With the explosive growth of AI Agent technology, the concept of "Skill" is gradually evolving from simple function calls into architectural units with lifecycles, perceptual capabilities, and complex logic. Recently, Anthropic's Claude Code and Solon AI have both proposed their own Skill systems.
Although they share the same name, their design intentions, application scenarios, and implementation logic are fundamentally different.
I. Role Positioning: Production Tool vs. Business Foundation
1. Claude Code Agent Skills: Efficiency Improvement Tools for "Developers"
Claude Code's Skills are essentially Local-First Agent capability extensions. They primarily serve end-users (developers), focusing on operating system-level interactions.
Core Scenarios: File system read/write, terminal command execution, code repository search.
Interaction Model: Typically runs in the user's local development environment, possessing extremely high local privileges.
2. Solon AI Skills: An Architecture Solution for "Enterprise-Level Business"
Solon AI's Skill is an Enterprise-Ready software architecture abstraction. It defines not only "what it can do," but also "what it can do in what business environment."
Core Scenarios: Multi-tenant SaaS systems, distributed business logic, cross-process capability discovery.
Interaction Model: Supports cross-process communication (e.g., via the MCP protocol), emphasizing capability scheduling in complex server-side environments.
II. Core Differences in Technical Features
| Feature Dimensions | Claude Code Agent Skills | Solon AI Skills |
|---|---|---|
| Perception Capabilities | Static/Passive: Explicitly granted by the user or invoked on demand by the model. | Dynamic/Active: Possesses isSupported semantics, capable of context-aware activation. |
| Instruction Injection | Fixed: Skill descriptions are typically passed in as static Prompts. | Dynamic: Supports getInstruction, dynamically generating instructions based on the current tenant/role. |
| Access Control | Terminal Control: Relies on local permissions of the runtime environment. | Tri-state Routing: Supports dynamic distribution of tool lists based on business attributes (Role/Tenant). |
| Protocol Standard | Dedicated to the Claude ecosystem. | Compatible with the MCP protocol, enabling cross-process evolution through MCP. |
III. The Game of Core Architecture Design
1. Static Definition vs. Dynamic Lifecycle
Claude Code's skills are more like a tool library. When a model needs a certain ability, it looks in the library. Solon AI introduces complete lifecycle hooks (onAttach, isSupported, etc.). This means that before the model speaks, the Skill has already completed identity verification, environment preparation, and instruction warm-up.
2. System Constraints vs. Business Constraints
Claude Code's constraints mainly come from the environment (e.g., prohibiting deletion of the root directory). Solon AI's constraints come from the business context.
Example: In Solon AI, a "refund skill" is aware of the user_role in the Prompt. If it's a regular user, the skill will return false in the isSupported phase; the model won't even see the word "refund" in the context, thus completely eliminating the possibility of unauthorized calls.
IV. Summary: How Should You Choose?
If you are developing a personal assistant, command-line tool, or assisted programming agent: Claude Code's approach is more suitable. It pursues extreme efficiency in operating local resources, allowing the model to manipulate files and terminals like a human developer.
If you are building an enterprise-level agent, a multi-tenant SaaS backend, or a complex distributed system: Solon AI Skills are a better choice. It solves the three major pain points of "contextual noise," "permission vacuum," and "uncontrolled behavior" by evolving MCPs into skills with awareness.
V. Conclusion: The Future of Skills is "Awareness"
LLM's native standards only include Prompt and Tool-Call. Both Claude and Solon's exploration of Skills demonstrates an engineering consensus: models shouldn't see all tools, but only those tools that are properly constrained, at the right time, and with the right permissions.
By deeply integrating MCP with the Skill system, Solon AI effectively provides enterprise-level agents with a capability distribution network "with a security gate."
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