Results for “agent-testing”

Build type-safe AI agents and graph-based workflows with PydanticAI and PydanticGraph. Agent creation, function tools, capabilities, dependency injection, structured output, streaming, multi-agent patterns, testing, evals, and graph state machines. Use whenever you are building agents, tool-using LLM workflows, or graph-based state machines in Python.

Builds reliable browser automation skills through iterative experimentation, running an inner agent to browse sites and improving navigation instructions until tasks pass consistently.

Spawns GLM-5 powered teammates with thinking mode for coding, review, testing, planning, and research tasks.

Agent D4 - Measurement Instrument Developer - Scale construction and psychometric validation. Covers item development, validity evidence, and reliability testing for social science research.

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.

Guides users through creating, editing, and optimizing agent skills, including drafting, testing, evaluating, and improving skill performance.

Orchestrates parallel AI agent sessions using dmux, a tmux pane manager, with patterns for research, implementation, testing, and code review across multiple harnesses.

Sets up and runs the Weaviate MCP server, including building and testing with the provided client.

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

Design and apply QA methodology for software teams: test strategy, regression testing, CI failure triage, test automation, quality gates and metrics, risk-based testing, exploratory testing, test design techniques, AI code quality gates (independent verification, acceptance-criteria testability review for agentic Spec-Driven Development), mutation-guided test hardening and review evidence (surviving mutants, weak assertions, diff-aware mutation testing), agentic eval design (dataset test design, judge-as-system-under-test, flaky-eval discipline), QA career levels (Senior/Staff/Principal), and SDET engineering (test infrastructure, gTAA, CI/CD integration). Do not use for root-cause debugging of production incidents, security implementation or threat modeling, or evaluation framework governance and statistical analysis — route those to systematic-debugging, secure-software-engineering, and agent-evals-and-observability respectively.

Spawns GLM-5 powered teammates with native thinking mode for coding, review, testing, planning, and research tasks.

MLOps Engineer IA — Expert en déploiement de modèles ML (A/B testing, feature stores, MLflow, Kubeflow). Industrialise le ML du notebook à la production.

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.

Launches four parallel read-only sub-agents to review a git diff or file scope for regressions, security risks, performance issues, and contract gaps, then aggregates findings into a prioritized fix path.

Guides users through creating, refining, and evaluating agent skills, including drafting, testing, and optimizing descriptions for better triggering.

Automates git bisect workflows with custom test scripts and CI pipeline integration to identify regression-introducing commits.

Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings. Use when defining red-team rules of engagement, assessing prompt injection or excessive agency, testing tool and identity boundaries, evaluating memory or cross-agent attacks, scoring a campaign, or verifying remediation in an approved environment.

Set up and run mutation testing (StrykerJS / mutmut) to measure whether tests assert behavior, not just execute lines. Use when "add mutation testing", "are our tests real", "can our test suite be gamed", or after an agent bulk-generated tests. Coverage plan → plan-test-coverage. Writing tests → test-unit.

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

Runs regression evaluations comparing agent edits against human-approved golden projects, and registers new goldens after human approval.

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.

Runs a structured self-review checklist before committing substantial code changes, covering edge cases, error paths, test coverage, documentation, and code quality.

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

Builds, tests, and deploys Microsoft 365 apps and agents for Teams and Copilot using the ATK CLI, with sub-skills for project creation, local testing, cloud deployment, troubleshooting, and Slack-to-Teams migration.

Enforce organizational security policies across Kubernetes clusters and CI/CD pipelines using Open Policy Agent (OPA) and Gatekeeper, including writing Rego policies, deploying admission controllers, and testing policies locally.

Automates Chrome/Chromium via CDP with accessibility-tree snapshots and element refs, covering web pages, Electron apps, Slack, and cloud browsers.

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.

AI-driven Game Development Studio (BMAD-GDS). Routes game projects through Pre-production, Design, Architecture, Production, and Game Testing phases using 6 specialized agents. Supports Unity, Unreal Engine, Godot, and custom engines.

Use when user experience design, interface optimization, usability testing, or conversion-focused design is needed. This agent specializes in UX design and user experience optimization within the ContentForge AI ecosystem.

Restructure code while preserving behavior: confirm tests are green, refactor in small steps, keep tests green, review, and commit with a gated workflow.

Use when testing, fixing, or extending the MarketingClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.

Use when creating test scenarios for a skill, setting up a tests/ directory for a plugin or package, running skill tests against a subagent, or verifying whether a SKILL.md changes agent behavior

Build evaluation frameworks for agent systems with deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, and outcome measurement.

Generates custom Semgrep rules from natural language descriptions of vulnerability patterns, verifying syntax with semgrep --validate and testing against sample fixtures with semgrep --test.