Diagnose a failing Ginkgo suite as an agent — always run with --json-report into a predictable temp/gitignored location, read the terminal verdict line, then use jq to extract structured failure details (name, message, file:line, panic value, captured logs). Covers the panicked-vs-failed trap, panic locations pointing…
Parameterize and generate Ginkgo specs — DescribeTable/Entry table-driven specs, Entry descriptions (string, nil, closure, EntryDescription), PEntry/FEntry and per-Entry decorators, DescribeTableSubtree, generating specs in a construction-time loop, loading fixtures in TestXxx before RunSpecs, and shared-behavior…
Systematic evidence-based debugging using runtime logs. Generates hypotheses, instruments code with NDJSON logs, guides reproduction, analyzes log evidence, and iterates until root cause is proven with cited log lines. Use when the user reports a bug, unexpected behavior, or asks to debug an issue.
Enforces Effect-TS patterns for services, errors, layers, and atoms. Use when writing code with Effect.Service, Schema.TaggedError, Layer composition, or effect-atom React components.
Portable Effect patterns for robust promise execution. Use when wrapping async operations with timeouts, retries, tagged errors, caching, concurrency, pattern matching, or tracing - all designed to resolve to a plain Promise via Effect.runPromise.
Write Java/Kotlin tests with Fixture Monkey — enumerate the cases a method can produce, pick the ones worth testing, and build each fixture pinning only the properties that force the expected outcome.
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.
Use this skill immediately after any CIGE test run completes — pass or fail — and before any repair action is taken. It classifies the outcome and names which skill, if any, is allowed to act on it. Classification is not optional and not skippable: no agent may repair a test it has not been dispatched to.
Invoked by cige-failure-classification when a run is classified as a Product Defect: execution reached the system under test, the environment is healthy, execution steps are current — but the outcome does not match Intent.
Use this skill when writing, reviewing, or refactoring AI agent test cases. It enforces the CIGE standard — a structured format that separates stable test intent from adaptive execution, enabling self-healing agentic tests.