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Combining Google Agent and Claude Code platforms to extract a universal, migratable, and practical AI Agent Skill creation methodology. Master the core principles of Skill design and create cross-platform reusable AI skill modules from scratch.

Agent Skill

AI Development Guide1/15/25...About 3 minAI TechnologyAIAI AgentsLLMMCP
Agent Skills
Agent Skills

Core Concepts: What Are Agent Skills?

Agent Skills are standardized folder structures that bundle instructions, scripts, templates, and assets for specific tasks. At their core, they represent portable, programmatic knowledge.

Standard Components

  • SKILL.md: The core file containing YAML metadata (name, description) and detailed instructions in Markdown
  • scripts/: Executable scripts (Python, Bash, etc.) for handling complex logic
  • references/: Static reference materials or documentation
  • assets/: Output templates (LaTeX, HTML), images, or logos

Open Standard

A Skill you write can run seamlessly across Claude.ai, Claude Desktop, Claude Code, Claude Agent SDK, and even Codex, Gemini CLI, or any other platform that supports this standard.

Key Technical Features

Let's explore why Skills are more efficient than long prompts:

1. Progressive Disclosure

To protect the precious Context Window, Skills follow a load-on-demand principle:

StateDescription
InitialThe model only loads the Skill's name and description
TriggeredWhen user intent matches the description, the model loads the main SKILL.md content
Deep LoadOnly when necessary does the model read reference files or execute scripts

This prevents context pollution and significantly reduces hallucination risks in long conversations.

2. Deterministic Workflows

While LLMs are non-deterministic, business logic often requires consistency. With Skills, you can define strict SOPs:

  • Low vs. High Freedom: You can force the model to follow step-by-step instructions (Step 1, Step 2...) ensuring output formats (like specific CSV structures or PPT styles) remain consistent every time

Ecosystem: Skills, MCP, and Sub-agents

ComponentRoleFunction
MCPData InterfaceConnects to external data sources (BigQuery, Notion, Google Drive)
ToolsBuilding BlocksProvides foundational capabilities (Bash execution, file I/O)
SkillsDomain ExpertsGuide the model on "how to use tools" for specific tasks (e.g., brand analysis)
Sub-agentsParallel ExecutionIndependent execution units with their own context, assignable specific skills

Essential Tool: Skill Creator

The course highlights skill-creator — a "meta-skill" that uses AI to generate skills.

  • It includes Python scripts for initializing Skill structures, packaging files, and validation
  • Best Practice: Don't start from scratch with Markdown. Discuss your needs with Claude first, then let it invoke skill-creator to generate production-ready Skills

Cross-Platform Implementation

Here's how Skills perform across different environments:

PlatformUse Case
Claude.aiUpload a .zip package to enable the web version to handle specific report formats
Claude APIRemote Skill invocation via API with code_execution preview capabilities
Claude CodeAutomate development workflows (code reviews, test generation) via the .claude/skills directory
Agent SDKBuild complex applications like "Research Assistants" where a main agent coordinates Sub-agents with specialized skills (Search, Repo Analysis, Document Reading)

Developer Takeaways

  • Shift from "Writing Prompts" to "Packaging Skills": If you find yourself typing the same repetitive instructions, it's time to package them into a Skill
  • Build Your Private Skill Library: Create domain-specific skills for your business — like "Architecture Review" or "React Component Refactoring" — and migrate them quickly across projects
  • Embrace Standardization: Agent Skills mark the evolution of agent development from "prompt tinkering" to engineering governance

了解更多 Skills →
更多模板 →
DeepLearning.AI Agent Skills Course →

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