Feature Flag Lifecycle Repair
Repair feature-flag lifecycle drift in a repository using Cursor.
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You say
Review our feature flags in the Next.js app and Node API. We have inconsistent fallbacks for 'new-checkout' and stale mocks in our Vitest suite. Prepare a safe repair patch.
Your agent does
1. Repair decision
Decision: FEATURE FLAG LIFECYCLE DRIFT FOUND. Found conflicting fallbacks (Server: false, Client: true) and raw string keys in middleware.
5. Drift findings
- F1: Fallback mismatch in
checkout-registry.ts. - F2: Mock drift in
checkout.test.ts.
6. Safe patch plan
- Unify fallbacks in a shared config.
- Replace raw strings with typed constants.
- Update Vitest fixtures to use variant objects.
What you get
About this skill
The problem
Feature flag behavior often drifts between registries, provider adapters, and across the stack from API to browser. Inconsistent fallbacks, orphaned keys, and duplicated string literals lead to unpredictable production behavior and risky code cleanup.
What it does
- Inspects flag definitions to identify mismatched fallbacks between server, client, and worker processes.
- Traces flag evaluation paths to find dead branches, stale aliases, and multivariate flags incorrectly treated as booleans.
- Centralizes typed flag definitions and aligns server-client evaluation contracts for consistent targeting context.
- Prepares bounded repository patches and deterministic local tests to repair lifecycle metadata and cleanup paths.
- Detects exposure analytics drift where events fire on component render instead of behavior decision boundaries.
Why this beats prompting it yourself
General prompts often suggest deleting flags without checking cross-process dependencies like queued jobs or mobile client compatibility. This skill enforces a strict lifecycle protocol, identifying "authority" sources and preserving kill switches that shouldn't be retired.
Use cases
- Aligning browser and server fallback values to prevent hydration errors during provider outages.
- Refactoring raw flag-key strings into a central, typed registry with explicit variant mapping.
- Repairing API-to-worker decision paths to ensure queued jobs respect the original flag context.
- Establishing a deterministic retirement plan for 100% rolled-out features without breaking legacy client support.
Known limitations
Does not connect to live providers like LaunchDarkly or Unleash. It cannot verify remote targeting rules, segment membership, or production telemetry.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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