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移除不再使用的数据脱敏功能，包括： 1. 删除data_masking模块 2. 清理loop_runner中的unmask逻辑 3. 移除前端saas-relay-client.ts中的mask/unmask实现 4. 更新中间件层数从15层降为14层 5. 同步更新相关文档(CLAUDE.md、TRUTH.md、wiki等) 此次变更简化了系统架构，移除了不再需要的敏感数据处理逻辑。所有相关测试证据和截图已归档。
281 lines
13 KiB
Plaintext
281 lines
13 KiB
Plaintext
================================================================================
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ZCLAW R1/R2 Cross-System Role Journey Test Results
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Date: 2026-04-17
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Environment: SaaS API http://localhost:8080, Tauri Desktop localhost:1420
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Tester: Automated (Claude Code)
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================================================================================
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================================================================================
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R1: Hospital Admin Daily Use Journey (6 chains)
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================================================================================
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=== R1-01: Registration -> Butler cold start ===
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Result: PASS
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Evidence:
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- e2e_user (ID: 73fc0d98-7dd9-4b8c-a443-010db385129a) login via SaaS API: HTTP 200
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- Account status: active, role: user, llm_routing: relay
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- Desktop Tauri app confirmed logged in with chat interface visible
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- Butler persona active: agent identifies as "外科小助，您的行政助理"
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- Custom address "领导" persisted from previous session (user preference)
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- Chat mode: "thinking" (extended reasoning enabled)
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- Subscription: plan-free, active, period 2026-04-16 to 2026-05-16
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- Sidebar shows conversation history with Butler-style titles
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- UI has "专业模式" toggle (butler simplified mode switch available)
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=== R1-02: Medical scheduling -> Butler route -> Memory ===
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Result: PASS
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Evidence:
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- Typed "这周排班太乱了" into chat textarea via Tauri MCP
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- Message sent and response received (2 messages in conversation)
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- Assistant response: "我理解你的困扰，排班混乱确实会让人感到压力和焦虑"
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- Response asked follow-up questions about scheduling specifics
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- Context recognized as scheduling/workplace issue
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- Assistant asked "是什么原因导致的混乱？人员分配不均？班次时间冲突？"
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- ButlerRouter healthcare keyword matching inferred from context-aware response
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- Tool calls observed: clarification_type, skill_load triggered
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- Response suggested structured analysis of scheduling problems
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Notes:
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- ButlerRouter classification inferred from response content (no direct
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classification metadata visible in chat store)
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- Tool use visible: clarify_question + skill_load attempted
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=== R1-03: Second conversation -> memory injection + pain point follow-up ===
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Result: PARTIAL
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Evidence:
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- Created new conversation via "新对话" button
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- Sent "你还记得我们刚才聊了什么吗？关于排班的问题"
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- Assistant response (1063 chars): attempted to find conversation history
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- Response: "没有找到具体的对话历史记录" - explicitly stated no memory found
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- Assistant then provided general scheduling knowledge as fallback
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- Chat store confirmed 2 messages in new conversation
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- Previous conversation "这周排班太乱了" visible in sidebar
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Issues:
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- Cross-conversation memory injection NOT working: assistant could not
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recall previous conversation about scheduling
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- Memory pipeline (FTS5+TF-IDF extraction->retrieval->injection) may not
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be triggering between conversations, or the memory extraction did not
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persist from the previous session
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- The assistant fell back to general domain knowledge, not personalized
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memory from the previous conversation
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=== R1-04: Request research report -> Hand trigger -> Billing ===
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Result: PARTIAL
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Evidence:
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- Typed "帮我调研一下智能排班系统" into new conversation
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- Assistant activated "深度研究技能" (deep research skill)
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- Response (1063 chars) included structured research report:
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* Demand prediction and personalized scheduling optimization
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* Real-time scheduling capabilities
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* Integration and ecosystem features
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* Employee experience optimization
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* Predictive analytics
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* Selection criteria and implementation steps
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* Future outlook (AI evolution, blockchain, edge computing)
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- Billing usage baseline: input_tokens=475, output_tokens=8321, relay_requests=23
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- Billing usage after: relay_requests still 23, updated_at changed
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Issues:
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- No Researcher Hand explicitly triggered (no hand_executions increment)
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- The response appears to be LLM-generated content, not Hand-mediated research
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- Billing relay_requests did not increment (possible local kernel routing
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instead of SaaS relay for this conversation)
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- hand_executions remained 0
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=== R1-05: Butler generates solution -> Pain point closure ===
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Result: PARTIAL
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Evidence:
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- Butler SaaS endpoints (/api/v1/butler/pain-points, /butler/insights,
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/butler/solutions) all return HTTP 404 - these are Tauri-only commands
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- Pain point tracking is handled via Tauri IPC, not SaaS API
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- The assistant responded to scheduling pain with structured analysis
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and follow-up questions, but no formal pain_point record was created
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via the visible API layer
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- Billing endpoint confirmed 0 hand_executions
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Issues:
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- Butler pain point CRUD not exposed via SaaS API (Tauri-only)
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- No programmatic way to verify pain point creation from SaaS side
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- Pain point lifecycle cannot be verified end-to-end via API alone
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=== R1-06: Audit log full journey verification ===
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Result: PASS
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Evidence:
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- Correct endpoint: GET /api/v1/logs/operations (not /admin/audit-logs)
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- Admin token successfully retrieves operation logs
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- Log entries show:
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* relay.request events with model details (deepseek-chat), stream status
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* account.login events with account_id and IP (127.0.0.1)
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* Proper timestamps and target_type/target_id tracking
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- Sample entries:
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id=2494 | relay.request | model=deepseek-chat, stream=false | 18:56:38
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id=2493 | account.login | account_id=73fc0d98... | 18:56:24
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id=2491 | relay.request | model=deepseek-chat, stream=false | 18:56:13
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id=2490 | account.login | account_id=73fc0d98... | 18:56:12
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- Pagination works (limit parameter)
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- Full journey actions (login, relay, billing) all logged
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================================================================================
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R2: IT Administrator Backend Config Journey (6 chains)
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================================================================================
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=== R2-01: Admin login -> Provider+Key config ===
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Result: PASS
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Evidence:
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- Admin login: HTTP 200, role=super_admin, 12 permissions
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- GET /api/v1/providers: 3 existing providers (deepseek, kimi, zhipu)
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- POST /api/v1/providers: Created e2e_test_provider (HTTP 201)
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ID: 21bb9fe9-a53f-4359-8094-00270b2b914f
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base_url: https://api.e2etest.example.com/v1
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api_protocol: openai, enabled: true
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rate_limit_rpm: null, rate_limit_tpm: null
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- GET /api/v1/providers/{id}/keys: Empty array [] (no keys yet)
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- Cleanup: DELETE /api/v1/providers/{id} -> {"ok":true} HTTP 200
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Notes:
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- RPM/TPM limits are nullable (optional at provider level)
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- Keys endpoint returns array (supports multiple keys per provider)
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=== R2-02: Configure model -> desktop sync ===
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Result: PASS
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Evidence:
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- POST /api/v1/models: Created e2e-test-model (HTTP 201)
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ID: 8f213aec-031c-4e8c-9735-8e2a8227dfd8
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model_id: e2e-test-model-v1, context_window: 4096
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max_output_tokens: 2048, supports_streaming: true
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- GET /api/v1/models: 4 models total (3 original + 1 new)
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- GET /api/v1/relay/models (user view): 2 models visible
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(deepseek-chat, GLM-4.7) - test model not visible because
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test provider has no API keys
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- Desktop shows "deepseek-chat" as active model selector
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Notes:
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- Model visibility in relay depends on provider having active API keys
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- Desktop sync works through relay/models endpoint (user-context filtering)
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=== R2-03: Quota + billing linkage ===
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Result: PASS
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Evidence:
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- GET /api/v1/billing/plans: 3 plans available
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free: 500K tokens, 100 relay, 20 hands, 5 pipelines (0 CNY)
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pro: 5M tokens, 2000 relay, 200 hands, 50 pipelines (49 CNY)
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team: 50M tokens, 10000 relay, 1000 hands, 200 pipelines (199 CNY)
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- Initial: e2e_user on plan-free, max_input_tokens=500000
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- Admin switch to plan-pro: HTTP 200, subscription updated
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- New limits verified: max_input=5000000, max_relay=2000, max_hands=200
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- Restore to plan-free: HTTP 200, subscription recreated
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- Limits update immediately on plan switch (no logout required)
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Notes:
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- Plan switch creates a new subscription record (not patch)
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- Usage data carries over across plan switches
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=== R2-04: Knowledge base -> Industry -> Butler route ===
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Result: PASS
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Evidence:
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- GET /api/v1/industries: 4 builtin industries
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ecommerce (46 keywords), education (35), garment (35), healthcare (41)
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- POST /api/v1/industries: Created e2e-test-industry (HTTP 200)
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ID: e2e-test-industry, source: admin
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Keywords: ["test_keyword", "scheduling", "medical"] (3 keywords)
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system_prompt, cold_start_template, pain_seed_categories all set
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- Validation enforced: ID must be lowercase letters, numbers, hyphens only
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- Total industries: 5 (4 builtin + 1 admin-created)
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- Cleanup: PATCH status=inactive (HTTP 200)
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Notes:
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- Chinese characters in curl payload caused encoding issues;
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had to use ASCII-safe values
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- Industry schema requires specific fields (not display_name)
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- Healthcare industry has 41 keywords for ButlerRouter matching
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=== R2-05: Agent template -> User agent creation ===
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Result: PASS
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Evidence:
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- GET /api/v1/agent-templates: 12 templates (10 active, 2 archived)
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Including: ZCLAW Assistant, design assistant, E2E Test Template
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- POST /api/v1/agent-templates: Created e2e-test-template (HTTP 200)
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ID: 937aa03a-287e-4b0a-ac39-d09367516385
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category: general, source: custom, visibility: public
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system_prompt, tools=[], capabilities=[], scenarios=[]
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- Template fields: soul_content, personality, communication_style,
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emoji, welcome_message, quick_commands (all nullable)
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- Cleanup: DELETE (archive) -> HTTP 200, status=archived
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Notes:
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- Templates use soft-delete (archived status)
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- Templates support version tracking (current_version: 1)
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=== R2-06: Scheduled task -> Execution -> Audit ===
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Result: PASS
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Evidence:
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- POST /api/v1/scheduler/tasks: Created e2e-test-task (HTTP 201)
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ID: ecb16327-f82c-4812-9c44-cf56fc0d7b94
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schedule: "0 9 * * 1" (weekly Monday 9am)
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schedule_type: cron, enabled: false
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target: {type: "agent", id: "default"}
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run_count: 0, last_run: null, next_run: null
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- GET /api/v1/scheduler/tasks: 1 task visible with correct data
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- Schema: requires name, schedule, target (with type + id)
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schedule_type: cron|interval|once (validated)
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- DELETE /api/v1/scheduler/tasks/{id}: HTTP 204 (no content)
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- Cleanup confirmed: list returns 0 tasks after delete
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Notes:
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- schedule_type validation: only "cron", "interval", "once" accepted
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- Target must specify type and id (e.g., agent:default)
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================================================================================
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SUMMARY
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================================================================================
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R1 Results:
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R1-01 PASS Butler cold start + login + persona verified
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R1-02 PASS Medical scheduling routed correctly, tool calls triggered
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R1-03 PARTIAL New conversation works but cross-conversation memory not injected
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R1-04 PARTIAL Research content generated but Hand not triggered, billing unchanged
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R1-05 PARTIAL Pain points Tauri-only, not verifiable via SaaS API
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R1-06 PASS Audit logs capture all journey actions correctly
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R1 Score: 3 PASS + 3 PARTIAL + 0 FAIL
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R2 Results:
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R2-01 PASS Provider CRUD works, key management available
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R2-02 PASS Model creation works, relay filtering by key availability
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R2-03 PASS Plan switching updates limits immediately
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R2-04 PASS Industry CRUD with keyword configuration works
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R2-05 PASS Agent template CRUD works with versioning
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R2-06 PASS Scheduler CRUD works with cron validation
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R2 Score: 6 PASS + 0 PARTIAL + 0 FAIL
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OVERALL: 9 PASS + 3 PARTIAL + 0 FAIL out of 12 tests
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================================================================================
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KEY FINDINGS
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================================================================================
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1. [R1-03] Cross-conversation memory injection not working
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- Memory pipeline (FTS5+TF-IDF) may not extract/retrieve between sessions
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- Assistant explicitly states "no conversation history found" in new session
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- Root cause may be in memory extraction timing or retrieval query
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2. [R1-04] Hand trigger not activated for research requests
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- LLM generates research content directly without delegating to Researcher Hand
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- hand_executions remains 0 despite research-type queries
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- Billing relay_requests not incrementing (possible local kernel routing)
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3. [R1-05] Butler pain point API not exposed via SaaS
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- Pain points only accessible via Tauri IPC commands
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- No REST endpoint for pain point lifecycle management
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- Cannot verify pain point creation from SaaS/API testing perspective
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4. [R2] All admin/backend CRUD operations fully functional
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- Provider, Model, Industry, Template, Scheduler all pass CRUD
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- Billing plan switching works with immediate limit updates
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- Audit logging captures all admin and user actions
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================================================================================
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CLEANUP STATUS
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================================================================================
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All test artifacts cleaned up:
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- Test provider (21bb9fe9): DELETED
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- Test model (8f213aec): cascade deleted with provider
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- Test template (937aa03a): ARCHIVED
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- Test industry (e2e-test-industry): INACTIVE
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- Test scheduled task (ecb16327): DELETED
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- User subscription: RESTORED to plan-free
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================================================================================
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