ai-llm
Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference serving, and lifecycle operations with cost/safety controls.
npx skills add majiayu000/claude-skill-registry --skill ai-llm --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.
# LLM Development & Engineering — Complete Reference Build, evaluate, and deploy LLM systems with **modern production standards**. This skill covers the full LLM lifecycle: - **Development**: Strategy selection, dataset design, instruction tuning, PEFT/LoRA fine-tuning - **Evaluation**: Automated testing, LLM-as-judge, metrics, rollout gates - **Deployment**: Serving handoff, latency/cost budgeting, reliability patterns (see `ai-llm-inference`) - **Operations**: Quality monitoring, change management, incident response (see `ai-mlops`) - **Safety**: Threat modeling, data governance, layered mitigations (NIST AI RMF: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf) **Modern Best Practices (January 2026)**: - Treat the model as a **component** with contracts, budgets, and rollback plans (not “magic”). - Separate **core concepts** (tokenization, context, training vs adaptation) from **implementation choices** (providers, SDKs). - Gate upgrades with repeatable evals and staged rollout; avoid blind model swaps. **For detailed patterns:** See [Resources](#resources-best-practices--operational-patterns) and [Templates](#templates-copy-paste-ready) sections below. --- ## Quick Refere
- Quick Reference
- Decision Tree: LLM System Architecture
- Core Concepts (Vendor-Agnostic)
- Implementation Practices (Tooling Examples)
- Do / Avoid
- When to Use This Skill
- Scope Boundaries (Use These Skills for Depth)
- Resources (Best Practices & Operational Patterns)
- Core Operational Patterns
- Domain-Specific Patterns
- Templates (Copy-Paste Ready)
- Selection & Governance
- RAG Pipelines
- Prompt Engineering
What does the ai-llm skill do?
Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference serving, and lifecycle operations with cost/safety controls.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-llm --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.