Plugins

3 plugins

Dotnet Test Migration

Skills and an orchestrator agent for migrating .NET test frameworks and platforms: MSTest and xUnit version upgrades, xUnit-to-MSTest conversion, and VSTest to Microsoft.Testing.Platform.

5 skills · plugin

@alirezarezvani

Engineering Team

32 engineering skills: architecture, frontend, backend, fullstack, QA, DevOps, security, AI/ML, data engineering, Playwright (9 sub-skills), self-improving agent, Stripe integration, TDD guide, tech stack evaluator, Google Workspace CLI, a11y audit (WCAG 2.2), Azure cloud architect, GCP cloud architect, security pen testing, Snowflake development, adversarial-reviewer, ai-security, cloud-security,

16 skills · plugin

Planning skills for the Han suite: specifying, planning, sequencing, breaking down, and stress-testing work before implementation. Home of plan-a-feature, plan-implementation, plan-a-phased-build, plan-work-items, and iterative-plan-review, plus the discussion-facilitator agent. Depends on han-communication and han-core; bundled by the han meta-plugin.

5 skills · plugin

Results for “agent-testing”

24 skills

Writes unit, smoke, and e2e tests for AI agents, covering tools, instructions, routing, citations, and guardrails.

Automates browser interactions for web testing, form filling, screenshots, and data extraction.

Code Testing Agent

Generates and writes unit tests for any programming language using a multi-agent pipeline that researches, plans, and implements tests with build and verification steps.

ai-builder-club

Prove a feature works by delegating verification to an independent sub-agent that drives the real app, then open a pull request with the proof.

Guides the creation, iterative improvement, and evaluation of agent skills, including drafting, testing, benchmarking, and optimizing descriptions.

Scaffold and develop AI agents using OpenAI Agents SDK patterns, covering agent definition, tools, guardrails, handoffs, context, RAG pipelines, streaming, API routes, testing, and debugging.

More results

Uses two independent review agents to verify output quality, requiring both to pass before shipping.

Runs a QA validation cycle followed by bugfix until all criteria pass clean, with automated verification and re-validation.

Systematically explore a web application to find bugs and UX issues, producing a structured report with screenshots, repro videos, and detailed steps for each finding.

Automatically detects project state and runs an 8-phase pipeline to plan, build, test, lint, fix bugs, and document code across multiple AI coding agents.

Resolves legacy references to the e2e-runner capability by routing to the current runtime implementation, agent, or plugin skill.

Gan Style Harness

Uses a multi-agent generator-evaluator feedback loop to build high-quality applications from a single prompt, inspired by GANs and Anthropic's harness design.

Derives a comprehensive test plan and writes unit, integration, and end-to-end test cases from user stories and acceptance criteria, flagging issues in prior artifacts and declaring UAT readiness.

Systematically explores a web application, finds issues, and produces a report with reproduction evidence for each finding.

Enforces a test-first workflow for features and bug fixes, guiding the red-green-refactor cycle with verification checklists.

Guides systematic debugging by investigating root causes before proposing fixes, with structured phases for analysis, hypothesis testing, and implementation.

155 Zero 0a870ee6

Defines quality standards and validation checklists for agent workflows, Python tools, and documentation, including automated checks and pre-commit gates.

Build production-ready AI agents with type-safe tool use, structured outputs, dependency injection, and multi-model support using PydanticAI.

Generates, reviews, fixes, and migrates Playwright end-to-end tests, with templates, agents, and integrations for TestRail and BrowserStack.

Orch Change Feature

Orchestrate altering an existing, working feature to new desired behavior by updating tests first, then changing implementation, reviewing, and committing with gated approval.

Subagent Driven Development

Execute implementation plans by dispatching a fresh subagent per task, with task review after each and a whole-branch review at the end.

Extracts a language-agnostic ghost package (spec, tests, install and verify docs) from an existing repository, preserving behavior via tests.yaml and evidence bundles.

Guides AI agents through Test-Driven Development with 42 prioritized rules covering the red-green-refactor cycle, test design, isolation, data management, assertions, organization, performance, and strategy.

Provides a formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles to define pass/fail criteria, measure reliability with pass@k metrics, and create regression test suites.