vibescaler

Use when the user wants their firsthand knowledge of building, deploying, or running this project with real customers, users, or developers turned into published documentation — e.g. "interview me for docs", "extract what I know", "help me document this so others can run it without me".

Audit and improve spec coverage for a given spec. Use when (1) a spec has low or 0% requirement coverage, (2) tests exist but lack @req tags, (3) code behaviors have drifted from the spec's success criteria, (4) you need to identify unspecified behaviors in the codebase. Covers the full audit loop: analyze coverage -> tag existing tests -> identify spec gaps -> propose spec updates.

You MUST use this before any creative work — creating features, building components, adding functionality, or modifying behavior. Starts from existing specs rather than scratch. Use when the user asks to build, add, change, or design anything, even if it seems simple. Covers the full loop: find governing spec -> explore intent -> design within spec constraints -> transition to planning.

Code verification and testing for the Human Evaluation Workshop. Use when (1) running tests after code changes, (2) writing new unit tests (pytest/vitest), (3) writing E2E tests with Playwright/TestScenario, (4) debugging test failures, (5) understanding what to mock in E2E tests, (6) verifying a feature implementation. Covers the full test pyramid: unit tests -> integration tests -> E2E tests.

Use when you have a spec or requirements for a multi-step task, before touching code. Creates spec-linked implementation plans with TDD steps, exact file paths, and spec coverage tracking. Use this after brainstorming, when a user says 'plan this', 'how should we implement', or when you're about to start a multi-file feature. Covers the full loop: spec review -> file mapping -> task decomposition -> TDD steps -> coverage verification.

MLflow 3 GenAI evaluation for agent development. Use when (1) writing mlflow.genai.evaluate() code, (2) creating @scorer functions, (3) building evaluation datasets from traces, (4) using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), (5) analyzing traces for latency/errors/architecture, (6) optimizing agent context/prompts/token usage, (7) debugging evaluation failures. Covers the full eval workflow: trace analysis -> dataset building -> scorer creation -> evaluation execution.