feat(cli): add 'bd thanks' command to thank contributors

Adds a new command that displays a thank you page listing all human
contributors to the beads project. Features:

- Static list of contributors (compiled into binary)
- Top 20 featured contributors displayed in columns
- Additional contributors in wrapped list
- Styled output using lipgloss (colored box, sections)
- Dynamic width based on content
- JSON output support (--json flag)
- Excludes bots and AI agents by email pattern

BENCHMARKS.md

@@ -0,0 +1,140 @@
+# Beads Performance Benchmarks
+
+This document describes the performance benchmarks available in the beads project and how to use them.
+
+## Running Benchmarks
+
+### All SQLite Benchmarks
+```bash
+go test -tags=bench -bench=. -benchmem ./internal/storage/sqlite/...
+```
+
+### Specific Benchmark
+```bash
+go test -tags=bench -bench=BenchmarkGetReadyWork_Large -benchmem ./internal/storage/sqlite/...
+```
+
+### With CPU Profiling
+```bash
+go test -tags=bench -bench=BenchmarkGetReadyWork_Large -cpuprofile=cpu.prof ./internal/storage/sqlite/...
+go tool pprof -http=:8080 cpu.prof
+```
+
+## Benchmark Categories
+
+### Compaction Operations
+- **BenchmarkGetTier1Candidates** - Identify L1 compaction candidates
+- **BenchmarkGetTier2Candidates** - Identify L2 compaction candidates
+- **BenchmarkCheckEligibility** - Check if issue is eligible for compaction
+
+### Cycle Detection
+Tests on graphs with different topologies (linear chains, trees, dense graphs):
+- **BenchmarkCycleDetection_Linear_100/1000/5000** - Linear dependency chains
+- **BenchmarkCycleDetection_Tree_100/1000** - Tree-structured dependencies
+- **BenchmarkCycleDetection_Dense_100/1000** - Dense graphs
+
+### Ready Work / Filtering
+- **BenchmarkGetReadyWork_Large** - Filter unblocked issues (10K dataset)
+- **BenchmarkGetReadyWork_XLarge** - Filter unblocked issues (20K dataset)
+- **BenchmarkGetReadyWork_FromJSONL** - Ready work on JSONL-imported database
+
+### Search Operations
+- **BenchmarkSearchIssues_Large_NoFilter** - Search all open issues (10K dataset)
+- **BenchmarkSearchIssues_Large_ComplexFilter** - Search with priority/status filters (10K dataset)
+
+### CRUD Operations
+- **BenchmarkCreateIssue_Large** - Create new issue in 10K database
+- **BenchmarkUpdateIssue_Large** - Update existing issue in 10K database
+- **BenchmarkBulkCloseIssues** - Close 100 issues sequentially (NEW)
+
+### Specialized Operations
+- **BenchmarkLargeDescription** - Handling 100KB+ issue descriptions (NEW)
+- **BenchmarkSyncMerge** - Simulate sync cycle with create/update operations (NEW)
+
+## Performance Targets
+
+### Typical Results (M2 Pro)
+
+| Operation | Time | Memory | Notes |
+|-----------|------|--------|-------|
+| GetReadyWork (10K) | 30ms | 16.8MB | Filters ~200 open issues |
+| Search (10K, no filter) | 12.5ms | 6.3MB | Returns all open issues |
+| Cycle Detection (5000 linear) | 70ms | 15KB | Detects transitive deps |
+| Create Issue (10K db) | 2.5ms | 8.9KB | Insert into index |
+| Update Issue (10K db) | 18ms | 17KB | Status change |
+| **Large Description (100KB)** | **3.3ms** | **874KB** | String handling overhead |
+| **Bulk Close (100 issues)** | **1.9s** | **1.2MB** | 100 sequential writes |
+| **Sync Merge (20 ops)** | **29ms** | **198KB** | Create 10 + update 10 |
+
+## Dataset Caching
+
+Benchmark datasets are cached in `/tmp/beads-bench-cache/`:
+- `large.db` - 10,000 issues (16.6 MB)
+- `xlarge.db` - 20,000 issues (generated on demand)
+- `large-jsonl.db` - 10K issues via JSONL import
+
+Cached databases are reused across runs. To regenerate:
+```bash
+rm /tmp/beads-bench-cache/*.db
+```
+
+## Adding New Benchmarks
+
+Follow the pattern in `sqlite_bench_test.go`:
+
+```go
+// BenchmarkMyTest benchmarks a specific operation
+func BenchmarkMyTest(b *testing.B) {
+	runBenchmark(b, setupLargeBenchDB, func(store *SQLiteStorage, ctx context.Context) error {
+		// Your test code here
+		return err
+	})
+}
+```
+
+Or for custom setup:
+
+```go
+func BenchmarkMyTest(b *testing.B) {
+	store, cleanup := setupLargeBenchDB(b)
+	defer cleanup()
+	ctx := context.Background()
+
+	b.ResetTimer()
+	b.ReportAllocs()
+
+	for i := 0; i < b.N; i++ {
+		// Your test code here
+	}
+}
+```
+
+## CPU Profiling
+
+The benchmark suite automatically enables CPU profiling on the first benchmark run:
+
+```
+CPU profiling enabled: bench-cpu-2025-12-07-174417.prof
+View flamegraph: go tool pprof -http=:8080 bench-cpu-2025-12-07-174417.prof
+```
+
+This generates a flamegraph showing where time is spent across all benchmarks.
+
+## Performance Optimization Strategy
+
+1. **Identify bottleneck** - Run benchmarks to find slow operations
+2. **Profile** - Use CPU profiling to see which functions consume time
+3. **Measure** - Run baseline benchmark before optimization
+4. **Optimize** - Make targeted changes
+5. **Verify** - Re-run benchmark to measure improvement
+
+Example:
+```bash
+# Baseline
+go test -tags=bench -bench=BenchmarkGetReadyWork_Large -benchmem ./internal/storage/sqlite/...
+
+# Make changes...
+
+# Measure improvement
+go test -tags=bench -bench=BenchmarkGetReadyWork_Large -benchmem ./internal/storage/sqlite/...
+```