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Posted to issues@flink.apache.org by "wenjin272 (via GitHub)" <gi...@apache.org> on 2026/05/12 07:29:16 UTC
Re: [D] Agent Skills Investigation [flink-agents]
GitHub user wenjin272 closed a discussion: Agent Skills Investigation
# Agent Skills Investigation
### Introduction
Agent Skills are folders of instructions, scripts, and resources that agents can discover and use to do things more accurately and efficiently.
#### example
Take the `Brave Search Skill` as an example:：[https://clawhub.ai/steipete/brave-search](https://clawhub.ai/steipete/brave-search), It includes the following files:
* `SKILL.md` describes the skill’s name, purpose, and usage.
* `search.js` and `content.js` are Node.js scripts used for web search and content extraction, respectively.
### Why Agent Skills
* **For skill authors**: Build capabilities once and deploy them across multiple agent products.
* **For compatible agents**: Support for skills lets end users give agents new capabilities out of the box.
* **For teams and enterprises**: Capture organizational knowledge in portable, version-controlled packages.
My understanding is:
* Dynamically expand knowledge and capability boundaries—similar to RAG retrieving SOPs or domain-specific knowledge.
* Increase determinism and reliability in task execution—akin to explicitly defining workflows.
* Share skills across any agent that supports the agent skill—similar to how MCP servers work.
### How skills work
Skills use progressive disclosure to manage context efficiently:
1. **Discovery**: At startup, agents load only the name and description of each available skill, just enough to know when it might be relevant.
2. **Activation**: When a task matches a skill’s description, the agent reads the full SKILL.md instructions into context.
3. **Execution**: The agent follows the instructions, optionally loading referenced files or executing bundled code as needed.
In terms of implementation in agent framework:
1. During initialization, the agent loads the `name` and `description` of all skills from a specified directory and injects them into the system prompt.
2. A "load skill" tool is provided; when the LLM decides a skill is needed, it generates a tool call to fetch the skill’s full content.
3. A shell command execution tool is provided; when the LLM determines that a script or command from the skill should be run in the shell, it generates a tool call to execute it.
#### agentscope-java implementation
We evaluated three repositories: LangChain, AgentScope, and AgentScope-Java. Among them, AgentScope-Java provides the most complete support for Agent Skills: [https://github.com/agentscope-ai/agentscope-java/issues/92](https://github.com/agentscope-ai/agentscope-java/issues/92).
It mainly includes three components:
* Implement basic skill registration, loading, and integration
* Support sandboxed execution of scripts within skills
* Improve ecosystem compatibility and maintainability
GitHub link: https://github.com/apache/flink-agents/discussions/536
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