Play 101
Pester Test Development
AI-assisted PowerShell test generation with Pester 5.x — from legacy code to 90%+ coverage.
AI-assisted PowerShell test development using the Pester 5.x framework. 7-phase pipeline: AST-based discovery, testability assessment, dependency mapping, legacy code refactoring (Add-Type thin wrappers), comprehensive test generation with mocking, and coverage enforcement (>90% JaCoCo). Supports greenfield TDD, brownfield legacy code, Azure Policy testing, .NET interop, and CI/CD integration (Azure DevOps + GitHub Actions).
Architecture Pattern
Pester pipeline: AST discovery - testability assessment - dependency mapping - mock graph - legacy refactoring - test generation - coverage validation
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Pester Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 4 skills — deploy (149 lines), evaluate (173 lines), generate-tests (180 lines), tune (221 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI key input + envFile
TuneKit (AI Config)
- config/openai.json - low temperature for deterministic test generation
- config/guardrails.json - coverage >=90%, mock verification, test isolation
- evaluation/eval.py - test pass rate, coverage analysis, mock verification
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.