Resources
Playbooks, guides, and best practices for AI-native E2E testing.
Cursor Rules: How They Work and When a Rule Beats a Skill
Cursor rules are persistent instructions the agent loads into context, defined as .mdc files under .cursor/rules. This guide covers the four rule types, the legacy .cursorrules migration, AGENTS.md, and a decision framework for when a rule, a skill, or an MCP server is the right tool.
How to Test Code Written by GitHub Copilot
Copilot went from autocomplete to an agent that edits across files and opens pull requests. The bugs changed with it: less syntax, more integration. Here is how to add a verification layer to Copilot's output, from unit tests it already handles to browser E2E it cannot do alone.
Pre-Action Checks for AI Coding Agents: Tools and Patterns
Pre-action checks stop an AI coding agent before it runs a dangerous command or edits a protected file: permission prompts, plan review, PreToolUse hooks, sandboxes, and branch rules. This guide covers the seven mechanisms that matter, and where each one fits against post-action verification.
Shiplight vs Momentic: AI E2E Testing Platforms Compared (2026)
Both turn plain-language intent into E2E tests stored as YAML in your repo. The difference is the exit path, the run economics, and whose subscription pays for authoring. An honest head-to-head.
How to Test Code Written by Windsurf
Windsurf's Cascade agent edits across files and runs terminal commands, but it cannot see whether the UI it just changed actually works. This guide covers adding a browser layer through MCP, turning Cascade's verifications into regression tests, and running them in CI.
AI Code Security: The Failure Modes That Actually Show Up
The worry people voice is that an agent writes an exploitable bug. The failure we see more often is quieter: a refactor silently removes a check that was already there, and nothing in the summary mentions it.