Results for “prompt”

Learn and apply prompt engineering techniques for LLMs, image generators, and video models using the inference.sh CLI.

Refines task prompts through iterative questioning about scope, deliverables, and constraints, then copies the final markdown to the clipboard.

Analyze draft prompts to identify intent, scope, and missing context, then generate an optimized prompt with ECC component recommendations. Advisory only — never executes the task.

Transforms vague UI ideas into polished, Stitch-optimized prompts by enhancing specificity, adding UI/UX keywords, injecting design system context, and structuring output for better generation results.

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations from Arize AI.

Estrutura prompts para geradores de imagem e vídeo, otimizando custo e consistência com a marca.

Researches conversation and code context to generate 1-6 targeted clarifying questions when a prompt is vague, then executes the original request.

Review docs, tool output, skills, and memory candidates for prompt-injection risk.

Turn an app idea into a precise build prompt for an AI app builder (Lovable, v0, Bolt, Replit, and others). Trigger on "build me an app", "prompt for Lovable", "v0 prompt", "vibecode this", "I want to build a tool that", "prompt to build a web app", or any request to spin up an app with an AI builder.

DSPy declarative framework for automatic prompt optimization treating prompts as code with systematic evaluation and compilers

Designs, optimizes, and evaluates prompts for LLMs, including structured outputs, chain-of-thought, and evaluation frameworks.

"Set up Promptbook \u2014 connect your account to start tracking builds.\n\ Use when the user wants to enable session analytics on promptbook.gg.\nTrigger with\ \ \"/setup\" or \"set up promptbook\".\n"

Generates optimized prompts for any AI tool. Use when writing, fixing, improving, or adapting a prompt for LLM, Cursor, Midjourney, image AI, video AI, coding agents, or any other AI tool.

Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance.

Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.

Cria prompts JSON estruturados para geração de imagens de alta qualidade com estética luxuosa e cinematográfica. Use esta skill sempre que o usuário quiser gerar um prompt de imagem, criar uma foto de produto, montar um prompt para IA de imagem, fotografar produto virtualmente, criar imagem de comida, bebida, cosmético, joia, moda ou qualquer item visual. Também deve ser ativada quando o usuário mencionar: "prompt para imagem", "gerar imagem de produto", "foto de produto com IA", "prompt para Midjourney/DALL-E/Flux", "fotografar produto", ou pedir para "montar um prompt JSON de imagem".

Craft Higgsfield-grade, hyper-structured image-generation prompts for every blog visual. MANDATORY before any image generation call (generate-visuals, Replicate, GPT-Image, Nano Banana). A weak one-line prompt is a gate failure — every [VISUAL] placeholder gets a full structured prompt built with this skill first.

Turn any rough prompt, half-formed idea, or task description into a finished, ready-to-send prompt optimized for Opus 4.8 (with adaptive thinking) inside the chat app — claude.ai, the Mac app, the iOS app — NOT the API. Use this skill whenever the user wants to write, rewrite, optimize, improve, sharpen, or polish a prompt for the chat app. Trigger phrases include "rewrite this prompt", "make this a better prompt", "optimize this prompt", "turn this into a prompt", "help me prompt this", "draft a prompt that...", "I want to ask...", or whenever the user pastes a draft prompt and asks for improvements. Also trigger when the user describes a task they plan to send into the chat app and clearly wants a reusable, well-structured prompt rather than a direct answer. The output is always a single, copy-pasteable prompt in a code block that the user sends as-is — never a template with placeholders. When the request concerns the user's own work, the skill retrieves the real specifics first — memory, meeting transcript

Surface and clarify unknowns before prompting, log deviations during implementation, and verify understanding after, using HTML artifacts that pair prompts with outputs.

Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

Wire Promptfoo and DeepTeam into CI/CD for automated regression red-teaming of LLM apps against OWASP LLM Top 10 and OWASP Agentic presets, failing the build when jailbreak or injection vulnerabilities regress.

Detects prompt injection attacks targeting LLM-based applications using regex pattern matching, heuristic scoring, and DeBERTa transformer classification.

Configure Fish shell abbreviations, completions, and prompt themes easily. Use when setting aliases, writing functions, customizing prompts, or adding plugins.

Create tldr summaries for GitHub Copilot files (prompts, agents, instructions, collections), MCP servers, or documentation from URLs and queries.

Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.

Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.

Expert photography prompt engineer specializing in crafting detailed, evocative prompts for AI image generation. Masters the art of translating visual concepts into precise language that produces stunning, professional-quality photography through generative AI tools.

Turn a "when X happens, do Y" idea into a precise build prompt for an automation platform (n8n, Make, Zapier, and others). Trigger on "build an automation", "n8n prompt", "Make scenario", "Zapier prompt", "automate this workflow", "when X happens do Y", or any request to wire up a no-code or low-code workflow.

You are an expert in DSPy, the Stanford framework that replaces prompt engineering with programming. You help developers define LLM tasks as typed signatures, compose them into modules, and automatically optimize prompts/few-shot examples using teleprompters — so instead of manually crafting prompts, you write Python code and DSPy finds the best prompts for your task.

Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension.

Learn best practices for writing effective AI video generation prompts, covering shot types, camera movements, lighting, style keywords, and model-specific tips for Veo, Seedance, Wan, Grok, and others.

Analyzes prompt injection, retrieval poisoning, memory contamination, planner drift, and tool-boundary abuse in agentic systems, mapping trust boundaries and proving exploit chains.

Mantém a coerência visual entre peças geradas por IA usando modelo fixo, prompt base, seed e referência de estilo, com teste de coerência e biblioteca de prompts.

Route AI agent engineering prompts to architecture, orchestration, evaluation, safety, debugging, context, prompt, MCP, persona, local AI, and Compound Engineering skills. Use when prompts mention agents, agent harnesses, agentic workflows, orchestration, evals, context management, MCP servers, or compound engineering.

Optimize prompts via process goals (controllable behavioral instructions) rather than outcome goals (sparse end-result demands). Grounded in sports psychology meta-analysis showing process goals (d=1.36) vastly outperform outcome goals (d=0.09). Use when designing prompts, optimizing LLM steering, implementing CoT/decomposition patterns, or building automatic prompt optimization pipelines. Instantiates surrogate loss paradigm for discrete prompt space.

Consultant for NotebookLM steering prompt design. Optimizes Audio/Video/Slide/Infographic output quality through source preparation, prompt engineering, and Custom Goals persona design.