mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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1e560ab8e8
- Delete 21 old team skill directories using CSV-wave pipeline pattern (~100+ files) - Delete old team-lifecycle (v3) and team-planex-v2 - Create generic team-worker.toml and team-supervisor.toml (replacing tlv4-specific TOMLs) - Convert 19 team skills from Claude Code format (Agent/SendMessage/TaskCreate) to Codex format (spawn_agent/wait_agent/tasks.json/request_user_input) - Update team-lifecycle-v4 to use generic agent types (team_worker/team_supervisor) - Convert all coordinator role files: dispatch.md, monitor.md, role.md - Convert all worker role files: remove run_in_background, fix Bash syntax - Convert all specs/pipelines.md references - Final state: 20 team skills, 217 .md files, zero Claude Code API residuals Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
74 lines
2.9 KiB
Markdown
74 lines
2.9 KiB
Markdown
---
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role: profiler
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prefix: PROFILE
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inner_loop: false
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message_types:
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success: profile_complete
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error: error
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---
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# Performance Profiler
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Profile application performance to identify CPU, memory, I/O, network, and rendering bottlenecks. Produce quantified baseline metrics and a ranked bottleneck report.
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## Phase 2: Context & Environment Detection
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| Input | Source | Required |
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|-------|--------|----------|
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| Task description | From task subject/description | Yes |
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| Session path | Extracted from task description | Yes |
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| .msg/meta.json | <session>/.msg/meta.json | No |
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1. Extract session path and target scope from task description
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2. Detect project type by scanning for framework markers:
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| Signal File | Project Type | Profiling Focus |
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|-------------|-------------|-----------------|
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| package.json + React/Vue/Angular | Frontend | Render time, bundle size, FCP/LCP/CLS |
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| package.json + Express/Fastify/NestJS | Backend Node | CPU hotspots, memory, DB queries |
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| Cargo.toml / go.mod / pom.xml | Native/JVM Backend | CPU, memory, GC tuning |
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| Mixed framework markers | Full-stack | Split into FE + BE profiling passes |
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| CLI entry / bin/ directory | CLI Tool | Startup time, throughput, memory peak |
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| No detection | Generic | All profiling dimensions |
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3. Use ACE search or CLI tools to map performance-critical code paths within target scope
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4. Detect available profiling tools (test runners, benchmark harnesses, linting tools)
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## Phase 3: Performance Profiling
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Execute profiling based on detected project type:
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**Frontend profiling**:
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- Analyze bundle size and dependency weight via build output
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- Identify render-blocking resources and heavy components
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- Check for unnecessary re-renders, large DOM trees, unoptimized assets
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**Backend profiling**:
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- Trace hot code paths via execution analysis or instrumented runs
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- Identify slow database queries, N+1 patterns, missing indexes
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- Check memory allocation patterns and potential leaks
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**CLI / Library profiling**:
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- Measure startup time and critical path latency
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- Profile throughput under representative workloads
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- Identify memory peaks and allocation churn
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**All project types**:
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- Collect quantified baseline metrics (timing, memory, throughput)
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- Rank top 3-5 bottlenecks by severity (Critical / High / Medium)
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- Record evidence: file paths, line numbers, measured values
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## Phase 4: Report Generation
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1. Write baseline metrics to `<session>/artifacts/baseline-metrics.json`:
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- Key metric names, measured values, units, measurement method
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- Timestamp and environment details
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2. Write bottleneck report to `<session>/artifacts/bottleneck-report.md`:
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- Ranked list of bottlenecks with severity, location (file:line), measured impact
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- Evidence summary per bottleneck
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- Detected project type and profiling methods used
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3. Update `<session>/.msg/meta.json` under `profiler` namespace:
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- Read existing -> merge `{ "profiler": { project_type, bottleneck_count, top_bottleneck, scope } }` -> write back
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