Hephaestus is a semi-structured agentic framework that enables autonomous agents to build workflows by discovering tasks dynamically. Operations teams benefit from reduced upfront planning and increased adaptability. It connects to Python-based tools and workflows, leveraging Claude agents for task discovery and execution.
git clone https://github.com/Ido-Levi/Hephaestus.githttps://ido-levi.github.io/Hephaestus/
1. Define the process you want to automate. Be specific about the tasks involved and the tools you want to use. 2. Identify the variables that may change in the future. This will help Hephaestus create a workflow that is adaptable to changes. 3. Use the prompt template to create a workflow. Be sure to include the process, the number of tasks, and the variables. 4. Review the workflow created by Hephaestus. Make sure it meets your requirements and is adaptable to changes. 5. Use the workflow to automate the process. Monitor the workflow and make adjustments as needed.
Automatically generate software from a Product Requirements Document (PRD).
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Scan and index a codebase to enhance knowledge management.
Add new features to existing applications following established patterns.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/Ido-Levi/HephaestusCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create a workflow for [PROCESS] using Hephaestus. The workflow should include [NUMBER] tasks. The tasks should be discovered dynamically and executed using Python-based tools. The workflow should be adaptable to changes in [VARIABLES].
Based on your request, I've created a dynamic workflow for the data processing process. The workflow includes 5 tasks: data collection, data cleaning, data transformation, data analysis, and data visualization. The tasks were discovered and executed using Python-based tools such as Pandas, NumPy, and Matplotlib. The workflow is adaptable to changes in data sources, data formats, and analysis requirements. The workflow is as follows: 1. Data Collection: The workflow starts with data collection from various sources such as CSV files, Excel files, and databases. The data is collected using Python-based tools such as Pandas and SQLAlchemy. 2. Data Cleaning: The collected data is then cleaned to remove any inconsistencies, duplicates, and missing values. The data cleaning process is performed using Python-based tools such as Pandas and NumPy. 3. Data Transformation: The cleaned data is then transformed to make it suitable for analysis. The data transformation process is performed using Python-based tools such as Pandas and NumPy. 4. Data Analysis: The transformed data is then analyzed to extract meaningful insights. The data analysis process is performed using Python-based tools such as Pandas, NumPy, and Scikit-learn. 5. Data Visualization: The analyzed data is then visualized to present the insights in a clear and concise manner. The data visualization process is performed using Python-based tools such as Matplotlib and Seaborn.
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