Skills: Skills Introduction & Core Concepts
Last updated: 2026-08-31
Still writing long prompts from scratch every conversation? Skills let you package best practices into a button — next time, invoke it with one click.
1. What Are Skills
Skills are reusable capability extension units for AI coding assistants, composed of prompts, tool bindings, and triggers. Whether it's Claude Code's .claude/skills/, OpenCode's skills/ directory, or Cursor Rules, they all share the same philosophy:
| Dimension | Raw Chat (No Skills) | Using Skills |
|---|---|---|
| Prompts | Manually typed each time | Pre-built templates, loaded with one click |
| Tool Calls | Must explicitly specify | Auto-bound, activated on demand |
| Consistency | Varies by person, quality fluctuates | Standardized output, stable quality |
| Reusability | Copy-paste | Version-managed, team-shared |
| Activation | Manual | Auto-detection + manual invocation |
(1) Positioning of Skills
Skills fill the gap between "pure prompt engineering" and "fully autonomous Agents":
- Not simple prompt templates — they bind tools, define trigger conditions, and manage context
- Not complete Agent frameworks — they are Agent "plugins" that enhance rather than replace
- Not one-off scripts — versioned, composable, shareable
(2) Core Capabilities
Skills Core Architecture
├── Prompt Templates (system prompts + task templates + examples)
├── Tool Bindings (file read/write / Shell / search / MCP tools)
├── Triggers (keywords / file types / context conditions)
├── Variable System (user input / environment variables / context injection)
├── Composition Orchestration (skill chains / parallel skills / conditional branches)
└── Versioning & Sharing (Git management / skill marketplace / team sync)
About Code Example Outputs
This course uses a deterministic/non-deterministic separation pattern for code examples, which is the industry best practice for AI Agent tool tutorials:
| Marker | Meaning | Your Output |
|---|---|---|
| Output: | Deterministic results (installation, configuration, counts, etc.) | Should closely match the example |
| Interaction Flow: | Agent behavior flow (LLM calls, tool selection, etc.) | Actual text will differ, but the flow will be similar |
| Verification: | How to check exercise results | Follow the described steps to verify |
1 + 1 = 2 (always the same). AI Agent tools: agent.chat("analyze code") = ??? (different every time). This is an essential characteristic of AI Agent tools, not a bug.
2. Core Concepts
(1) Prompt
The prompt is the soul of a Skill, defining the behavioral norms for AI under that skill:
name: code-review
system_prompt: |
You are a senior code review expert. Follow these principles when reviewing:
1. Understand intent first, then check implementation
2. Prioritize security issues and performance bottlenecks
3. Provide specific fix suggestions, not vague commentary
4. Severity levels: 🔴 Must fix / 🟡 Suggested fix / 🟢 For reference
(2) Tool Bindings
Tool bindings define the set of tools a Skill can use during execution:
| Tool Type | Description | Examples |
|---|---|---|
| File Tools | Read and write project files | Read, Write, Edit |
| Shell Tools | Execute commands | Bash, Terminal |
| Search Tools | Code and content search | Grep, Glob |
| MCP Tools | External service connections | Database, API, Browser |
| Custom Tools | User-developed | Any callable function |
(3) Trigger
Triggers determine when a Skill is activated:
triggers:
- keyword: "review code|review"
- file_pattern: "**/*.py"
- context: "git_diff_available"
(4) Context
Context manages information injection during Skill execution, including project structure, code style, team conventions, etc.
3. From Manual to Automated
Alice is a team tech lead who wants to standardize code reviews. At first, she manually writes long prompts every time:
Scenario: Standardizing Code Reviews
Traditional approach — Alice repeats the prompt for every review:
Alice: "Please review this PR, focus on security, performance, readability,
grade by severity, provide fix suggestions......"
(She has to write 200+ words of prompts every time)
Skills approach — Alice creates a code-review skill, then invokes it with one click:
# .claude/skills/code-review.md
When I say "review", automatically perform a code review......
Bob comments: "The value of Skills isn't just saving time — it turns the team's best practices into standard operations. Anyone who invokes it gets consistent, high-quality output."
4. Platform Comparison
| Platform | Skills Location | Format | Features |
|---|---|---|---|
| Claude Code | .claude/skills/*.md |
Markdown + YAML | Native support, rich triggers |
| OpenCode | skills/ directory |
Markdown | Simple and intuitive, active community |
| Cursor | .cursor/rules/ |
Markdown | Project-level rules, auto-activation |
| Windsurf | .windsurfrules |
Text | Single file configuration |
| Copilot | .github/copilot-instructions.md |
Markdown | GitHub ecosystem integration |
❓ FAQ
📖 Summary
- Skills are reusable combination packages of prompts + tools + triggers
- Four core concepts: Prompt, Tool Bindings, Trigger, Context
- Cross-platform: Claude Code, OpenCode, Cursor all support similar mechanisms
- A paradigm shift from "manually writing prompts every time" to "one-click standardized invocation"
📝 Exercises
- Basic (⭐): Explain in your own words the three core differences between a Skill and a regular prompt.
- Intermediate (⭐⭐): List 3 scenarios from your daily work, analyze which ones are suitable for encapsulating as Skills, and explain why.
- Advanced (⭐⭐⭐): Design a "deployment check" Skill: what prompts are needed? Which tools to bind? What are the trigger conditions?
