“prompt engineering”

World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production.

Prompt engineering and LLM evaluation.

Prompt engineering frameworks for building, testing, versioning, and evaluating prompts: chain-of-thought, few-shot, regression testing, and rubrics.

Generate optimized LLM prompts using chain-of-thought, ReAct, and other scientific reasoning patterns

This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug…

Prompt engineering workflow for building reliable assistants and agents: task decomposition, instruction hierarchy, tool-use patterns, safety constraints, and…

面向电商设计师、海报美工、品牌视觉、UI设计师、信息图编辑、商业摄影师、内容创作者等需要高质量可控出图的角色，在需要用 GPT-Image-2 生成电商主图、电影海报、信息图、品牌视觉、UI截图、古籍国风、角色IP等场景时，通过「Prompt as…

Use for AI and agent engineering work: system prompt design, tool call architecture, context engineering, memory and learning systems, multi-agent…

Design or improve Starlight agents from a value opportunity through architecture, agent contracts, prompt engineering, bounded proactivity and evaluation.

Design and build use cases, POCs and production systems with TypeSafe's Jev decision model (System One): choose a design mode and a use-case archetype, build…

Use OpenAI CLIP for zero-shot image-text inference, prompt engineering, feature extraction, and responsible evaluation.

A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems.

Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show.

Use when the user shares a raw, vague, or first-draft prompt and wants it clarified, tightened, strengthened, or made more reliable — or asks to improve…

Master context engineering for AI agent systems.

ICM-based zero-to-verified engineering workflow.

Turns a linear agent, prompt chain, or orchestration script into an engineered graph — nodes with contracts, edges that carry data, fan-out/fan-in only where…

Lightweight WebGL/WebGPU game engine with entity-component architecture and visual editor integration.

Chaos engineering: hypothesis-driven fault injection to surface weakness before users do.

Understanding how a compiled, obfuscated, packed or virtualized target works: binaries and firmware, Android APKs and iOS IPAs, .NET and JVM assemblies, WASM…

Analyzes and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich regions, checking curated glycan evidence, and…

Run bounded, evidence-driven loops for repeated, resumable, delegated, or scheduled work.

Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features