prompt-engineering
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install ancoleman-ai-design-components-prompt-engineering
Repository
Skill path: skills/prompt-engineering
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: Development teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: ancoleman.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install prompt-engineering into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/ancoleman/ai-design-components before adding prompt-engineering to shared team environments
- Use prompt-engineering for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.