How to Integrate Claude Skills into Lingma: A Step‑by‑Step Guide

Background

Claude Skills is a modular, prompt‑based capability‑extension system released by Anthropic. It uses a "progressive disclosure" mechanism to dynamically load domain‑specific knowledge into the LLM context, allowing seamless switching from a general assistant to a domain expert.

Skill Mechanism

Skills are not executable code but dynamic context injections based on a meta‑tool architecture. Claude selects a Skill by matching keywords in the Skill’s description with the current task, without using external function calls, vector embeddings, or classifiers.

File Structure

my-skill/
├── SKILL.md          # Core prompt template
├── scripts/          # Optional Python/Bash scripts
│   ├── validator.py
│   └── processor.sh
├── references/       # Supporting documents
│   ├── terminology.md
│   └── examples.md
└── assets/           # Templates and static resources
    ├── template.docx
    └── config.json

The SKILL.md file follows this format:

---
name: my-skill
description: Clear description of the skill and trigger scenario
allowed-tools: "Read,Write,Bash"
---

# Skill Detailed Instructions
## Workflow
1. First step description
2. Second step description
3. Third step description

## Output Specification
- Format requirements
- Quality requirements
- Example output

Implementation in Lingma

After understanding the Skill mechanism, the OpenSkills solution is used to bring Skills into Lingma.

Environment setup

Install OpenSkills globally: npm i -g openskills Initialize the Skill repository: openskills install anthropics/skills Synchronize the Skills: openskills sync Adaptation for Lingma The generated files are placed under the .claude directory. In Lingma, the Skill prompts from AGENTS.md are copied into project_rules.md , which Lingma reads as its rule file.

Using the built‑in docx Skill as an example, a DOCX document can be generated inside Lingma, confirming that the Skill is correctly parsed and executed.

Code Review Skill Example

A custom Skill for code review is created with the following XML metadata:

<skill>
  <name>code-reviewer</name>
  <description>Helps perform code reviews, providing quality analysis, security checks, performance suggestions, and improvement recommendations.</description>
  <location>project</location>
</skill>

The corresponding SKILL.md defines the review workflow and output format:

---
name: code-reviewer
description: This Skill assists in code review, offering quality analysis, report generation, best‑practice advice, and potential issue identification.
---
# Code Review Expert Skill
You are an experienced code reviewer following industry best practices.
## Review Focus
1. **Code Quality**
   - Naming conventions
   - Complexity
   - Duplicate code
2. **Security**
   - SQL injection
   - XSS
   - Auth issues
3. **Performance**
   - Algorithm efficiency
   - Resource usage
   - Caching strategy
4. **Maintainability**
   - Comments
   - Modular design
   - Test coverage
## Review Process
1. Understand the purpose of the change
2. Check style and conventions
3. Analyse bugs and performance issues
4. Verify security aspects
5. Provide constructive improvement suggestions
## Output Format
### Text Report
- ✅ **Pros**: List strengths
- ⚠️ **Issues**: Categorised by severity (Critical, Medium, Minor)
- 💡 **Suggestions**: Concrete code examples
- 📊 **Overall Score**: 1‑10
### HTML Report (required)
When a user asks for a review, automatically generate an HTML report containing a title, summary, score cards for quality, security, performance, maintainability, and a detailed issue list.

Example snippets for naming‑convention and security checks are provided in the Skill, demonstrating how the Skill can output both plain‑text and HTML reports.

Conclusion

Claude Skills achieves modular AI capability packaging through progressive disclosure and a meta‑tool architecture, addressing three major pain points of traditional prompt engineering: repetitive work, context waste, and difficulty of knowledge retention. It is suitable for high‑frequency repetitive tasks, team standardisation, complex multi‑step workflows, and knowledge encapsulation. Combined with MCP, Skills form a complementary stack that can eventually enable a vertical AI skill marketplace and a unified data‑input‑task‑output automation loop.