ai-prompt-engineering
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (injection/exfiltration), and prompt evaluation/regression testing. Use when designing, debugging, or standardizing prompts for Codex CLI, Claude Code, and OpenAI/Anthropic/Gemini APIs.
npx skills add majiayu000/claude-skill-registry --skill ai-prompt-engineering --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Prompt Engineering — Operational Skill **Modern Best Practices (January 2026)**: versioned prompts, explicit output contracts, regression tests, and safety threat modeling for tool/RAG prompts (OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/). This skill provides **operational guidance** for building production-ready prompts across standard tasks, RAG workflows, agent orchestration, structured outputs, hidden reasoning, and multi-step planning. All content is **operational**, not theoretical. Focus on patterns, checklists, and copy-paste templates. ## Quick Start (60 seconds) 1. Pick a pattern from the decision tree (structured output, extractor, RAG, tools/agent, rewrite, classification). 2. Start from a template in `assets/` and fill in `TASK`, `INPUT`, `RULES`, and `OUTPUT FORMAT`. 3. Add guardrails: instruction/data separation, “no invented details”, missing → `null`/explicit missing. 4. Add validation: JSON parse check, schema check, citations check, post-tool checks. 5. Add evals: 10–20 cases while iterating, 50–200 before release, plus adversarial injection cases. ## Model Notes (2026) This skill includes Claude Code + Codex CL
- Quick Start (60 seconds)
- Model Notes (2026)
- Quick Reference
- Decision Tree: Choosing the Right Pattern
- Copy/Paste: Minimal Prompt Skeletons
- 1) Generic "output contract" skeleton
- 2) Tool/agent skeleton (deterministic)
- 3) RAG skeleton (grounded)
- Operational Checklists
- Context Engineering (2026)
- Context Engineering vs Prompt Engineering
- Key Context Engineering Patterns
- Core Concepts vs Implementation Practices
- Core Concepts (Vendor-Agnostic)
What does the ai-prompt-engineering skill do?
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (injection/exfiltration), and prompt evaluation/regression testing. Use when designing, debugging, or standardizing prompts for Codex CLI, Claude Code, and OpenAI/Anthropic/Gemini APIs.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-prompt-engineering --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.