AgentSkillsCN

perf-profile

结构化的性能剖析工作流。识别瓶颈，对照预算进行衡量，并按优先级排序生成优化建议。

SKILL.md
--- frontmatter
name: perf-profile
description: "Structured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings."
argument-hint: "[system-name or 'full']"
user-invocable: true
allowed-tools: Read, Glob, Grep, Bash

When this skill is invoked:

  1. •

    Determine scope from the argument:

    • •If a system name: focus profiling on that specific system
    • •If full: run a comprehensive profile across all systems
  2. •

    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)
  3. •

    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
  4. •

    Generate the profiling report:

    markdown
    ## 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 |
|---|----------|-------|------------------|------------|
| 1 | [file:line] | [description] | [High/Med/Low] | [S/M/L] |
| 2 | [file:line] | [description] | [High/Med/Low] | [S/M/L] |

### 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]
  5. •

    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