239건 · 관련도순
Use when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing
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ComfyUI prompt engineering knowledge covering CLIP text encoding syntax, weight modifiers, model-specific prompting strategies, and best practices
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Turn a repeatable AI prompt or workflow into a structured, shareable skill for the affiliate-skills GitHub repository. Use this skill when the user wants to create a new skill, write a SKILL.md, convert a prompt to a skill, share a skill via the GitHub repo, or document an AI workflow. Also trigger for: "create a skill", "write a skill", "make this a skill", "turn this into a skill", "publish skill", "add skill to list", "write SKILL.md", "skill from prompt", "document this workflow", "package this as a skill".
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This skill should be used when the user asks to "generate video prompts", "create Seedance prompts", "write video descriptions", mentions "Seedance", "seedance", "火山引擎", "火山引擎平台", "视频提示词", "视频生成", "AI视频", "短剧", "广告视频", "视频延长", or discusses video prompt engineering, AI video generation, or Seedance 2.0 workflows.
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Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.
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Optimize prompts for code-related tasks following prompt-engineering best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.
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Interactive prompt engineering coach that elevates vague prompts through Socratic dialogue, multiple transformation styles, and guided learning. Use when improving prompts, learning agentic engineering, or wanting coached guidance rather than automated transformation. NEVER auto-executes - always displays and asks first.
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Expert prompt engineering for Seedance 2.0. Use when the user wants to generate a video with multimodal assets (images, videos, audio) and needs the best possible prompt.
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Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.
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Guia especializado em padrões de engenharia de prompts, melhores práticas e técnicas de otimização. Use quando o usuário quer melhorar prompts, aprender estratégias de prompting ou depurar comportamento de agentes.
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Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)
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Manage how context, the text supplied to an AI, is compressed with RTK filters. Preview compression on real prompts, validate and install filter files, and inspect filter loading diagnostics.
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Coordinate software investigations, implementation, code reviews, and testing through Codex tasks in desktop, remote, or cloud environments. Inspect pull requests and automated checks, follow up on authorized fixes, and report results with verification limits.
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Find available computers and remote environments, then start or continue a task in the requested location. Route work involving files or apps on the user's computer to that computer, and follow progress while preserving the selected environment.
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Improve an existing AI agent by analyzing execution records, user feedback, and performance metrics. Refine prompts and workflows, compare original and revised versions through tests, and plan staged releases with monitoring and rollback when results worsen.
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Guide the design of AI product features with output validation, responses displayed as they arrive, prompt versioning, and testing. Address API costs, failures, and unsafe input when preparing language model integrations for production.
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Design focused products built around AI APIs, with guidance on prompts, model selection, output validation, and user experience. Track usage and set cost limits to connect product behavior with operating costs and pricing decisions.
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Build and operate LLM applications with guidance on RAG, which uses retrieved documents to inform answers. Covers document indexing, vector database selection, prompt improvement, response evaluation, caching, and estimates of model usage costs.
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Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and tool definitions are packed into the window. Produces a context inventory with a keep/cut/restructure verdict per component, ordering and caching fixes, and a token budget. For wording-level prompt tuning use prompt-optimizer.
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This skill should be used when the user asks to "estimate LLM costs", "count tokens in prompts", "optimize prompt token usage", "compare model pricing", or "reduce LLM API costs".
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Restructure any rough build, research, or legal-drafting request into an IRAC-shaped prompt — Issue, Rule, Analysis, Conclusion — optimized for a frontier model. It's the bar-exam framework, repurposed as prompt engineering. The skill leads with the issue and ends with the conclusion (where models weight attention most), forces you to name your constraints and non-goals, and specifies what "good" looks like before a single token is generated. Use it before any non-trivial build, or whenever a vague ask deserves a precise brief.
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Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
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Shapes agent behavior via instruction framing and style transfer. Use when composing dispatch prompts or writing skill instructions for parallel review agents.
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Generate visual prototype prompts from SCR- entries for Google Stitch (or equivalent UI generation tool). Triggers on: 'make a prototype', 'visualize screens', 'generate Stitch prompt', 'I need a visual demo', 'prototype the workflow', 'show me what this looks like', 'get this to a demo', 'visual gate'. Consumes SCR- (Screen Flow Definition), PER- (Personas), UJ- (User Journeys), DES- (Design Components). Outputs Stitch prompt blocks per SCR- entry + Feedback Capture Template. No new SoT IDs created — this skill makes existing SCR- entries visual and routes feedback back to them.
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