perf-profile
SkillDev toolsStructured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the perf-profile skill
What this skill tells your AI
The instructions your AI receives, as published by striderza/opencodegamestudios in .agents/skills/perf-profile/SKILL.md and read by ahel’s review.
Phase 1: Determine Scope
Read the argument:
- System name → focus profiling on that specific system
full→ run a comprehensive profile across all systems
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)
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
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.
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
/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
/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.
Phase 6: Next Steps
- If bottlenecks require architectural change: run
/architecture-decision. - If scope reduction is needed: run
/scope-check [feature]. - To schedule optimizations: run
/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
Signals
- GitHub stars
- 85
- Forks
- 8
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
perf-profile-striderza- Source
- github.com/striderza/opencodegamestudios