Mastering LLM Skills: Modular Prompt Engineering for Scalable AI Workflows

What is a Skill

A Skill is a sub‑agent expressed in natural language that encapsulates a specific domain context. It consists of a short metadata block that stays in the prompt and a detailed SKILL.md body that is loaded only when the Skill is triggered, thereby reducing token consumption.

Key mechanisms

Lazy loading : Only the metadata is kept in the prompt; the full body is injected into the LLM context on demand.

Dynamic context injection : At runtime the Agent reads SKILL.md and inserts its rules directly into the reasoning context, guiding subsequent tool calls.

Skill file structure

skill-name/
├── SKILL.md          # metadata + execution SOP
├── scripts/          # optional executable scripts (Python/Bash)
├── references/       # optional docs for on‑demand reading
└── assets/           # optional templates, images, etc.

Relation to Prompt, MCP and Function Calling

Prompt is a one‑off textual instruction that disappears after the turn. Function Calling is the low‑level mechanism that lets the LLM invoke external tools; it does not define the orchestration logic. Model Context Protocol (MCP) standardises how external resources (files, databases, APIs) are accessed by the LLM.

Skills sit on top of MCP: they decide *when* and *how* to use those tools, encapsulating complex orchestration logic while keeping the definition reusable and discoverable.

Typical Skill examples

code-reviewer

– enforces architecture, SOLID, security, and performance checks. api-endpoint-generator – generates standardised API code from a project‑wide response model. database-access-review – analyses query plans, indexes, and slow‑query risks. security-audit – scans for SQL injection, XSS, and privilege‑escalation patterns.

Open‑source Skill repositories

Code‑Review‑Expert: https://github.com/sanyuan0704/code-review-expert Git commit with Conventional Commits:

https://github.com/github/awesome-copilot/blob/main/skills/git-commit/SKILL.md

TDD Skill:

https://github.com/obra/superpowers/blob/main/skills/test-driven-development/SKILL.md

Project demo: AI‑Powered Interview Assistant

A reference implementation built with Spring Boot 4.0, Java 21 and Spring AI 2.0 provides three core capabilities:

Intelligent résumé analysis with multi‑dimensional scoring and improvement suggestions.

Simulated interview generation based on résumé content, supporting real‑time Q&A and answer evaluation.

RAG‑enabled knowledge base for technical document retrieval.

Repository URLs (MIT‑style license, no paid tier):

GitHub: https://github.com/Snailclimb/interview-guide Gitee: https://gitee.com/SnailClimb/interview-guide The system architecture follows a classic Agent‑Tool pattern: the Agent loads required Skills, injects their SOPs into the LLM context, and invokes MCP‑exposed tools (e.g., vector store retrieval, script execution) via Function Calling as needed.