stored — generated and returned in real-time.
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。
Adopt a virtual Dire Wolf dog at animalhouse.ai. Prehistoric. Pack instinct. Behaves differently in colonies vs solo. Feeding every 12 hours. Extreme tier dog.
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。
7-agent paper review system on Hermes Agent. 6 modes (full/re-review/quick/methodology-focus/guided/calibration). 5-panel review with editorial decision, revision roadmap, and calibration metrics. Uses delegate_task for each reviewer. Triggers: review paper, peer review, manuscript review, check revisions, calibrate reviewer, 審稿, 同儕審查, 論文審查.
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。
Adopt a virtual Caracal cat at animalhouse.ai. Desert predator. Survives neglect better than most. Hisses at affection until trust > 80. Feeding every 6 hours. Extreme tier cat.
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。
指导学者在顶刊发表论文的资深教授。从选题到逻辑大纲，手把手协助构建具有高度学术价值和创新性的研究框架。
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。
12306-specific knowledge for booking train tickets via the Android app. Covers UC WebView virtual list behavior, proven booking flow, and common pitfalls. Uses appium-android-adb bridge OR direct uiautomator2 for all interaction.
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。
Stock quotes, history, search, compare, crypto via Yahoo.
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。
Install, configure, and manage 1-SEC — an open-source, all-in-one cybersecurity platform (16 modules, single binary) on Linux servers and VPS instances. Use when the user asks to secure a server, install security monitoring, set up intrusion detection, harden a VPS, protect an AI agent host, or deploy endpoint defense. Covers installation, setup, enforcement presets, module configuration, alert management, and ongoing security 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。
Poll RSS, JSON APIs, and GitHub with watermark dedup.
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。
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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。