Below you will find pages that utilize the taxonomy term “Swenyai”
April 8, 2026
SWEny E2E — Agentic Browser Tests
Version updated for https://github.com/swenyai/e2e to version v1.0.2.
This action is used across all versions by ? repositories. Action Type This is a Composite action.
Go to the GitHub Marketplace to find the latest changes.
Action Summary This GitHub Action, SWEny E2E, facilitates agent-driven end-to-end browser testing against a deployed application without requiring traditional test scripts, selectors, or fixtures. By leveraging the agent-browser tool, the action automates UI testing using natural language workflows and self-healing capabilities that adapt to dynamic UI changes. It also captures and uploads screenshots as artifacts after every run, providing a visual audit trail for debugging and analysis.
April 8, 2026
SWEny Triage — SRE Alert Investigation
Version updated for https://github.com/swenyai/triage to version v1.0.2.
This action is used across all versions by ? repositories. Action Type This is a Composite action.
Go to the GitHub Marketplace to find the latest changes.
Action Summary The SWEny Triage GitHub Action automates the detection, investigation, and management of system errors by integrating with observability tools (e.g., Datadog) and issue trackers (e.g., Linear). It identifies new alerts, analyzes their root causes, filters duplicates, creates or updates tickets, and can optionally propose fixes via pull requests. This action streamlines incident response and SRE triage workflows, reducing manual effort and improving operational efficiency.
April 7, 2026
SWEny AI
Version updated for https://github.com/swenyai/sweny to version actions/e2e/v1.0.0.
This action is used across all versions by 0 repositories. Action Type This is a Node action using Node version 24.
Go to the GitHub Marketplace to find the latest changes.
Action Summary The SWEny GitHub Action automates the creation, execution, and monitoring of AI-driven workflows using natural language descriptions. It simplifies complex tasks by converting user-provided instructions into Directed Acyclic Graphs (DAGs) with structured outputs, conditional routing, and appropriate tools integrated at each step. This action streamlines processes like security audits, dependency scans, and issue tracking, offering users an efficient way to build and run workflows directly in CI environments or via a CLI.