This skill provides a way to retrieve information from the AgentScope library for analysis and decision-making.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
This guide covers the essential operations for retrieving and answering questions about the AgentScope library. If you need to answer questions regarding the AgentScope library, or look up specific information, functions/classes, examples or guidance, this skill will help you achieve that.
The skill provides the following key scripts:
When being asked an AgentScope-related question, you can follow the steps below to find the relevant information:
First decide which of the three scripts to use based on the user's question.
First ask for the user's permission to clone the agentscope GitHub repository if you haven't done so:
git clone -b main https://github.com/agentscope-ai/agentscope
In this repo, the examples folder contains various examples demonstrating how to use different features of the
AgentScope library.
They are organized in a tree structure by different functionalities. You should use shell command like ls or cat to
navigate and view the examples. Avoid using find command to search for examples, as the name of the example
files may not directly relate to the functionality being searched for.
Similarly, first ensure you have cloned the agentscope GitHub repository.
The source agentscope tutorial is located in the docs/tutorials folder of the agentscope GitHub repository. It's
organized by the different sections. To search for guidance, go to the docs/tutorials folder and view the tutorial
files by shell command like ls or cat.
First, ensure you have installed the agentscope library in your environment:
pip list | grep agentscope
If not installed, ask the user for permission to install it by command:
pip install agentscope
Then, run the following script to search for specific modules, classes, or functions. It's suggested to start with
agentscope as the root module name, and then specify the submodule name you want to search for.
python view_agentscope_module.py --module agentscope
About detailed usage, please refer to the ./view_agentscope_module.py script (located in the same folder as this
SKILL.md file).
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
The English summary is being prepared. Showing the original description.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
The English summary is being prepared. Showing the original description.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
The English summary is being prepared. Showing the original description.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
The English summary is being prepared. Showing the original description.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
The English summary is being prepared. Showing the original description.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
The English summary is being prepared. Showing the original description.