Results for “prompt”

Introduces Prompt-Driven Development (PDD), a methodology that uses version-controlled natural-language prompts as the source of truth for generating code, tests, and documentation, with a practical adoption roadmap.

Generate new screens from text prompts or images, edit existing screens with prompts and design system tokens, and generate design variants using Stitch MCP. Includes prompt enhancement pipeline, design mappings, professional UI/UX terminology, design tokens and theme system capabilities.

Generates new design screens from text or images, edits existing screens with prompts and design tokens, and creates design variants using Stitch MCP, with prompt enhancement and asset downloading.

Forces one deeper, more creative pass on an answer or plan while preserving the original escalation prompt verbatim.

Enriches vague, low-detail prompts into structured, agent-optimized XML before execution. INVOKE IMMEDIATELY — before any tool use or file reads — when you detect any of these signals: prompt under 10 words with no file path or error message; vague action verbs with no object ("fix the bug", "make it better", "clean this up", "refactor this", "optimize performance", "improve the UI", "add authentication", "add payments", "add notifications", "build the feature"); CLARIFIER_ADVISORY in your context window; user says "clarify", "help me describe this", "enrich this prompt", "structure my request". Also triggers on: "make this work", "it's broken", "it looks bad", "add X" with no further detail, "implement Y" with no constraints. Do NOT trigger on: prompts ending with ?, prompts containing error messages or stack traces, prompts with specific file paths, prompts already containing acceptance criteria or success metrics.

Quick reference for Windows Command Prompt commands, including environment variable editing, remote desktop, and encoding settings.

Detect prompt injections and jailbreak attempts in LLM applications using Meta's 86M parameter classifier. Filter user inputs, third-party data, and RAG documents with low latency and multilingual support.

ScepticAgent AI prompt reference. Use whenever the user asks about the prompts, wants to change analysis behaviour, modify what the AI looks for, adjust highlight categories or scoring, understand what instructions are sent to the AI, or tune the output format of analysis or highlights.

Batch-generate images through the OpenAI Images API with a prompt sampler and gallery output.

Schedule prompts to run later, once or on a repeating cron expression, with tools to create, list, and delete scheduled tasks.

Prompt and interaction designer for reliable AI behavior. Use when the user asks to talk to Luna, requests prompt engineering, or needs agent behavior specification.

Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.

Generates high-fidelity UI screens, multi-page websites, DESIGN.md documentation, and React components from natural-language prompts using the Stitch MCP server, with support for prompt refinement and walkthrough video creation via Remotion.

Read-only OWASP LLM Top 10 audit of app-facing AI features: prompt injection, data leak, supply chain, poisoning, unsafe output, excessive agency, system-prompt leak, RAG/embedding risks, misinformation, unbounded consumption. Use when "audit LLM security", "prompt injection", "jailbreak my chatbot", "is my AI safe".

Instrument LLM applications with Langfuse for tracing, prompt versioning, evaluation, and dataset management across Python and JavaScript SDKs.

Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.

Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.

Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.

Provides expertise in Langfuse for LLM observability, including tracing, prompt management, evaluation, and integration with LangChain, LlamaIndex, and OpenAI.

Generates high-fidelity images using reasoning-driven prompts and structured creative briefs via muapi.ai.

Enhances plain-language requests into structured, reviewable prompts and inspects a self-hosted MCP gateway with provider-free defaults.

FLOW framework integration — evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "seo flow", "evidence-led SEO", "find leverage optimize win", or wants stage-specific SEO prompts.

Forces a second, harder escalation pass using the original glazer prompt words verbatim, pushing for a sharper replacement thesis rather than more polish.

Builds or debugs Anthropic Claude integrations using @anthropic-ai/sdk, covering messages, tool use, extended thinking, prompt caching, and streaming.

Instrument LLM applications with Langfuse for tracing, prompt management, evaluation, and cost tracking, including integrations with OpenAI, LangChain, and LlamaIndex.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Guides building production-grade AI features with LLM integration patterns, RAG architecture, prompt engineering, and cost optimization.

Generates a clipboard-ready handoff prompt for another AI agent to investigate or continue a task, with structured context and review instructions.

Generates or edits images for projects using the OpenAI Image API, with support for batch runs and structured prompt augmentation.

Generates copyable scientific figure prompts from research documents, papers, and reference images for AI illustration models.

FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "blog flow", "evidence-led blogging", "find optimize win", or wants stage-specific blog prompts.

Threat-model and harden AI agents, RAG systems, assistants, and tool-using workflows against direct, indirect, stored, cross-agent, and multimodal prompt injection. Use when reviewing an agent architecture, isolating untrusted content, constraining tools and egress, protecting secrets, adding injection-focused tests, investigating a suspected injection incident, or documenting residual prompt-injection risk.

Create concise tldr summaries for GitHub Copilot customization files, MCP server documentation, or Copilot documentation from URLs or queries.