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
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
- Establish the baseline: what is slow, by how much, and for whom? Get a number first (timer, profiler,
EXPLAIN ANALYZE, Lighthouse,time). - 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)
- Python:
- Attribute cost: rank the top offenders by measured share of time/memory.
- Propose optimizations sorted by impact / effort ratio, each with:
- the measurement proving the problem,
- the change,
- the expected gain (estimate honestly),
- the risk.
- 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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