When this skill is invoked:

Determine scope from the argument:

• If a system name: focus profiling on that specific system

• If full : run a comprehensive profile across all systems

Read performance budgets — Check for existing performance targets in design docs or CLAUDE.md:

• Target FPS (e.g., 60fps = 16.67ms frame budget)

• Memory budget (total and per-system)

• Load time targets

• Draw call budgets

• Network bandwidth limits (if multiplayer)

Analyze the codebase for common performance issues:

CPU Profiling Targets:

• _process() / Update() / Tick() functions — list all and estimate cost

• Nested loops over large collections

• String operations in hot paths

• Allocation patterns in per-frame code

• Unoptimized search/sort over game entities

• Expensive physics queries (raycasts, overlaps) every frame

Memory Profiling Targets:

• Large data structures and their growth patterns

• Texture/asset memory footprint estimates

• Object pool vs instantiate/destroy patterns

• Leaked references (objects that should be freed but aren't)

• Cache sizes and eviction policies

Rendering Targets (if applicable):

• Draw call estimates

• Overdraw from overlapping transparent objects

• Shader complexity

• Unoptimized particle systems

• Missing LODs or occlusion culling

I/O Targets:

• Save/load performance

• Asset loading patterns (sync vs async)

• Network message frequency and size

Generate the profiling report:

Performance Profile: [System or Full]

Generated: [Date]

Performance Budgets

Hotspots Identified

Optimization Recommendations (Priority Order)

1. [Title] — [Description of the optimization]
   • Location: [file:line]
   • Expected gain: [estimate]
   • Risk: [Low/Med/High]
   • Approach: [How to implement]

Quick Wins (< 1 hour each)

• [Simple optimization 1]
• [Simple optimization 2]

Requires Investigation

• [Area that needs actual runtime profiling to determine impact]

Output the report with a summary: top 3 hotspots, estimated headroom vs budget, and recommended next action.

Rules

• Never optimize without measuring first — gut feelings about performance are unreliable

• Recommendations must include estimated impact — "make it faster" is not actionable

• Profile on target hardware, not just development machines

• Distinguish between CPU-bound, GPU-bound, and I/O-bound bottlenecks

• Consider worst-case scenarios (maximum entities, lowest spec hardware, worst network conditions)

• Static analysis (this skill) identifies candidates; runtime profiling confirms