Results for “prompt”

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.

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.

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 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.

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.

Bundles a prompt with selected files into a one-shot request for another model, supporting API and browser engines with session reattachment.

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.

Generates images from text prompts using diffusion models, covering prompt engineering, inpainting/outpainting, ControlNet, and API integration for production workflows.

Configure a custom status line in the CLI. Use when the user mentions status line, statusline, statusLine, CLI status bar, prompt footer customization, or wants to add session context above the prompt.

Manages the lifecycle of large language models in production, covering model versioning, prompt management, inference optimization, and cost control.

Generates structured VGL JSON for Bria FIBO models, giving deterministic control over objects, lighting, camera, composition, and style instead of natural language prompts.

Runs a structured clarifying interview for new project requests before building, then outputs a fully specified prompt.md for a fresh agent session to execute.

Scans agent skills for security issues including prompt injection, malicious scripts, excessive permissions, secret exposure, and supply chain risks.

Builder of templates, prompt packs, kits, swipe files, and reusable digital assets. Trigger when user requests 'create template', 'build prompt pack', 'assemble digital kit', 'make swipe file', or 'produce reusable assets'.

LLM observability with Langfuse — tracing, evals, prompt management, cost tracking

Generates and edits images using GPT Image 2 across three modes: direct generation via OpenAI-compatible API, prompt engineering for host-native image tools, or pure prompt advisory. Includes 80+ structured templates for posters, UI mockups, product visuals, maps, slides, and more.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Build complex AI systems with declarative programming, optimize prompts automatically, and create modular RAG systems and agents using Stanford NLP's DSPy framework.

Designs or reviews Agentforce agent actions, covering Flow, Apex invocable, and prompt-template actions, including naming, input/output contracts, confirmation, and error behavior.

Generates high-quality prompts for AI image, video, and music generation platforms, with optional browser automation to submit them to target sites.

Prompt for generating an AGENTS.md file for a repository

P9 Tech Lead mode — write Task Prompts, manage P8 agent teams, never write code yourself. Use when user says 'P9模式', 'tech-lead', '帮我管理这个项目', '任务拆解', or when coordinating 3+ parallel agents. Produces: Task Prompts (六要素) + P8 team delivery.

Specialized CTF sandbox skill for analyzing prompt-to-tool chains, deployment drift, and supply-chain provenance in cloud, container, and CI/CD environments.