Have you ever experienced this frustrating situation?
You're using AI programming tools, but find yourself
repeating the same questions over and over,
consuming massive amounts of tokens,
only to end up with code that doesn't meet your expectations.
The root cause of this problem isn't "insufficient AI capabilities."
It's simply not knowing the correct workflow and Skills utilization methods.
After using Claude Code in real projects for six months, I've reached one clear conclusion:
AI programming efficiency is determined not by tool selection, but by Skills construction and operational methods.
This article systematically organizes the practical workflow of Claude Code + Skills + automated Skill package generation based on actual usage experience. It's designed for developers who want to improve development efficiency, reduce token consumption, and build personal AI workflows.
What is Claude Code? Why is it Different from Other AI Tools?
Claude Code is a CLI-level AI programming assistant provided by Anthropic.
Key Differences from Other AI Tools
Traditional AI Programming Tools:
- Single question-answer format
- Cannot understand entire project context
- Require starting from scratch every time
Claude Code Features:
- Project-level context understanding: Grasps entire directory structure
- Multi-Agent collaboration: Multiple AIs work with role division
- Skills mechanism: Predefined execution flows for efficiency
- Local/API hybrid usage: Flexible operation options
Official Documentation: https://code.claude.com/docs/en/overview
Claude Code Setup: From Basic Installation to Practical Use
1. Installation and Initial Configuration
npm install -g @anthropic-ai/claude-code
After installation, a ./claude directory will be generated in your project root.
First run:
claude
Follow the prompts to configure your account and you can start using it immediately.
2. Third-party API Integration (Optional)
If you prefer not to use the official API directly, these tools are available:
These handle API routing and switching between different keys.
MyClaude: Extension Project to Maximize Claude Code Capabilities
For efficient Claude Code utilization, I use the MyClaude project.
Project URL: https://github.com/cexll/myclaude
MyClaude Installation Steps
git clone https://github.com/cexll/myclaude.git
cd myclaude
Run installation:
# macOS
python3 install.py --install-dir ~/.claude
# Windows
python install.py --install-dir %USERPROFILE%\.claude
If it fails:
python install.py
Practical Command Suite Provided by MyClaude
Core Development Commands
/dev # Complete development flow
/code # Code generation
/debug # Debugging
/test # Test generation
/review # Code review
Auxiliary Commands
/refactor # Refactoring
/optimize # Performance optimization
/docs # Documentation generation
/bugfix # Bug fixing
/ask # Direct questions
/think # Analysis
/enhance-prompt # Prompt enhancement
Understanding Commands / Agents / Skills Relationships
Commands = Input instructions (interface)
Agents = Execution processes (decision makers)
Skills = Tool capabilities (rules and steps)
After command input, Claude Code automatically selects Agents based on tasks and loads corresponding Skills for execution.
Methods to Check Available Commands and Skills
Method 1: Command Line
python install.py --list-modules
Method 2: Local Directory Check
# macOS / Linux
ls ~/.claude/commands/
ls ~/.claude/skills/
# Windows
dir %USERPROFILE%\.claude\commands\
dir %USERPROFILE%\.claude\skills\
Method 3: Within Claude Code
/help
Or ask Claude directly.
What are Skills? Why Do They Dramatically Reduce Token Consumption?
The Essence of Skills
Skills are collections of predefined execution flows and constraint rules.
When Claude determines that the current task matches a specific Skill, it directly loads the corresponding rules and executes them in sequence.
Token Reduction Mechanism
Without Skills:
- Claude must scan all context every time
- Repeatedly processes the same explanations
- Uncertain execution paths
With Skills:
- Direct entry into specified execution paths
- Efficient processing following predefined rules
- Consistent behavioral patterns
Results:
- Faster responses
- Lower token consumption
- More stable operations
Skill Seekers: Automated Specialized Skills Generation Tool
After basic capability setup, true efficiency improvement comes from customizing Skills for specific domains.
Here we use the Skill Seekers project:
https://github.com/yusufkaraaslan/Skill_Seekers
Skill Seekers Functionality
Skill Seekers is a tool that automatically converts documents into Claude Skills.
Supported Data Sources:
- Official documentation sites
- GitHub repositories
- PDF files
Typical Workflow
Documents / GitHub / PDF
↓
Automatic crawling
↓
SKILL.md generation
↓
.zip packaging
↓
Upload to Claude
Skill Seekers Installation
pip install skill-seekers
# or
uv tool install skill-seekers
Complete Skill Seekers Command Structure Guide
skill-seekers <command> [options]
| Subcommand | Data Source | Use Case |
|---|---|---|
| scrape | Documentation sites | Official docs, tech sites |
| github | GitHub repositories | Source code analysis |
| PDF files | Manuals, specifications | |
| unified | Multiple sources | Complex projects |
scrape: Web Documentation Processing (Most Used)
skill-seekers scrape [options]
| Parameter | Description | Example |
|---|---|---|
| -config | Config file path | configs/react.json |
| -name | Skill name | myproject |
| -url | Documentation URL | –url https://docs.example.com |
| -description | Skill description | –description "React development framework" |
| -skip-scrape | Use cache | -skip-scrape |
| -enhance | API enhancement | -enhance |
| -enhance-local | Local Claude enhancement | -enhance-local |
| -dry-run | Preview | -dry-run |
| -async | Asynchronous | -async |
| -workers | Concurrency | -workers 8 |
Typical Usage Examples
skill-seekers scrape --config configs/react.json --enhance-local
skill-seekers scrape --name myproject --url https://docs.example.com
github: Source Code Analysis
skill-seekers github [options]
| Parameter | Description |
|---|---|
| -repo | owner/repo |
| -name | Skill name |
| -description | Description |
skill-seekers github --repo microsoft/TypeScript --name typescript
pdf: PDF Document Processing
skill-seekers pdf [options]
| Parameter | Description |
|---|---|
| PDF path | |
| -name | Skill name |
| -description | Description |
| -from-json | JSON construction |
unified: Multi-source Integrated Generation (Advanced)
skill-seekers unified --config configs/react_unified.json
| Parameter | Description |
|---|---|
| -merge-mode | rule-based / claude-enhanced |
| -dry-run | Preview |
AI Enhancement: enhance Function
skill-seekers enhance <skill_directory>
Effects:
- Basic SKILL.md (~75 lines)
- Enhanced to complete professional guide (~500 lines)
Included Content:
- Real code samples
- Quick reference
- Usage scenario suggestions
Local enhancement example:
skill-seekers scrape --config configs/react.json --enhance-local
⚠️ Testing Note: While documentation claims local model support, current testing shows command recognition issues that require further verification.
skill-seekers Parameter Loading Methods Summary (Usage Recommendations)
| Method | Suitable Users |
|---|---|
| Quick mode | Beginners, one-time use |
| Interactive mode | Unfamiliar with parameters |
| JSON configuration | Stable reuse |
| GitHub mode | Source code analysis (requires GITHUBTOKEN) |
Practical Supplement: API Verification and Collaboration Beyond AI Workflows
In actual engineering projects, Claude Code + Skills primarily solve code generation, understanding, and process automation problems. However, when projects enter backend development or cross-team collaboration phases, AI-generated results alone are insufficient, and API verification, debugging, and documentation management remain essential.
Integrated Workflow Practice
In my practice, I typically combine this AI workflow with API tools like Apidog. The overall division of labor is relatively clear:
Claude Code + Skills:
- Rapid generation, analysis, and refactoring of API-related code
Apidog:
- Actual verification of API behavior
- Unified entry point for API documentation and debugging
Benefits of Combined Usage
The advantage of this combination approach is the ability to separate AI's "reasoning and generation capabilities" from engineering tools' "deterministic verification capabilities".
On one hand, it avoids over-reliance on model outputs; on the other hand, it reduces communication costs at the API level during multi-person collaboration.
From an engineering practice perspective, Claude Code is more like an intelligent development assistant, while Apidog handles execution, verification, and collaboration roles at the API level. Together, they form a relatively stable, complementary usage pattern in real projects.
Summary: Building Sustainable AI Workflows
Through actual usage, I've gradually realized that Claude Code and Skills aren't meant to replace developers, but to structure and reuse experience and processes.
When they're properly integrated into engineering systems, AI becomes more like a stable efficiency amplifier rather than a one-time inspiration tool.
Truly sustainable AI workflows emerge from reasonable combinations with existing engineering tools, not complete dependence on models themselves.
If you're trying to introduce AI into your development process, I recommend starting with controllable, verifiable small scenarios and gradually making it part of your toolchain.
How has your AI development experience been? If you have impressive experiences with Claude Code or Skills, please share them in the comments!
References:
- Claude Code Official Documentation: https://code.claude.com/docs/en/overview
- MyClaude: https://github.com/cexll/myclaude
- Skill Seekers: https://github.com/yusufkaraaslan/Skill_Seekers
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