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Feature Flag Analytics Expert

Community
roedyrustam
feature-flag-analytics-expert

Feature flags, progressive rollouts (PostHog), and A/B testing / Feature flag, rollout bertahap (PostHog), dan pengujian A/B.

Overview

Publisherroedyrustam
Repositoryvibes-plug
Skill namefeature-flag-analytics-expert
Stars
117
Forks
27
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by roedyrustam on GitHub. Read the source before you install it.

Installation

Install the Feature Flag Analytics Expert AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/roedyrustam/vibes-plug/tree/main/skills/feature-flag-analytics-expert
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/roedyrustam/vibes-plug.git /tmp/vibes-plug
mkdir -p .claude/skills
cp -r /tmp/vibes-plug/skills/feature-flag-analytics-expert .claude/skills/feature-flag-analytics-expert
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feature Flag Analytics Expert in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Feature Flag Analytics Expert on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Feature Flag Analytics Expert is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Feature Flag & Progressive Rollout Expert (2026 Edition)

English | Bahasa Indonesia


English

Orchestration & Integration

  • Upstream Orchestrators: Works under brainstorming and zero-to-prod-orchestrator (Phase 6 - Hardening, Canary Rollouts & Experimentation) to manage feature releases.
  • Downstream Cooperating Skills:
    • data-telemetry-expert — Bridge flag evaluation events with analytics data pipelines.
    • nextjs-app-router-expert — Implement zero-flicker server-side flag evaluation in Server Components.
    • e2e-testing-expert — Mock feature flag overrides during automated Playwright test suites.
    • zero-tech-debt-auditor — Scan and remove obsolete/deprecated flags to prevent code rot.
    • session-memory-manager — Store active flag definitions and progressive rollout percentages.

Description

Production-grade engineering guide for feature flag management, progressive rollouts (canary releases), A/B testing statistical orchestration, and dynamic server-side/client-side flag evaluation using PostHog, LaunchDarkly, and GrowthBook. Covers zero-flicker server-side bootstrapping in Next.js 15 App Router, deterministic user bucketing, conversion metric tracking, and automated flag lifecycle deprecation.

Trigger Conditions

  • Decoupling software deployment from feature release with instant operational kill-switches.
  • Phased rollouts to user cohorts (e.g., internal team -> 5% beta -> 25% canary -> 100% GA).
  • Setting up multivariate A/B/n testing experiments with statistical significance monitoring.
  • Managing server-side gating in Next.js Server Components, Fastify, or Bun backends.
  • Retiring obsolete feature flags to prevent technical debt accumulation.

Zero-Flicker Server Component Pattern (Next.js 15 / PostHog)

typescript
// src/lib/posthog-server.ts
import { PostHog } from 'posthog-node';

let posthogClient: PostHog | null = null;

export function getPostHogServer(): PostHog {
  if (!posthogClient) {
    posthogClient = new PostHog(process.env.POSTHOG_API_KEY!, {
      host: process.env.POSTHOG_HOST || 'https://us.i.posthog.com',
      flushAt: 1,
      flushInterval: 0,
    });
  }
  return posthogClient;
}

// src/app/dashboard/page.tsx (React Server Component)
import { getPostHogServer } from '@/lib/posthog-server';
import { getCurrentUser } from '@/lib/auth';
import { ModernDashboard } from '@/components/dashboard/ModernDashboard';
import { LegacyDashboard } from '@/components/dashboard/LegacyDashboard';

export default async function DashboardPage() {
  const user = await getCurrentUser();
  const posthog = getPostHogServer();

  // Evaluated on the server: zero layout shift, zero client-side flicker
  const isV2Enabled = await posthog.isFeatureEnabled('modern-dashboard-v2', user.id, {
    personProperties: { tier: user.subscriptionTier, region: user.region },
  });

  return isV2Enabled ? <ModernDashboard user={user} /> : <LegacyDashboard user={user} />;
}

Client-Side Integration & Tracking (React 19)

tsx
// src/components/checkout/CheckoutCTA.tsx
'use client';

import { useFeatureFlagVariantKey } from 'posthog-js/react';
import posthog from 'posthog-js';

export function CheckoutCTA({ orderTotal }: { orderTotal: number }) {
  const variant = useFeatureFlagVariantKey('checkout-cta-variant') || 'control';

  const handleClick = () => {
    posthog.capture('checkout_button_clicked', {
      variant,
      order_total: orderTotal,
      currency: 'USD',
    });
  };

  return (
    <button
      onClick={handleClick}
      className={`rounded-lg px-6 py-3 font-semibold transition ${
        variant === 'emerald-glow'
          ? 'bg-emerald-600 text-white hover:bg-emerald-500 shadow-sm'
          : 'bg-slate-900 text-white hover:bg-slate-800'
      }`}
    >
      {variant === 'emerald-glow' ? 'Complete Secure Checkout' : 'Pay Now'}
    </button>
  );
}

Progressive Rollout Checklist

  • Deterministic Hashing: Ensure flag evaluations use stable identifiers (distinct_id / user UUID) so users do not oscillate between variants.
  • Server-Side Fallbacks: Always provide hardcoded safe fallbacks (false or 'control') if the flag provider times out (>100ms).
  • Flag Cleanup Cadence: Add calendar reminders or GitHub issues to remove flags that reach 100% rollout within 14 days.

Sovereign Execution, Token Efficiency & Invariants

DimensionSovereign Mandate & Deterministic Contract
Frontier Model CascadeMedium / High (GPT-5.6 Sol Medium, Gemini 4 Argon, Claude Sonnet 5.5 High, Grok 4.7 High Fast)
Availability FallbackGemini 4 Argon/Pro -> Gemini 3.8 Flash; Claude Opus 5.5 -> Claude Opus 4.6 (Zero unhandled missing-model errors)
KV-Cache & Token ProtocolPin static instructions & schemas at prompt prefix (kv-cache-prefix-optimizer); pass surgical CONTEXT_MAP.md slices (<250 tokens/call) during Swarm Fan-Out
SaaT CLI & MCP DispatchCLI: vibes skill exec feature-flag-analytics-expert <audit|eval|verify|generate|blueprint> / MCP: vibes_skill_dispatch, vibes_ast_query, vibes_compile_cache, vibes_optimize_budget, vibes_verify_gate
NFR & Anti-Slop InvariantsP99 API Gateway Latency < 85ms, OpenAPI 3.1 / TypeSpec strict schema parity, RFC 9457 Problem Details error contracts, Idempotency-Key enforcement; 100% complete zero-placeholder output verified via node scripts/check-anti-slop.js --strict

Bahasa Indonesia

Integrasi Orkestrasi

  • Orkestrator Hulu: Beroperasi di bawah arahan brainstorming dan zero-to-prod-orchestrator (Fase 6 - Hardening, Rilis Canary & Eksperimentasi) untuk mengelola peluncuran fitur.
  • Skill Hilir & Kerja Sama:
    • data-telemetry-expert — Menghubungkan evaluasi fitur dengan pipeline data analitik.
    • nextjs-app-router-expert — Mengoptimalkan evaluasi server-side agar tidak terjadi pergeseran tata letak (layout shift).
    • e2e-testing-expert — Melakukan mock nilai flag pada pengujian otomatis Playwright.
    • zero-tech-debt-auditor — Menemukan dan membersihkan flag yang sudah kedaluwarsa untuk mencegah code rot.
    • session-memory-manager — Menyimpan definisi flag aktif dan persentase rollout progresif.

Deskripsi

Panduan tingkat produksi untuk manajemen feature flags, rilis bertahap (canary rollout), orkestrasi pengujian A/B berbasis statistik, dan evaluasi fitur dinamis pada sisi server maupun klien menggunakan PostHog, LaunchDarkly, dan GrowthBook. Mencakup evaluasi server-side tanpa kedip (zero-flicker) pada Next.js 15 App Router, segmentasi pengguna deterministik, pelacakan metrik konversi, dan pembersihan siklus hidup flag secara berkala.

Kondisi Pemicu

  • Memisahkan deployment kode dari aktivasi fitur dengan tombol kill-switch instan.
  • Meluncurkan fitur secara bertahap ke kelompok pengguna (misal: tim internal -> 5% beta -> 25% canary -> 100% GA).
  • Menjalankan eksperimen A/B/n testing dengan pemantauan signifikansi statistik.
  • Mengelola pengecekan fitur di sisi server pada Server Components Next.js atau API Bun/Node.js.
  • Membersihkan flag lama untuk mencegah penumpukan utang teknis (technical debt).

Pola Integrasi Server-Side Tanpa Kedip

typescript
// src/app/checkout/page.tsx
import { getPostHogServer } from '@/lib/posthog-server';
import { auth } from '@/lib/auth';

export default async function CheckoutPage() {
  const session = await auth();
  const posthog = getPostHogServer();

  // Evaluasi langsung di server sebelum HTML dikirim ke browser
  const isSnapBiEnabled = await posthog.isFeatureEnabled(
    'doku-snap-bi-direct',
    session?.user?.id || 'anonymous'
  );

  return (
    <main className="container mx-auto p-6">
      <h1 className="text-2xl font-bold">Checkout Pembayaran</h1>
      {isSnapBiEnabled ? <DirectSnapBiPayment /> : <StandardHostedCheckout />}
    </main>
  );
}

Checklist Implementasi Rollout

  • Identitas Pengguna Deterministik: Gunakan ID pengguna yang konsisten agar pengguna yang sama tidak melihat tampilan yang berubah-ubah saat me-refresh halaman.
  • Nilai Fallback Aman: Tentukan nilai default aman jika layanan pihak ketiga mengalami latensi atau gangguan jaringan.
  • Jadwal Pembersihan Flag: Hapus pengecekan flag dari kode dalam waktu 14 hari setelah fitur diluncurkan 100%.

Matriks Eksekusi Berdaulat, Efisiensi Token & Invarian

DimensiMandat Berdaulat & Kontrak Deterministik
Kaskade Model FrontierMedium / High (GPT-5.6 Sol Medium, Gemini 4 Argon, Claude Sonnet 5.5 High, Grok 4.7 High Fast)
Invarian Fallback KetersediaanGemini 4 Argon/Pro -> Gemini 3.8 Flash; Claude Opus 5.5 -> Claude Opus 4.6 (Bebas gagal saat model belum tersedia/kuota penuh)
Protokol Hemat Token & KV-CacheKunci instruksi & skema statis di prefix awal (kv-cache-prefix-optimizer); kirim potongan CONTEXT_MAP.md (<250 token/panggilan) saat Fan-Out
Eksekusi SaaT CLI & MCPCLI: vibes skill exec feature-flag-analytics-expert <audit|eval|verify|generate|blueprint> / MCP: vibes_skill_dispatch, vibes_ast_query, vibes_compile_cache, vibes_optimize_budget, vibes_verify_gate
Invarian NFR & Anti-SlopLatensi P99 API < 85ms, paritas skema ketat OpenAPI 3.1 / TypeSpec, kontrak error RFC 9457 Problem Details, penegakan Idempotency-Key; kode 100% tuntas tanpa placeholder diverifikasi melalui node scripts/check-anti-slop.js --strict

Frequently asked questions

What does the Feature Flag Analytics Expert AI skill do?

Feature flags, progressive rollouts (PostHog), and A/B testing / Feature flag, rollout bertahap (PostHog), dan pengujian A/B.

Why use Feature Flag Analytics Expert on TypingMind?

Because you install it once and use it with any model. Feature Flag Analytics Expert is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Feature Flag Analytics Expert in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/roedyrustam/vibes-plug/tree/main/skills/feature-flag-analytics-expert. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Feature Flag Analytics Expert?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Feature Flag Analytics Expert?

As many as you like. As long as a model supports skills, you can use Feature Flag Analytics Expert with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Feature Flag Analytics Expert AI skill free?

Yes. It is published on GitHub by roedyrustam under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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