Perf Profile

> Port provenance: adapted from the pinned upstream source at 984023ddac0d5e27624f2baacde6105e45de375f under MIT; see the repository parity ledger for the exact path and blob.

Phase 1: Determine Scope

Read the argument:

• System name → focus profiling on that specific system
• full → run a comprehensive profile across all systems

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Phase 2: Load Performance Budgets

Check for existing performance targets in design docs or AGENTS.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)

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Phase 3: Analyze Codebase

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

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Phase 4: Generate Profiling Report

## Performance Profile: [System or Full]
Generated: [Date]

### Performance Budgets
| Metric | Budget | Estimated Current | Status |
|--------|--------|-------------------|--------|
| Frame time | [16.67ms] | [estimate] | [OK/WARNING/OVER] |
| Memory | [target] | [estimate] | [OK/WARNING/OVER] |
| Load time | [target] | [estimate] | [OK/WARNING/OVER] |
| Draw calls | [target] | [estimate] | [OK/WARNING/OVER] |

### Hotspots Identified
| # | Location | Issue | Estimated Impact | Fix Effort |
|---|----------|-------|------------------|------------|

### Optimization Recommendations (Priority Order)
1. **[Title]** — [Description]
   - Location: [file:line]
   - Expected gain: [estimate]
   - Risk: [Low/Med/High]
   - Approach: [How to implement]

### Quick Wins (< 1 hour each)
- [Simple optimization 1]

### Requires Investigation
- [Area that needs actual runtime profiling to confirm impact]

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

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Phase 5: Scope and Timeline Decision

Activate this phase only if any hotspot has Fix Effort rated M or L.

Present significant-effort items and ask the user to choose for each:

• A) Implement the optimization (proceed with fix now or schedule it)
• B) Reduce feature scope (run $ai-game-studio:scope-check [feature] to analyze trade-offs)
• C) Accept the performance hit and defer to Polish phase (log as known issue)
• D) Escalate to technical-director for an architectural decision (run $ai-game-studio:architecture-decision)

If multiple items are deferred to Polish (choice C), record them under ### Deferred to Polish.

This skill is read-only — no files are written. Verdict: COMPLETE — performance profile generated.

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Phase 6: Next Steps

• If bottlenecks require architectural change: run $ai-game-studio:architecture-decision.
• If scope reduction is needed: run $ai-game-studio:scope-check [feature].
• To schedule optimizations: run $ai-game-studio:sprint-plan update.

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

Codex portability

Use the search, file-editing, shell, user-input, and subagent capabilities available in the active Codex surface. Use PowerShell syntax on Windows and POSIX syntax on macOS/Linux; do not require a Unix compatibility layer on Windows. Inherit the active model and permission mode, and do not weaken approval or sandbox boundaries.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: frabcd
• Source: frabcd/codex-ai-game-studio
• License: MIT
• Homepage: https://frabcd.github.io/codex-ai-game-studio/

Install and usage instructions live in the source repository linked above.