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.
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This skill provides a way to retrieve information from the AgentScope library for analysis and decision-making.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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).
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概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
日本語の概要は準備中です。原文の説明を表示しています。
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.
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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
日本語の概要は準備中です。原文の説明を表示しています。
Unified analysis pipeline for affinity-based proteomics platforms — Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer, RFU). Platform-aware QC, normalisation, differential abundance, volcano plots, heatmaps, and PCA.
日本語の概要は準備中です。原文の説明を表示しています。