Results for “prompt engineering”

Ultimate AI prompt database and builder with 2,500+ curated prompts across 19 categories and 17 AI models (Midjourney, Flux, Leonardo AI, DALL-E, Sora, Imagen, Mystic, Stable Diffusion, Ideogram, Adobe Firefly, and more). Search, build, enhance, and adapt prompts for image generation, video creation, text, and meta-prompt generators. Use when user says "prompt", "find a prompt", "build a prompt", "enhance my prompt", "adapt this prompt", "prompt library", "image prompt", "video prompt", "midjourney prompt", "flux prompt", or any AI prompt engineering task. Do NOT use for terminal/CLI prompts, JavaScript prompt() dialogs, user input prompts, or bash prompt customization.

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Specialized software engineering prompt methodology for Claude. Use when building prompts for system design, code review, incident response, security analysis, API design, architecture decisions, RFCs, ADRs, runbooks, or technical leadership. Trigger on: "build a prompt for code review," "system design prompt," "SRE prompt," "security review prompt," "incident response prompt," "API design prompt," "architecture decision prompt," "RFC prompt," "runbook prompt." Provides 11 tested approaches across identity, reasoning, and output for engineering analysis. Do NOT use for general coding help, writing code, or debugging — this builds prompts that shape engineering analysis, not code itself. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available).

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Test marketing prompts against shared cases, score required content, prohibited phrases, and output format, and compare alternatives. Keep version history and diffs, use reusable templates, and check that prompt or model updates preserve output quality.

• Compare two prompts for ads, email, or social content
• Keep prompt versions and review changes
• Check output quality after prompt or model updates

Create and improve prompts for agents, workflows, and AI evaluations. Reusable Handlebars templates, which insert data into a fixed structure, help generate consistent instructions from YAML or JSON inputs for use in other tasks.

• Improving prompts for recurring tasks
• Generating agent briefs from templates
• Creating AI evaluation and scoring prompts

Design and refine prompts for AI applications using examples, reusable templates, and structured outputs such as JSON. Test variations for accuracy, consistency, response time, and token usage to diagnose unreliable answers and formatting failures.

• Refine prompts that produce inconsistent answers
• Build reusable prompt templates with variables
• Design JSON outputs that follow a defined schema

Create or improve AI prompts through a guided conversation about goals, roles, examples, and output formats. Receive a complete, reusable prompt with an explanation of its design, including behavioral boundaries for public-facing AI roles when needed.

• Improving an existing AI prompt
• Designing prompts for product naming
• Defining boundaries for a customer service bot

Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.

• Improve a prompt using failed production traces
• Fix responses that use the wrong output format
• Review unsupported answers in retrieval-based responses

Use when refining a draft user prompt before sending it to a coding agent. Decomposes goal/context/constraints/acceptance/output-format, detects ambiguity and model-coupled framing, then emits a model-agnostic rewrite. Supports --analyze, --score, and --deep. NOT for prompts from scratch (mk:brainstorming), plans/reviews (mk:elicit), implementation plans (mk:plan-creator), or general codebase scouting (mk:scout).

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Design and refine prompts for LLM applications by organizing roles, context, instructions, constraints, and output formats. Develop representative examples, manage the information supplied to the model, and evaluate changes to improve how consistently responses match expectations.

• Structuring a system prompt for an LLM application
• Specifying response formats and constraints
• Creating examples that include edge cases

Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.

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Review AI prompts for harmful output risks, bias, privacy concerns, security weaknesses, and clarity. Receive an assessment, a revised prompt, and suggested tests with explanations of the principles behind each improvement.

• Review a prompt before putting it into use
• Check wording for stereotypes and bias
• Assess data exposure and prompt injection risks

Turn a rough idea or an existing prompt into a ready-to-paste instruction for a specific AI tool. The skill clarifies the task, output format, constraints, and success criteria for writing, image generation, or coding agents.

• Turning a rough idea into an AI prompt
• Improving a prompt for a specific AI tool
• Defining scope and completion criteria for coding agents

Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".

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Prompt 工程全流程。覆盖 System Prompt 设计、Few-shot 构造、Chain-of-Thought、 Structured Output、Prompt 版本管理、A/B 测试、注入防御、模板引擎。 触发关键词：Prompt、提示词、System Prompt、Few-shot、CoT、结构化输出、 Prompt 模板、Prompt 注入、Prompt 版本、提示词优化。

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Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).

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Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).

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Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.

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Claude API (platform.claude.com) のプロンプトエンジニアリング・評価リファレンス。 prompt engineering, XML タグ, few-shot, system prompts, モデル別プロンプティング（Fable 5.1 / Fable 5 / Opus 5.5 / Opus 5 / Sonnet 5.5 / Sonnet 5 / Haiku 5.5）。 evals, grading（LLM-graded 等）, jailbreak・prompt leak 対策, hallucination 低減, classification, ticket routing, commerce agent 等ユースケース実装。

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Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.

• Planning prompt evaluation before deployment
• Allocating context capacity and designing retrieval-based answers
• Planning an authorized AI agent security assessment

Improve AI instructions with examples, clear output formats, and comparison tests. Use reusable templates and system prompts, which set ongoing behavior, to address inconsistent answers and investigate unexpected agent behavior.

• Making AI summaries more consistent
• Structuring information extracted from support tickets
• Creating reusable code review prompts

Design and refine prompts for language model applications. Choose useful examples, create reusable templates and system instructions, and compare prompt variations using accuracy, consistency, response time, and token usage to guide improvements.

• Refine prompts that produce inconsistent answers
• Create reusable templates with variables and conditions
• Select relevant input-output examples for a task

Create, refine, and optimize high-quality YAML prompts for AI assistants. Use when working with prompt templates, system prompts, agent prompts, or any prompt engineering tasks. Provides structure guidelines, template patterns, and quality standards for YAML-based prompts.

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Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.

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AI Engineering Toolkit workflow skill. Use this skill when the user needs 6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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