Enables coding agents to scout, rank, and preflight software work before implementation, returning evidence-backed ACT, VERIFY, or SKIP decisions for issues and pull requests.
Enables AI clients to analyze software test results and defect records, producing explainable GO, CONDITIONAL_GO, or NO_GO release-readiness recommendations with deterministic risk scoring, failed-test retrieval, defect hotspot ranking, and regression test planning.
Provides tools for agents to manage a local review graph, tracking acceptance behaviors, evidence, review passes, and human waivers to decouple review convergence from shipping readiness.
Provides structured, sandboxed test and lint feedback for coding agents, returning compact typed verdicts with failure fingerprints instead of raw runner output. It enables impact-selected test execution in isolated containers, distinguishing pre-existing failures from regressions.
Provides MCP tools for AI agents to classify changes into autonomous or human review lanes, and to run shadow simulations over backlogs without shipping anything.
Safety checks an agent runs before it acts — flags malicious packages, destructive shell commands, secret leaks, and web-backend holes before execution.