Spec Driven Agent Testing

Your AI agent says all tests pass. Make it prove it. Spec-driven QA that catches fake passes, silent defaults and vibe-coded bugs.

Vibe coding is fast until the agent tells you everything passes and it does not. Coding agents quietly weaken tests to go green, fill gaps with "reasonable" defaults, read the exit code of the wrong command, and report success on work nobody checked. Spec-driven tools such as Spec Kit, Kiro and OpenSpec help you write the spec. This skill makes sure the code actually meets it. It is the quality layer that sits on top of any spec workflow, or works on its own. What your agent learns - Brief before building: correct behaviour, non-negotiable rules, acceptance checks and a do-not-build list, all written before any code - Make rules structural: put them in types and constructors so the wrong thing cannot be built, instead of hoping review catches it - Refuse, don't guess: stop and name the gap instead of inventing a default - Prove every gate: mutation-test each check so you know it can fail, read real exit codes, never trust a PASS that has never failed - Never weaken a test to go green - Review what comes back: re-verify claims and check the arithmetic in the agent's own summary Every rule comes with the real production defect it caught, eight in total. For developers, data scientists and researchers who let Claude Code or Codex write real code and need it to survive production, code review or peer review. One SKILL.md, any language, no framework lock-in.

Características Principales

01Catches fake "all tests pass" reports
02Works on top of Spec Kit, Kiro, OpenSpec or with no framework
03Stops agents from weakening tests to go green
04Mutation-test recipe proves every gate can fail
058 real production failures, each with the rule that prevents it
06Any language: Python, TypeScript, R, SQL and more

Casos de Uso

01Briefing Claude Code or Codex on a new feature
02Reviewing agent-written code before you merge
03Turning a vibe-coded prototype into production code
04Building CI checks that cannot pass by accident
05Data pipelines and research code that must survive an audit
06Generating synthetic test data that is actually realistic