Claude-Code-Workflow/.claude/skills/team-perf-opt/role-specs/profiler.md

prefix, inner_loop, subagents, message_types

prefix	inner_loop	subagents	message_types
PROFILE	false	explore	success error profile_complete error

Performance Profiler

Profile application performance to identify CPU, memory, I/O, network, and rendering bottlenecks. Produce quantified baseline metrics and a ranked bottleneck report.

Phase 2: Context & Environment Detection

Input	Source	Required
Task description	From task subject/description	Yes
Session path	Extracted from task description	Yes
shared-memory.json	/wisdom/shared-memory.json	No

1. Extract session path and target scope from task description
2. Detect project type by scanning for framework markers:

Signal File	Project Type	Profiling Focus
package.json + React/Vue/Angular	Frontend	Render time, bundle size, FCP/LCP/CLS
package.json + Express/Fastify/NestJS	Backend Node	CPU hotspots, memory, DB queries
Cargo.toml / go.mod / pom.xml	Native/JVM Backend	CPU, memory, GC tuning
Mixed framework markers	Full-stack	Split into FE + BE profiling passes
CLI entry / bin/ directory	CLI Tool	Startup time, throughput, memory peak
No detection	Generic	All profiling dimensions

3. Use explore subagent to map performance-critical code paths within target scope
4. Detect available profiling tools (test runners, benchmark harnesses, linting tools)

Phase 3: Performance Profiling

Execute profiling based on detected project type:

Frontend profiling:

• Analyze bundle size and dependency weight via build output
• Identify render-blocking resources and heavy components
• Check for unnecessary re-renders, large DOM trees, unoptimized assets

Backend profiling:

• Trace hot code paths via execution analysis or instrumented runs
• Identify slow database queries, N+1 patterns, missing indexes
• Check memory allocation patterns and potential leaks

CLI / Library profiling:

• Measure startup time and critical path latency
• Profile throughput under representative workloads
• Identify memory peaks and allocation churn

All project types:

• Collect quantified baseline metrics (timing, memory, throughput)
• Rank top 3-5 bottlenecks by severity (Critical / High / Medium)
• Record evidence: file paths, line numbers, measured values

Phase 4: Report Generation

1. Write baseline metrics to <session>/artifacts/baseline-metrics.json:

   • Key metric names, measured values, units, measurement method
   • Timestamp and environment details

2. Write bottleneck report to <session>/artifacts/bottleneck-report.md:

   • Ranked list of bottlenecks with severity, location (file:line), measured impact
   • Evidence summary per bottleneck
   • Detected project type and profiling methods used

3. Update <session>/wisdom/shared-memory.json under profiler namespace:

   • Read existing -> merge { "profiler": { project_type, bottleneck_count, top_bottleneck, scope } } -> write back