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    Feature Flag Lifecycle Auditor

    by heyhridyansh

    1

    Audits feature flag lifecycles to identify stale toggles, rollout risks, and evaluation drift.

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    See it in action

    You say

    Review these LaunchDarkly flag definitions and the React SDK wrapper for stale toggles and rollout risks, especially the 'new-checkout-flow' flag.

    Your agent does

    Lifecycle Health Score: 68/100. Verdict: Material feature-flag debt. Findings:

    • 'new-checkout-flow': High Risk. Missing data compatibility check; rollback will break pending orders.
    • 'alpha-test-user': Stale. 100% rollout for 90 days with no owner. Action: Implement cleanup plan for 4 flags.

    What you get

    Identify stale flags and completed experiments for safe removal.Detect evaluation drift between server-side and client-side SDKs.Verify kill-switch reliability and fallback behavior for critical features.Audit data compatibility to ensure safe rollbacks without breaking schemas.

    About this skill

    The problem

    Feature flags often evolve into permanent technical debt, causing server/client evaluation drift or dangerous "fail-open" behaviors during SDK outages. Managing stale experiments and ensuring data compatibility during rollbacks is difficult to track across complex codebases.

    What it does

    • Identifies stale flags, ownerless toggles, and expired experiments that are ready for cleanup.
    • Audits evaluation consistency across server, browser, and mobile environments to prevent tenant leakage.
    • Evaluates kill-switch effectiveness and fallback safety for high-risk features.
    • Maps flag dependencies and nested evaluations to prevent state explosion and circular logic.
    • Generates a prioritized cleanup plan with a 100-point lifecycle health score.

    Why this beats prompting it yourself

    General LLMs often miss the subtle data compatibility risks of a flag rollback or the difference between a release toggle and an authorization gate. This skill uses a specialized rubric to detect side effects like duplicate payments or API contract breaks that simple code reviews overlook.

    Use cases

    • Auditing a legacy flag inventory before a major infrastructure migration.
    • Reviewing a complex percentage rollout plan for cross-platform consistency.
    • Identifying dead code branches and stale experiments for a sprint cleanup task.
    • Verifying that new feature toggles have safe fallbacks and proper observability hooks.

    Known limitations

    Does not perform live production changes or alter targeting rules in your provider dashboard. Requires manual input of flag configurations, SDK wrappers, or audit logs for analysis.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Creator

    heyhridyansh
    heyhridyansh

    26 skills on Agensi

    I create practical, AI-ready skills for Cursor, Claude Code, Codex CLI, Replit, and other agents that support the SKILL.md format. My skills focus on specific business and workflow problems, including ecommerce creative audits, design quality checks, prompt and skill validation, content systems, and process automation. Each skill is built with clear inputs, structured outputs, defined permissions, safeguards, and real-world usability.

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