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Found 376 Skills
Explain how code works in detail. Use when trying to understand unfamiliar code, complex logic, or system architecture.
Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
Create effective custom prompts for Cursor AI. Triggers on "cursor prompts", "prompt engineering cursor", "better cursor prompts", "cursor instructions". Use when working with cursor custom prompts functionality. Trigger with phrases like "cursor custom prompts", "cursor prompts", "cursor".
Use when improving agent prompts, frontmatter, and tool restrictions.
電影感 AI 繪圖提示詞生成器。根據使用者的場景描述，自動選配攝影機模組、光影預設， 產出 Midjourney 與 Gemini 3 Pro 雙平台格式的 cinematic prompt。
Unified prompt engineering and requirements planning. Use when exploring ideas, improving prompts, creating PRDs, generating task breakdowns, or iterating on existing plans. Includes Jira integration for creating subtasks from plans.
Expert guidance for creating, building, and using Claude Code subagents and the Task tool. Use when working with subagents, setting up agent configurations, understanding how agents work, or using the Task tool to launch specialized agents.
Expert prompt engineering for creating effective prompts for Claude, GPT, and other LLMs. Use when writing system prompts, user prompts, few-shot examples, or optimizing existing prompts for better performance.
Generate and edit images using AI. Use when the user asks to "generate an image," "create an image," "make a picture," "edit this image," "modify this image," or when building UI that needs visual assets like hero images, icons, or illustrations.
Enhanced reasoning patterns via slash commands (/think, /verify, /adversarial, /edge, /compare, /confidence, /budget, /constrain, /json, /flip, /assumptions, /tensions, /analyze, /trade) or natural language ("argue against", "what could break", "show reasoning", "deep review", "meta-prompts", "thinking modes", "second-best approach", "list assumptions", "opposing perspectives").
Evaluates and optimizes agent skills using a DSPy-powered GEPA (Generate/Evaluate/Propose/Apply) loop. Loads scenario YAML files as DSPy datasets, scores outputs with pattern-matching metrics, and optimizes prompts via BootstrapFewShot or MIPROv2 teleprompters. Also generates new scenario YAML files from skill descriptions.
Provide concrete examples—existing code patterns, style samples, input/output pairs—to guide AI toward your project's conventions