Bookworm Smart Assistant

v5.7 — Neural Gateway Architecture
14 Technical Architecture Diagrams | Generated 2026-03-02
Skills: 52 Agents: 10 Hooks: 17 MCP: 22+3

INDEX

1

System Overview

graph TB
graph TB
    subgraph USER["User Layer"]
        Input["User Input"]
        SlashCmd["/skill-name"]
    end
    subgraph GATEWAY["Neural Gateway v5.7"]
        direction TB
        Hook1["route-interceptor"]
        Tokenizer["Tokenize + Synonym Expand\n22 groups"]
        BM25["BM25 + TF-IDF\n3-tier keywords"]
        ContextFusion["Context Fusion\nBM25*0.6 + Session*0.2\n+ Project*0.1 + Workflow*0.1"]
        Disambig["Disambiguation\n31 JSON rules"]
        BWR["BWR Directive Inject"]
    end
    subgraph COMPLIANCE["Compliance Gate"]
        PreTool["route-compliance-gate\n(PreToolUse:Skill)"]
        PostAudit["route-auditor\n(Stop)"]
    end
    subgraph SKILLS["Skills (52)"]
        direction LR
        S_AI["AI/Data (3)"]
        S_DEV["Dev (12)"]
        S_ARCH["Arch (8)"]
        S_OPS["DevOps (4)"]
        S_SEC["Security (1)"]
        S_QA["Quality (3)"]
        S_PROD["Product (4)"]
        S_BIZ["Business (9)"]
        S_CONTENT["Content (5)"]
        S_META["Meta (3)"]
    end
    subgraph AGENTS["Agents (10)"]
        direction LR
        A_OPUS["Opus (2)\norchestrator\ncode-reviewer"]
        A_SONNET["Sonnet (8)\nresearch-analyst | full-stack-builder\nquality-gate | self-auditor\nself-healer | canvas-ui-designer\ntest-writer | pre-deploy-checker"]
    end
    subgraph MCP_LAYER["MCP Ecosystem (22+3)"]
        direction LR
        MCP_LOCAL["Local (12)"]
        MCP_CLOUD["Cloud (9)"]
        MCP_PLUGIN["Plugin (1)"]
        MCP_ONDEMAND["On-demand (3)"]
    end
    subgraph EVOLUTION["Self-Evolution"]
        direction LR
        DriftDetect["drift-detector"]
        Auditor["self-auditor\n8-dim audit"]
        Healer["self-healer\nmetadata fix"]
        EvoLog["evolution-log\n18 seq"]
    end
    Input --> Hook1
    SlashCmd -.->|"Priority 1"| SKILLS
    Hook1 --> Tokenizer --> BM25 --> ContextFusion --> Disambig --> BWR
    BWR -->|"BWR directive"| PreTool
    PreTool -->|"PASS"| SKILLS
    SKILLS --> PostAudit
    SKILLS -.->|"complex"| AGENTS
    AGENTS --> MCP_LAYER
    SKILLS --> MCP_LAYER
    PostAudit -->|"feedback"| EVOLUTION
    DriftDetect --> Auditor --> Healer --> EvoLog
    EvoLog -.->|"weight learning"| BM25
    style GATEWAY fill:#1a1a2e,color:#e0e0ff,stroke:#4a4aff
    style COMPLIANCE fill:#2d1b2e,color:#ffccff,stroke:#aa44aa
    style SKILLS fill:#1b2e1b,color:#ccffcc,stroke:#44aa44
    style AGENTS fill:#2e2e1b,color:#ffffcc,stroke:#aaaa44
    style MCP_LAYER fill:#1b2e2e,color:#ccffff,stroke:#44aaaa
    style EVOLUTION fill:#2e1b1b,color:#ffcccc,stroke:#aa4444
      
2

Route Engine Pipeline

flowchart LR
flowchart LR
    subgraph INPUT["Input"]
        Raw["Raw Input"]
        Seg["Tokenize"]
        Syn["Synonym Expand\n22 groups"]
    end
    subgraph SCORING["Scoring"]
        BM25["BM25\nk1=1.2, b=0.75"]
        TF["TF-IDF\ncore/strong/extended"]
        CTX["Context Fusion\nwindow=10, decay=0.85"]
        PRJ["Project Detect\n9 types"]
        WF["Workflow n-gram"]
    end
    subgraph DECISION["Decision"]
        Fusion["Score Fusion\nBM25x0.6+CTXx0.2\n+PRJx0.1+WFx0.1"]
        Disamb["Disambiguation\n31 rules"]
        Chain["Skill Chain\ncomposable graph"]
        Confidence{"Confidence?"}
    end
    subgraph OUTPUT["Output"]
        HIGH["HIGH gte 0.8\nDirect Route"]
        MED["MED 0.5-0.8\nRoute + Hints"]
        LOW["LOW lt 0.5\ndeveloper-expert"]
    end
    Raw --> Seg --> Syn
    Syn --> BM25
    BM25 --> TF --> Fusion
    CTX --> Fusion
    PRJ --> Fusion
    WF --> Fusion
    Fusion --> Disamb --> Chain --> Confidence
    Confidence -->|"gte 0.8"| HIGH
    Confidence -->|"0.5~0.8"| MED
    Confidence -->|"lt 0.5"| LOW
      
3

Skill Panorama (52 Skills x 10 Categories)

mindmap
mindmap
  root((Bookworm v5.7\n52 Skills))
    AI / Data 3
      ai-ml-expert
      data-analyst-expert
      data-engineer-expert
    Dev 12
      frontend-expert
      backend-builder
      mobile-expert
      miniprogram-expert
      developer-expert
      debugger-expert
      api-integration-specialist
      regex-shell-wizard
      ultimate-code-expert
      browser-automation-expert
      workflow-automation-expert
      notification-system-expert
    Architecture 8
      architect-expert
      database-tuning-expert
      cloud-native-expert
      edge-computing-expert
      performance-expert
      impact-analyst
      diagram-as-code-expert
      zero-defect-guardian
    DevOps 4
      devops-expert
      devsecops-expert
      git-operation-master
      sre-expert
    Security 1
      security-expert
    Quality 3
      tester-expert
      reviewer-expert
      project-audit-expert
    Product 4
      product-manager-expert
      designer-expert
      ux-researcher
      project-coordinator
    Business 9
      business-plan-skill
      finance-advisor
      sales-consultant
      pricing-strategist
      customer-success-expert
      growth-hacker
      investor-review-guide
      industry-research-cn
      legal-review-skill
    Content 5
      tech-writer-expert
      copywriter-expert
      email-communicator
      social-media-manager
      technical-seo-expert
    Meta 3
      genesis-engine
      prompt-optimizer
      tech-lead-mentor
      
4

Agent Orchestration (10 Agents)

graph TB
graph TB
    subgraph OPUS["Opus (2)"]
        ORC["orchestrator\nDecompose - Schedule - Verify"]
        CR["code-reviewer\nMulti-dim Review"]
    end
    subgraph SONNET_C["Sonnet Compound (3)"]
        RA["research-analyst\nExplore + Research"]
        FSB["full-stack-builder\nFront+Back+DB E2E"]
        QG["quality-gate\nPASS / BLOCKED"]
    end
    subgraph SONNET_S["Sonnet Specialist (3)"]
        CUD["canvas-ui-designer\nHi-fi UI/UX"]
        TW["test-writer\nAuto Test Gen"]
        PDC["pre-deploy-checker\nDeploy Safety"]
    end
    subgraph SONNET_M["Sonnet Meta (2)"]
        SA["self-auditor\n8-dim Audit"]
        SH["self-healer\nDrift Fix"]
    end
    ORC -->|"research"| RA
    ORC -->|"implement"| FSB
    ORC -->|"review"| CR
    ORC -->|"verify"| QG
    FSB -.->|"after impl"| TW
    TW -.->|"tests pass"| PDC
    SA -->|"audit report"| SH
    SH -.->|"after fix"| SA
    style OPUS fill:#4a1942,color:#ffccff,stroke:#aa44aa
    style SONNET_C fill:#1a3a1a,color:#ccffcc,stroke:#44aa44
    style SONNET_S fill:#1a2a3a,color:#cce0ff,stroke:#4488cc
    style SONNET_M fill:#3a2a1a,color:#ffe0cc,stroke:#cc8844
      
5

Hook Lifecycle (17 Hooks)

flowchart TB
flowchart TB
    subgraph SUBMIT["UserPromptSubmit (1)"]
        H1["route-interceptor\nNeural Gateway"]
    end
    subgraph PRE["PreToolUse (5)"]
        H2["block-sensitive-files\nWrite/Edit"]
        H3["block-dangerous-commands\nBash"]
        H4["commit-message-lint\ngit commit"]
        H5["code-quality-gate\nBash"]
        H6["route-compliance-gate\nSkill"]
    end
    subgraph POST["PostToolUse (4 main + 4 sub)"]
        H7["post-edit-dispatcher\nParallel Dispatch"]
        H7a["check-typescript"]
        H7b["check-lint"]
        H7c["suggest-tests"]
        H7d["drift-detector\n+ integrity-check"]
        H8["build-outcome-tracker"]
        H9["post-edit-quality-check"]
        H10["activity-logger"]
    end
    subgraph OTHER["SubagentStart / Stop (2)"]
        H11["subagent-route-injector"]
        H12["route-auditor\nE2E Audit"]
    end
    H1 -->|"BWR"| PRE
    PRE -->|"PASS"| POST
    H7 --> H7a & H7b & H7c & H7d
    POST --> OTHER
    H12 -.->|"feedback"| H1
    style SUBMIT fill:#2a1a3a,color:#e0ccff,stroke:#8844cc
    style PRE fill:#3a1a1a,color:#ffcccc,stroke:#cc4444
    style POST fill:#1a3a2a,color:#ccffe0,stroke:#44cc88
    style OTHER fill:#1a2a3a,color:#cce0ff,stroke:#4488cc
      
6

MCP 3-Tier Ecosystem

graph LR
graph LR
    subgraph T1["Tier 1 - Local (12)"]
        direction TB
        subgraph T1A["AI/Research"]
            DR["deep-research"]
            C7["context7"]
            ST["sequential-thinking"]
        end
        subgraph T1B["Browser"]
            PW["playwright"]
            CD["chrome-devtools"]
            BB["browserbase"]
        end
        subgraph T1C["Device"]
            MB["mobile"]
        end
        subgraph T1D["SaaS"]
            GH["github"]
            SK["slack"]
            LN["linear"]
            AT["atlassian"]
            SB["supabase"]
        end
    end
    subgraph T2["Tier 2 - Cloud (9)"]
        direction TB
        SE["sentry"]
        FG["figma"]
        NO["notion"]
        GA["gamma"]
        CA["canva"]
        VE["vercel"]
        CL["cloudinary"]
        SGW["scholar-gateway"]
        GP["graphos"]
    end
    subgraph T3["Tier 3 - Plugin + On-demand (1+3)"]
        direction TB
        FB["firebase"]
        PG["postgres"]
        RD["redis"]
        K8["kubernetes"]
    end
    T1 ---|"always on"| CORE["Claude Code\nRuntime"]
    T2 ---|"deferred tools"| CORE
    T3 ---|"manual install"| CORE
    style T1 fill:#1a2e2e,color:#ccffff,stroke:#44aaaa
    style T2 fill:#2e2e1b,color:#ffffcc,stroke:#aaaa44
    style T3 fill:#2e1b2e,color:#ffccff,stroke:#aa44aa
      
7

Self-Evolution Loop

flowchart LR
flowchart LR
    subgraph TRIGGER["Trigger"]
        Edit["File Edit"]
        Session["Session End"]
    end
    subgraph DETECT["Detection"]
        Drift["drift-detector"]
        Integrity["integrity-check\nSHA256 x 24"]
    end
    subgraph AUDIT["Audit - 10 Dimensions"]
        H1a["H1 Config 13%"]
        H2a["H2 Baseline 13%"]
        H3a["H3 Disk 10%"]
        H4a["H4 Hooks 13%"]
        H5a["H5 Skills 9%"]
        H6a["H6 Rules 9%"]
        H7a["H7 Accuracy 13%"]
        H8a["H8 Learning 10%"]
        H9a["H9 Compliance 10%"]
        H10a["H10 Hook Eff. 9%"]
    end
    subgraph HEAL["Healing"]
        SelfHeal["self-healer\nmetadata only"]
        Actions["Version Sync\nCount Fix\nRegistry Fix"]
    end
    subgraph LOG["Logging"]
        EvoLog["evolution-log\n18 seq"]
        Weights["route-weights\nlimit pm 0.5"]
        Feedback["route-feedback"]
    end
    Edit --> Drift --> Integrity
    Session --> AUDIT
    Integrity --> AUDIT
    AUDIT -->|"CRITICAL/WARN"| SelfHeal
    SelfHeal --> Actions --> EvoLog
    AUDIT --> Weights
    AUDIT --> Feedback
    Feedback -.->|"adaptive learning\n5-day half-life"| Weights
      
8

Data Flow & File Topology

graph TB
graph TB
    subgraph CONFIG["Config Layer (Source of Truth)"]
        CLAUDE["CLAUDE.md"]
        SKILL_MD["skills/*/SKILL.md x52"]
        AGENT_MD["agents/*.md x10"]
        SETTINGS["settings.json"]
    end
    subgraph COMPILED["Compiled Artifacts"]
        INDEX["skills-index.json\n52 x 2573 keywords"]
        STATS["stats-compiled.json"]
        RULES["rules-compiled.json"]
        DISAMB["disambiguation-rules.json\n31 rules"]
        SYNS["synonyms.json\n22 groups"]
        CHECKSUMS["checksums.json\n24 files SHA256"]
    end
    subgraph RUNTIME["Runtime Data"]
        RF["route-feedback.jsonl"]
        RW["route-weights.json"]
        AL["activity-log.jsonl"]
        EL["evolution-log.jsonl"]
        SM["session-memory.json"]
    end
    subgraph SCRIPTS["Script Engine (46)"]
        RA2["route-analyzer.js"]
        TF2["tfidf-engine.js"]
        SE2["synonym-expander.js"]
        CT2["context-tracker.js"]
        PD2["project-detector.js"]
        WP2["workflow-patterns.js"]
        SCR["skill-chain-recommender.js"]
        GEN["generate-skill-index.js"]
    end
    SKILL_MD -->|"extract"| GEN --> INDEX
    INDEX --> RA2
    SYNS --> SE2 --> RA2
    RA2 --> TF2
    CT2 --> RA2
    PD2 --> RA2
    WP2 --> RA2
    DISAMB --> RA2
    RA2 -->|"scores"| RF
    RF -->|"learn"| RW
    SETTINGS -->|"register"| COMPILED
    CLAUDE --> STATS
    AGENT_MD --> STATS
    style CONFIG fill:#1a1a2e,color:#e0e0ff,stroke:#4a4aff
    style COMPILED fill:#2e2e1b,color:#ffffcc,stroke:#aaaa44
    style RUNTIME fill:#1b2e1b,color:#ccffcc,stroke:#44aa44
    style SCRIPTS fill:#2e1b2e,color:#ffccff,stroke:#aa44aa
      
9

Request Lifecycle (Sequence)

sequenceDiagram
sequenceDiagram
    actor U as User
    participant HK1 as route-interceptor
    participant RE as Route Engine
    participant HK2 as compliance-gate
    participant SK as Skill
    participant HK3 as post-edit-dispatcher
    participant SUB as sub-hooks x4
    participant HK4 as activity-logger
    participant HK5 as route-auditor
    U->>HK1: Input text
    HK1->>RE: Tokenize + Synonym expand
    RE->>RE: BM25 + TF-IDF score
    RE->>RE: Context fusion (0.6+0.2+0.1+0.1)
    RE->>RE: Disambiguate (31 rules)
    RE-->>HK1: BWR directive
    HK1-->>U: additionalContext inject
    U->>HK2: Call Skill
    HK2->>HK2: Validate BWR match
    alt Compliant
        HK2-->>SK: PASS
    else Violation
        HK2-->>U: BLOCKED + suggestion
    end
    SK->>SK: Execute skill logic
    SK->>HK3: Edit/Write trigger
    par Parallel Dispatch
        HK3->>SUB: check-typescript
        HK3->>SUB: check-lint
        HK3->>SUB: suggest-tests
        HK3->>SUB: drift-detector + integrity
    end
    SK->>HK4: Activity log (JSONL)
    Note over U,HK5: Session End
    HK5->>HK5: E2E route audit
    HK5->>HK5: actualSkill feedback
    HK5-->>RE: route-feedback.jsonl
      
10

Security Defense-in-Depth

graph TB
graph TB
    subgraph L1["Layer 1 - Input Defense (PreToolUse)"]
        B1["block-sensitive-files\n.env / credentials"]
        B2["block-dangerous-commands\nrm -rf / DROP TABLE"]
        B3["commit-message-lint"]
        B4["code-quality-gate"]
        B5["route-compliance-gate\nBWR validation"]
    end
    subgraph L2["Layer 2 - Execution Defense (PostToolUse)"]
        P1["post-edit-quality-check\nAnti-pattern scan"]
        P2["integrity-check\nSHA256 x 24 files"]
        P3["drift-detector\nReal-time drift"]
        P4["build-outcome-tracker\nSuccess rate"]
    end
    subgraph L3["Layer 3 - Audit Defense (Stop + Meta)"]
        A1["route-auditor\nE2E audit + compliance"]
        A2["self-auditor\n10-dim health check"]
        A3["activity-logger\nFull JSONL trail"]
    end
    subgraph SAFEGUARD["Safety Constraints"]
        SG1["self-healer: metadata only\nno business logic"]
        SG2["Weight clamp +/-0.5\nwhitelist + holdout"]
        SG3["Checksums HMAC\ntamper-proof"]
    end
    L1 -->|"pass"| L2
    L2 -->|"pass"| L3
    L3 -.->|"CRITICAL/WARNING"| SAFEGUARD
    style L1 fill:#3a1a1a,color:#ffcccc,stroke:#cc4444
    style L2 fill:#3a2a1a,color:#ffe0cc,stroke:#cc8844
    style L3 fill:#1a2a3a,color:#cce0ff,stroke:#4488cc
    style SAFEGUARD fill:#1a3a1a,color:#ccffcc,stroke:#44aa44
      
11

Adaptive Learning Loop

flowchart TB
flowchart TB
    subgraph COLLECT["Data Collection"]
        Explicit["Explicit Feedback\nUser route corrections"]
        Implicit["Implicit Feedback\n5-min post-route usage"]
        ComplianceD["Compliance Log\ngate-pass / blocked"]
        Build["Build Results\nsuccess / fail / timeout"]
    end
    subgraph LEARN["Learning Engine"]
        Decay["Exponential Decay\nhalf-life 5 days"]
        Whitelist["Whitelist Validation\nregistered skills only"]
        Clamp["Weight Clamp\n-0.5 to +0.5"]
        Holdout["Holdout Validation\n20% reserved"]
        Snapshot["Weight Snapshot\nrollback protection"]
    end
    subgraph APPLY["Application"]
        Weights["route-weights.json\nweight delta"]
        Index["skills-index.json\n2573 keywords"]
        BM25["BM25 Scoring\nbase + learned delta"]
    end
    subgraph MONITOR["Monitoring"]
        H7M["H7 Route Accuracy 13%"]
        H8M["H8 Learning Convergence 10%"]
        Stale["Stale Detection\nexpired data alert"]
    end
    Explicit --> Decay
    Implicit --> Decay
    Decay --> Whitelist --> Clamp --> Holdout
    Holdout -->|"validated"| Weights
    Holdout -->|"failed"| Snapshot -->|"rollback"| Weights
    Weights --> BM25
    Index --> BM25
    ComplianceD --> H7M
    Build --> H8M
    H7M --> Stale
    H8M --> Stale
    style COLLECT fill:#2e1b2e,color:#ffccff,stroke:#aa44aa
    style LEARN fill:#1a2e2e,color:#ccffff,stroke:#44aaaa
    style APPLY fill:#1b2e1b,color:#ccffcc,stroke:#44aa44
    style MONITOR fill:#2e2e1b,color:#ffffcc,stroke:#aaaa44
      
12

Health Score Model (10 Dimensions)

pie + graph
pie title Health Score Weight Distribution (100%)
    "H1 Config Consistency" : 13
    "H2 Behavior Baseline" : 13
    "H3 Disk Health" : 10
    "H4 Hook Integrity" : 13
    "H5 Skill Index" : 9
    "H6 Rule Cache" : 9
    "H7 Route Accuracy" : 13
    "H8 Learning Convergence" : 10
    "H9 Route Compliance" : 10
    "H10 Hook Effectiveness" : 9
      
graph LR
    subgraph STATIC["Static Checks (54%)"]
        H1["H1 Config 13%"]
        H4["H4 Hooks 13%"]
        H5["H5 Skills 9%"]
        H6["H6 Rules 9%"]
        H10["H10 Hook Eff. 9%"]
    end
    subgraph DYNAMIC["Dynamic Analysis (36%)"]
        H2["H2 Baseline 13%\nIQR + Z-score"]
        H7["H7 Accuracy 13%"]
        H8["H8 Learning 10%"]
    end
    subgraph INFRA["Infrastructure (10%)"]
        H3["H3 Disk 10%"]
    end
    subgraph COMP["Compliance (10%)"]
        H9["H9 Compliance 10%"]
    end
    subgraph TUNE["Self-Tuning"]
        EWMA["EWMA Threshold"]
        TREND["Trend Prediction"]
        WOPT["Weight Auto-Opt"]
    end
    STATIC --> TUNE
    DYNAMIC --> TUNE
    INFRA --> TUNE
    COMP --> TUNE
    TUNE -->|"aggregate"| SCORE["Health: 0-100"]
    style STATIC fill:#1a2a3a,color:#cce0ff,stroke:#4488cc
    style DYNAMIC fill:#2e1b2e,color:#ffccff,stroke:#aa44aa
    style INFRA fill:#1a3a1a,color:#ccffcc,stroke:#44aa44
    style COMP fill:#3a2a1a,color:#ffe0cc,stroke:#cc8844
      
13

Production Deployment

graph TB
graph TB
    subgraph CLIENT["Clients"]
        EXT["Chrome Extension"]
        WEB["Web Dashboard\n:3005"]
    end
    subgraph HOST["Server "]
        subgraph DOCKER["Docker Compose"]
            API["FastAPI\n:8002 - :8000"]
            PG["PostgreSQL"]
            REDIS["Redis"]
        end
        subgraph OBSERVE["Observability"]
            PROM["Prometheus\n:9091"]
            GRAF["Grafana\n:3101"]
            JAEGER["Jaeger\n:16686 + :4317"]
        end
    end
    subgraph EXTERNAL["External"]
        XY["Xianyu WS"]
        LLM["LLM API"]
    end
    EXT -->|"WebSocket"| API
    WEB -->|"HTTP REST"| API
    API --> PG
    API --> REDIS
    API -->|"WS dual-path"| XY
    API -->|"AI reply gen"| LLM
    API -->|"OTel gRPC"| JAEGER
    PROM -->|"scrape"| API
    GRAF -->|"query"| PROM
    GRAF -->|"trace"| JAEGER
    style CLIENT fill:#2e2e1b,color:#ffffcc,stroke:#aaaa44
    style DOCKER fill:#1a2e2e,color:#ccffff,stroke:#44aaaa
    style OBSERVE fill:#1b2e1b,color:#ccffcc,stroke:#44aa44
    style EXTERNAL fill:#2e1b2e,color:#ffccff,stroke:#aa44aa
      
14

Version Timeline

timeline
timeline
    title Bookworm Evolution (v5.4 - v5.7)
    section v5.4 (Feb 23-24)
        seq 1-3 : 3-Layer Defense + Disambiguation (18 rules)
               : Learning Safety (whitelist + snapshot + holdout)
               : Keyword Gap Detection + Health Trends
    section v5.5 (Feb 26 - Mar 01)
        seq 4-13 : Dispatcher delegation pattern
                 : Disambiguation externalized (22 rules JSON)
                 : H9/H10 scoring fix
                 : 1320 tests all green
    section v5.6 (Mar 01)
        seq 14-17 : actualSkill compliance loop
                  : detectPipeline detection
                  : Framework summary detection (12)
                  : 27 disambiguation rules
    section v5.7 (Mar 01-02)
        seq 18 : MCP 3-tier ecosystem 13 to 22
               : Skills 50 to 52
               : Disambiguation 28 to 31
               : Route accuracy 98%
      

Key Metrics Summary

DimensionCountNotes
Skills5210 categories, all stable, 22 composable
Agents102 opus + 8 sonnet
Hooks1713 registered + 4 sub-hooks
MCP22+312 local + 9 cloud + 1 plugin + 3 on-demand
Scripts4645 .js + 1 .py
Disambiguation31JSON externalized rules
Synonyms22Dictionary expansion groups
Keywords2573core / strong / extended 3-tier
Health Dims10Auto-tuning weights + EWMA
Evolution18 seqv5.4 - v5.5 - v5.6 - v5.7
Route Accuracy98%With A/B experiment framework