Claude Skill

performance-auditor

Profile and optimize applications - hot paths, N+1 queries, blocking I/O, caching, bundle size, memory, startup time. Use for /turbo, "why is it slow?", or pre-launch performance passes.

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Download navinspire-ia-navin-navin_skills_performance-auditor-e9c73a3.zip · 1 KB
Part of navinspire-ia/navin — 182 skills

Install

skills CLI npx skills add https://github.com/Navinspire-ia/navin/tree/main/navin/skills/performance-auditor
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install navinspire-ia-navin@llmmart
Git git clone https://github.com/Navinspire-ia/navin.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole navinspire-ia/navin collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Performance Auditor

Overview

Find where time and memory actually go, prove it with measurements, and propose the highest-leverage optimizations. Rule number one: measure before recommending - no cargo-cult optimization.

Hot spots by layer

Layer Typical issues
Database N+1 queries, missing indexes, SELECT *, unbounded result sets, no connection pooling
Backend blocking I/O inside async code, sync file/network calls in hot paths, quadratic loops, chatty logging
Caching recomputing pure results, missing HTTP cache headers, cache stampedes
Frontend oversized bundles, unoptimized images, render waterfalls, missing memoization, layout thrashing
Memory leaks (listeners, closures, caches without eviction), large object retention
Startup eager imports, synchronous config fetches, unbounded migrations

Workflow

  1. Establish the baseline: what is slow, by how much, and for whom? Get a number first (timer, profiler, EXPLAIN ANALYZE, Lighthouse, time).
  2. Profile with what's available:
    • Python: cProfile, py-spy, tracemalloc
    • Node: --cpu-prof, clinic, Chrome DevTools
    • SQL: EXPLAIN (ANALYZE, BUFFERS), slow query log
    • Web: Lighthouse, bundle analyzers, Web Vitals (LCP, INP, CLS)
  3. Attribute cost: rank the top offenders by measured share of time/memory.
  4. Propose optimizations sorted by impact / effort ratio, each with:
    • the measurement proving the problem,
    • the change,
    • the expected gain (estimate honestly),
    • the risk.
  5. If asked to apply fixes: change one thing at a time and re-measure after each.

Anti-patterns

  • Optimizing without a baseline measurement
  • Micro-optimizations while an N+1 query dominates
  • Adding caches without an invalidation story
  • Claiming precise speedups you did not measure
Files (navin)
  • SKILL.md 2.1 KB
    ---
    name: performance-auditor
    description: Profile and optimize applications - hot paths, N+1 queries, blocking I/O, caching, bundle size, memory, startup time. Use for /turbo, "why is it slow?", or pre-launch performance passes.
    metadata: {"navin":{"emoji":"⚡","category":"devops"}}
    ---
    
    # Performance Auditor
    
    ## Overview
    
    Find where time and memory actually go, prove it with measurements, and propose the highest-leverage optimizations. Rule number one: **measure before recommending** - no cargo-cult optimization.
    
    ## Hot spots by layer
    
    | Layer | Typical issues |
    |-------|----------------|
    | Database | N+1 queries, missing indexes, `SELECT *`, unbounded result sets, no connection pooling |
    | Backend | blocking I/O inside async code, sync file/network calls in hot paths, quadratic loops, chatty logging |
    | Caching | recomputing pure results, missing HTTP cache headers, cache stampedes |
    | Frontend | oversized bundles, unoptimized images, render waterfalls, missing memoization, layout thrashing |
    | Memory | leaks (listeners, closures, caches without eviction), large object retention |
    | Startup | eager imports, synchronous config fetches, unbounded migrations |
    
    ## Workflow
    
    1. Establish the baseline: what is slow, by how much, and for whom? Get a number first (timer, profiler, `EXPLAIN ANALYZE`, Lighthouse, `time`).
    2. Profile with what's available:
       - Python: `cProfile`, `py-spy`, `tracemalloc`
       - Node: `--cpu-prof`, `clinic`, Chrome DevTools
       - SQL: `EXPLAIN (ANALYZE, BUFFERS)`, slow query log
       - Web: Lighthouse, bundle analyzers, Web Vitals (LCP, INP, CLS)
    3. Attribute cost: rank the top offenders by measured share of time/memory.
    4. Propose optimizations sorted by **impact / effort ratio**, each with:
       - the measurement proving the problem,
       - the change,
       - the expected gain (estimate honestly),
       - the risk.
    5. If asked to apply fixes: change one thing at a time and re-measure after each.
    
    ## Anti-patterns
    
    - Optimizing without a baseline measurement
    - Micro-optimizations while an N+1 query dominates
    - Adding caches without an invalidation story
    - Claiming precise speedups you did not measure
    

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