fix(cli): bump version to 0.5.1 (SMI-3537)

[wrsmith108]

Summary

• Bumps @skillsmith/cli from 0.4.3 to 0.5.1 to resolve npm registry version regression
• 0.5.0 was published with exact-pinned @skillsmith/core@0.4.15; subsequent squash-merges reverted local to 0.4.3
• npm disallows republishing used versions, so advancing to 0.5.1

Post-merge action

npm deprecate @skillsmith/cli@0.5.0 "Published with stale core dependency (0.4.15 exact pin). Use >=0.5.1"

Test plan

• npm run lint — zero errors
• npm run format:check — all formatted
• npm run typecheck — clean
• npm test -w @skillsmith/cli — 441 tests passed
• Pre-push security + coverage checks passed
• CI green

🤖 Generated with claude-flow

[github-actions]

Performance Benchmark Results

╔═══════════════════════════════════════════════════════════════╗
║ SMI-1537: V3 Migration Performance Benchmarks ║
╠═══════════════════════════════════════════════════════════════╣
║ Memory Operations: 40x target ║
║ Embedding Search: 150x target ║
║ Recommendation Pipeline: 4x target ║
╚═══════════════════════════════════════════════════════════════╝

Running 50 iterations with 10 warmup...

--- Memory Operations ---

--- Embedding Search ---
Indexing 10K vectors... done

--- Recommendation Pipeline ---
Initializing recommendation pipeline with 1000 skills... done

═══════════════════════════════════════════════════════════════

V3 Migration Benchmark Report

Date: 2026-03-22T00:32:46.862Z
Node.js: v22.22.1

Results

Operation	V2 Baseline	V3 Result	Speedup	Target	Status
Memory Store	200ms	0.00ms	178253x	40x	✅
Memory Get	150ms	0.00ms	267380x	40x	✅
Memory Delete	180ms	0.00ms	332717x	40x	✅
Embedding Search (10K vectors)	500ms	0.12ms	4101x	150x	✅
Recommendation Pipeline	800ms	0.12ms	6747x	4x	✅

Summary

• Total Benchmarks: 5
• Passed: 5
• Failed: 0
• All Targets Met: ✅ Yes

Notes

• V2 baselines are from pre-migration measurements (simulated for this benchmark)
• Target threshold includes 20% tolerance for environmental variance
• Memory operations use in-memory Map (real V3 uses optimized SQLite)
• Embedding search simulates HNSW algorithm efficiency (real V3 uses onnxruntime-node)