Meta-skill that routes a task to the right engineering skill. Load this at the start of a session or when unsure which skill applies — it maps intent to the process that should govern the work.
Catches breaking changes between services before they ship, by testing the contract each consumer actually depends on instead of spinning up the whole system. Use when services deploy independently, when a provider API change could break a consumer, or when end-to-end integration tests are too slow and flaky to guard every service boundary.
Proves whether a change actually moves the metric, with a valid A/B test instead of a guess — a hypothesis, a primary metric, a pre-computed sample size, guardrail metrics, and an honest readout. Use when shipping a change meant to improve a number (conversion, retention, performance), running an A/B or holdout, or deciding whether a feature "worked."
Decouples deploy from release using feature flags — ship code dark, roll out gradually, kill instantly, and remove the flag once it's done. Use when releasing risky or large changes, rolling out by percentage/cohort, delivering A/B variants, or when stale flags are accumulating as debt.
Finds security risks at design time — before code exists — by mapping the system, enumerating how it could be attacked, and deciding what to do about each threat. Use when designing a feature that touches auth, data, money, or external input, when changing a trust boundary, or when a security-sensitive change needs a structured risk pass rather than a code-level review.
Designs durable, curated memory that an agent carries across sessions — what to persist, how to scope and retrieve it, and how to stop it from drifting or poisoning later runs. Use when an agent needs to remember decisions, preferences, or project facts beyond one context window, or when stale/over-generalized memory is corrupting its behavior.
Stops an AI agent from declaring "done" on work it hasn't actually finished, by separating generation from evaluation and refusing self-graded completion. Use whenever an agent reports a task complete — especially in autonomous or long-running runs — to catch early termination, self-grading bias, and gamed tests before the work is trusted or merged.
Designs and supervises an autonomous agent loop (a /goal- or /loop-style run that prompts itself until a verifiable stop) so it ships work you can trust instead of accumulating silent errors. Use when setting up an agent to run unattended, on a schedule, or in a self-prompting cycle — and to decide when NOT to loop.