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App Analyzer Optimizer

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roedyrustam
app-analyzer-optimizer

Deep application architecture audit, bottleneck detection, and perf / Audit arsitektur aplikasi mendalam, deteksi bottleneck, dan optimasi.

Overview

Publisherroedyrustam
Repositoryvibes-plug
Skill nameapp-analyzer-optimizer
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 App Analyzer Optimizer 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/app-analyzer-optimizer
  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/app-analyzer-optimizer .claude/skills/app-analyzer-optimizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable App Analyzer Optimizer 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 App Analyzer Optimizer 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 App Analyzer Optimizer 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.

App Analyzer & Optimizer (2026 Edition)

English | Bahasa Indonesia


English

Orchestration & Integration

  • Upstream & Lifecycle: zero-to-prod-orchestrator (Phase 5 Optimization & Phase 6 QA), brainstorming, session-memory-manager.
  • AST & Codebase Graph: living-codebase-ast-graph (circular dependency & module size analysis), scalability-clean-code (code smell & God class refactoring).
  • Performance & Vitals: performance-web-vitals (Core Web Vitals LCP/INP profiling), production-ready-hardener (bundle audits & pre-flight hardening).
  • Database & Backend Profiling: database-orm-expert (N+1 query & index profiling), coderabbit (automated AI code review).

Description

Deep application analysis and optimization skill. Performs architectural audits, bottleneck detection, bundle analysis, database query profiling, and AI-assisted code review across Next.js, React, Node.js, Go, Python, and Rust applications.

Trigger Conditions

  • Auditing an existing codebase for architectural issues or technical debt.
  • Detecting performance bottlenecks (slow API routes, large bundles, N+1 queries).
  • Analyzing bundle size and suggesting code splitting opportunities.
  • Reviewing code quality (complexity, duplication, dead code).
  • Using AI-assisted tools to accelerate code review and analysis.

Analysis Protocol (5 Phases)

Phase 1: Architecture Discovery
bash
# Map the entire project structure
find . -type f -name "*.ts" -o -name "*.tsx" -o -name "*.go" | head -100
# Count lines per file (find largest files)
find . -name "*.ts" -exec wc -l {} + | sort -rn | head -20
# Find circular dependencies (TypeScript/JS)
npx madge --circular --extensions ts,tsx src/
Phase 2: Bundle Analysis (Next.js / Vite)
bash
# Next.js bundle analyzer
ANALYZE=true next build

# Or install dedicated tool
npx @next/bundle-analyzer

# Vite bundle visualization
npx vite-bundle-visualizer

# Check for duplicate dependencies
npx depcheck
npx bundle-phobia-cli check package.json

Key bundle red flags:

  • Any single chunk > 500KB (uncompressed).
  • Importing entire libraries (import _ from 'lodash' vs import debounce from 'lodash/debounce').
  • Moment.js (replace with date-fns or Temporal).
  • node_modules leaking into client bundle.
Phase 3: Database Query Analysis
sql
-- PostgreSQL: Find slow queries
SELECT query, mean_exec_time, calls, total_exec_time
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 20;

-- Find missing indexes (sequential scans on large tables)
SELECT schemaname, tablename, attname, n_distinct, correlation
FROM pg_stats
WHERE tablename = 'your_table'
ORDER BY n_distinct DESC;

-- Detect N+1 queries: Enable query logging
-- In Drizzle:
const db = drizzle(client, { logger: true });
-- In Prisma:
DATABASE_URL="...?connection_limit=5" prisma studio
Phase 4: AI-Assisted Code Review

Use AI tools to accelerate analysis:

ToolPurpose
CodeRabbitAutomated PR review, pattern detection
Sourcegraph CodyCodebase-wide semantic search and explanation
GitHub CopilotInline suggestions and refactoring
Cursor / WindsurfAI IDE with full-repo context

AI review prompts for Gemini Agent:

Analyze all files in src/features/ and identify:
1. Functions longer than 30 lines
2. Duplicated business logic across files
3. Missing error handling in async functions
4. Components that directly call APIs (violating separation of concerns)
Phase 5: Performance Profiling

Frontend (React / Next.js):

bash
# React DevTools Profiler — identify render bottlenecks
# Chrome DevTools > Performance tab > Record interaction

# Lighthouse CI — automated CWV tracking
npm install -g @lhci/cli
lhci autorun

Backend (Node.js):

bash
# Built-in Node.js profiler
node --prof server.js
node --prof-process isolate-*.log > processed.txt

# Clinic.js — flamegraph, bubble chart, doctor
npm install -g clinic
clinic doctor -- node server.js
clinic flame -- node server.js

Go:

bash
# Built-in pprof profiler
go tool pprof http://localhost:6060/debug/pprof/profile?seconds=30
go tool pprof -http=:8080 profile.out

Common Bottleneck Patterns & Fixes

BottleneckSymptomFix
N+1 queriesDB calls proportional to list lengthAdd .include() / JOIN or DataLoader
Missing indexesSlow queries on filtered columnsCREATE INDEX CONCURRENTLY
Unoptimized imagesLarge LCP, slow page loadNext.js <Image>, WebP, lazy loading
Over-fetchingFetching full objects when only 1 field neededSelect specific columns
No paginationFetching entire tablesAdd LIMIT/OFFSET or cursor pagination
Blocking main threadHigh INP, unresponsive UIuseTransition, web workers
No cachingSame data fetched repeatedlyRedis, React Query staleTime, HTTP cache
Bundle bloatLarge JS payloadTree-shaking, code splitting, lazy imports

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 app-analyzer-optimizer <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

  • Hulu & Siklus Hidup: zero-to-prod-orchestrator (Optimasi Fase 5 & QA Fase 6), brainstorming, session-memory-manager.
  • AST & Graf Basis Kode: living-codebase-ast-graph (dependensi sirkular & analisis ukuran modul), scalability-clean-code (refaktor code smell & God class).
  • Performa & Web Vitals: performance-web-vitals (profiling Core Web Vitals LCP/INP), production-ready-hardener (audit bundle & pengerasan pra-rilis).
  • Profiling Database & Backend: database-orm-expert (query N+1 & profiling indeks), coderabbit (code review AI otomatis).

Deskripsi

Skill analisis dan optimasi aplikasi mendalam. Melakukan audit arsitektur, deteksi bottleneck, analisis bundle, profiling query database, dan code review berbantuan AI di aplikasi Next.js, React, Node.js, Go, Python, dan Rust.

Kondisi Pemicu

  • Mengaudit codebase yang ada untuk masalah arsitektur atau hutang teknis.
  • Mendeteksi bottleneck performa (rute API lambat, bundle besar, query N+1).
  • Menganalisis ukuran bundle dan menyarankan peluang code splitting.
  • Meninjau kualitas kode (kompleksitas, duplikasi, kode mati).
  • Menggunakan tool berbantuan AI untuk mempercepat code review dan analisis.

Protokol Analisis (5 Fase)

Fase 1: Penemuan Arsitektur

Petakan seluruh struktur proyek, hitung baris per file untuk menemukan file terbesar, dan deteksi dependensi circular dengan npx madge --circular.

Fase 2: Analisis Bundle (Next.js / Vite)

Gunakan ANALYZE=true next build atau npx vite-bundle-visualizer. Tanda bahaya bundle: chunk tunggal > 500KB, mengimpor library penuh, Moment.js, atau node_modules yang bocor ke bundle klien.

Fase 3: Analisis Query Database

Gunakan pg_stat_statements untuk menemukan query lambat. Cari scan sequential pada tabel besar (tanda indeks yang hilang). Aktifkan logging query di Drizzle/Prisma untuk mendeteksi pola N+1.

Fase 4: Code Review Berbantuan AI

Gunakan CodeRabbit untuk review PR otomatis, Sourcegraph Cody untuk pencarian semantik seluruh codebase, dan Gemini Agent dengan prompt analisis spesifik untuk menemukan fungsi panjang, logika bisnis yang diduplikasi, dan error handling yang hilang.

Fase 5: Profiling Performa
  • Frontend: React DevTools Profiler, Lighthouse CI.
  • Backend Node.js: node --prof, Clinic.js untuk flamegraph.
  • Go: go tool pprof untuk CPU dan memory profiling.

Pola Bottleneck Umum & Perbaikan

BottleneckGejalaPerbaikan
Query N+1Panggilan DB proporsional dengan panjang daftarTambahkan .include() / JOIN atau DataLoader
Indeks yang hilangQuery lambat pada kolom yang difilterCREATE INDEX CONCURRENTLY
Gambar tidak dioptimalkanLCP besar, halaman lambatNext.js <Image>, WebP, lazy loading
Over-fetchingMengambil objek penuh saat hanya 1 field dibutuhkanPilih kolom spesifik
Tanpa paginasiMengambil seluruh tabelTambahkan LIMIT/OFFSET atau cursor pagination
Memblokir thread utamaINP tinggi, UI tidak responsifuseTransition, web workers
Tanpa cachingData yang sama diambil berulang kaliRedis, React Query staleTime, HTTP cache
Bundle membengkakPayload JS besarTree-shaking, code splitting, lazy imports

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 app-analyzer-optimizer <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 App Analyzer Optimizer AI skill do?

Deep application architecture audit, bottleneck detection, and perf / Audit arsitektur aplikasi mendalam, deteksi bottleneck, dan optimasi.

Why use App Analyzer Optimizer on TypingMind?

Because you install it once and use it with any model. App Analyzer Optimizer 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 App Analyzer Optimizer in TypingMind?

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

Which AI models can use App Analyzer Optimizer?

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 App Analyzer Optimizer?

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

Is the App Analyzer Optimizer 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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