feat: add multi-mode workflow planning skill with session management and task generation

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

@@ -0,0 +1,73 @@
+---
+prefix: PROFILE
+inner_loop: false
+subagents: [explore]
+message_types:
+  success: profile_complete
+  error: 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 | <session>/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