Initial commit: KingClawArmy AI Agent Team spec v4
Pure OpenClaw architecture for 15-17 agent team covering quant research, marketing, content, and engineering. Includes org structure, role definitions, collaboration patterns, scheduling, memory architecture, Discord integration, rollout plan, and JSON schemas. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Chris
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docs/spec_v3_schemas.md
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docs/spec_v3_schemas.md
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# KingClawArmy - 第八部分：JSON Schema
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> 基於 v2 schema 更新，新增會議相關 schema
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> 所有 schema 只保留格式模板，不放假資料
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---
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## 8.1 共用欄位
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```json
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{
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"task_id": "<string>",
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"project_id": "<string>",
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"agent_id": "<string>",
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"role": "<string>",
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"timestamp": "<ISO-8601 string>",
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"status": "<pending|running|pass|revise|block|done|cancelled>",
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"input_version": "<string>"
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}
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```
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---
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## 8.2 管理層 Schema
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### Task_Spec.json（CEO/COO 產出）
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```json
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{
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"task_id": "<string>",
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"project_id": "<string>",
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"agent_id": "ceo_coo",
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"role": "ceo_coo",
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"timestamp": "<ISO-8601>",
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"status": "<pending|running|done>",
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"goal": "<string>",
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"success_criteria": ["<string>"],
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"routes": [
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{
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"agent_id": "<string>",
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"subtask": "<string>",
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"required_output": "<string>",
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"collaboration_mode": "<task_handoff|meeting>"
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}
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],
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"priority": "<low|medium|high>",
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"meeting_required": "<boolean>",
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"meeting_type": "<meeting_quant_debate|meeting_strategy_review|meeting_cross_team_sync|null>",
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"notes": ["<string>"]
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}
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```
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### Final_Decision_Packet.json（CEO/COO 產出）
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```json
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{
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"task_id": "<string>",
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"project_id": "<string>",
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"agent_id": "ceo_coo",
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"timestamp": "<ISO-8601>",
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"status": "done",
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"summary": "<string>",
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"options": [
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{
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"name": "<string>",
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"description": "<string>",
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"pros": ["<string>"],
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"cons": ["<string>"],
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"risk_level": "<low|medium|high>"
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}
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],
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"recommended_option": "<string>",
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"review_verdict": "<pass|revise|block>",
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"meeting_conclusions": ["<string>"],
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"decision_needed": ["<string>"]
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}
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```
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### Meeting_Summary.json（秘書產出）
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```json
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{
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"task_id": "<string>",
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"project_id": "<string>",
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"agent_id": "secretary",
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"timestamp": "<ISO-8601>",
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"meeting_type": "<string>",
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"topic": "<string>",
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"participants": ["<agent_id>"],
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"key_points": ["<string>"],
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"consensus": "<string>",
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"disagreements": ["<string>"],
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"action_items": [
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{
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"owner": "<agent_id>",
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"task": "<string>",
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"deadline": "<ISO-8601|null>"
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}
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],
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"unresolved": ["<string>"]
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}
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```
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### State_Diff.json（秘書產出）
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```json
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{
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"task_id": "<string>",
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"agent_id": "secretary",
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"timestamp": "<ISO-8601>",
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"added": ["<string>"],
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"changed": ["<string>"],
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"removed": ["<string>"]
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}
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```
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### Review_Report.json（審查員產出）
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```json
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{
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"task_id": "<string>",
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"agent_id": "reviewer",
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"timestamp": "<ISO-8601>",
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"reviewed_agent_id": "<string>",
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"reviewed_output_type": "<string>",
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"verdict": "<pass|revise|block>",
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"issues": [
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{
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"severity": "<low|medium|high>",
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"category": "<string>",
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"evidence": "<string>",
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"required_fix": "<string>"
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}
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],
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"revise_count": "<number>",
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"recommend_meeting": "<boolean>",
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"notes": "<string>"
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}
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```
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---
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## 8.3 量化研究 Schema
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### Finance_Research_Brief.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "finance_researcher",
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"timestamp": "<ISO-8601>",
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"sources": [
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{
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"title": "<string>",
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"url": "<string>",
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"published_at": "<ISO-8601>",
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"source_type": "<news|blog|forum|official>",
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"summary": "<string>",
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"confidence": "<number 0-1>",
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"dedupe_key": "<string>"
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}
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],
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"macro_summary": "<string>",
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"conflicts": ["<string>"],
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"takeaways": ["<string>"]
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}
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```
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### Market_Structure_Report.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "market_structure_researcher",
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"timestamp": "<ISO-8601>",
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"timeframes_used": ["<string>"],
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"structure_summary": ["<string>"],
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"liquidity_zones": ["<string>"],
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"poi_candidates": ["<string>"],
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"mss_signals": ["<string>"],
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"bias": "<bullish|bearish|neutral|unclear>",
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"confidence": "<number 0-1>",
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"notes": ["<string>"]
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}
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```
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### Bullish_Research_Report.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "bullish_researcher",
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"timestamp": "<ISO-8601>",
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"bull_case": ["<string>"],
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"supporting_evidence": ["<string>"],
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"expected_edge": ["<string>"],
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"invalidations": ["<string>"],
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"conviction_level": "<low|medium|high>"
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}
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```
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### Bearish_Research_Report.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "bearish_researcher",
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"timestamp": "<ISO-8601>",
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"bear_case": ["<string>"],
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"supporting_evidence": ["<string>"],
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"risk_warnings": ["<string>"],
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"trade_rejection_reasons": ["<string>"],
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"conviction_level": "<low|medium|high>"
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}
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```
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### Quant_Strategy_Spec.json（含風控）
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```json
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{
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"task_id": "<string>",
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"agent_id": "quant_strategist",
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"timestamp": "<ISO-8601>",
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"strategy_hypothesis": ["<string>"],
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"entry_rules": ["<string>"],
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"exit_rules": ["<string>"],
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"risk_control": {
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"position_sizing": ["<string>"],
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"stop_rules": ["<string>"],
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"take_profit_rules": ["<string>"],
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"max_drawdown": "<string>",
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"max_concurrent_positions": "<number>"
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},
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"bias_checks": ["<string>"],
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"meeting_based": "<boolean>",
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"meeting_id": "<string|null>"
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}
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```
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### Backtest_Delivery.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "quant_engineer",
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"timestamp": "<ISO-8601>",
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"implementation_type": "<pine|python|both>",
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"rules_implemented": ["<string>"],
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"data_used": ["<string>"],
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"assumptions": ["<string>"],
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"results_summary": {
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"winrate": "<number>",
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"expectancy": "<number>",
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"max_drawdown": "<number>",
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"sharpe_ratio": "<number>",
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"total_trades": "<number>",
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"period": "<string>"
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},
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"artifacts": ["<string>"],
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"bias_warnings": ["<string>"],
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"handoff_notes": ["<string>"]
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}
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```
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### Data_Analysis_Report.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "data_analyst",
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"timestamp": "<ISO-8601>",
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"report_type": "<daily|weekly|backtest_analysis|ad_hoc>",
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"kpi_summary": ["<string>"],
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"insights": ["<string>"],
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"anomalies": ["<string>"],
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"recommendations": ["<string>"]
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}
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```
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---
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## 8.4 行銷 & 內容 Schema
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### Market_Research_Brief.json / Market_Analysis_Report.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "market_researcher",
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"timestamp": "<ISO-8601>",
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"report_type": "<brief|analysis>",
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"sources": [
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{
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"title": "<string>",
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"url": "<string>",
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"summary": "<string>",
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"confidence": "<number 0-1>"
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}
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],
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"pain_points": ["<string>"],
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"competitor_patterns": ["<string>"],
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"trend_signals": ["<string>"],
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"takeaways": ["<string>"]
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}
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```
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### Brand_Strategy_Plan.json / Growth_Strategy_Plan.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "strategy_director",
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"timestamp": "<ISO-8601>",
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"plan_type": "<brand|growth>",
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"usp": ["<string>"],
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"target_audience": ["<string>"],
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"core_messages": ["<string>"],
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"campaign_direction": ["<string>"],
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"funnel_plan": ["<string>"],
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"test_plan": ["<string>"]
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}
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```
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### Ads_Performance_Report.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "ads_analyst",
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"timestamp": "<ISO-8601>",
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"performance_summary": ["<string>"],
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"diagnosis": ["<string>"],
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"optimization_suggestions": ["<string>"],
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"alerts": ["<string>"]
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}
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```
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### Copywriting_Pack.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "copywriter",
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"timestamp": "<ISO-8601>",
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"content_type": "<ad_copy|video_script|both>",
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"hooks": ["<string>"],
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"bodies": ["<string>"],
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"ctas": ["<string>"],
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"video_sections": [
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{
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"scene": "<number>",
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"message": "<string>",
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"duration_seconds": "<number>"
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}
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]
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}
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```
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### Static_Creative_Brief.json / Storyboard_Brief.json
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```json
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{
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"task_id": "<string>",
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"agent_id": "creative_director",
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"timestamp": "<ISO-8601>",
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"brief_type": "<static|storyboard>",
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"visual_direction": ["<string>"],
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"must_include": ["<string>"],
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"asset_specs": ["<string>"],
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"shots": [
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{
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"scene": "<number>",
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"visual": "<string>",
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"motion": "<string>",
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"caption": "<string>"
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}
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],
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"style_notes": ["<string>"]
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}
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```
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---
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## 8.5 新增：會議相關 Schema
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### Meeting_Request.json（發起會議）
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```json
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{
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"meeting_id": "<string>",
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"meeting_type": "<meeting_quant_debate|meeting_strategy_review|meeting_cross_team_sync|meeting_daily_premarket|custom>",
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"topic": "<string>",
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"requested_by": "<agent_id>",
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"participants": ["<agent_id>"],
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"moderator": "<agent_id>",
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"input_context": ["<schema_name>"],
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"max_rounds": "<number>",
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"stop_condition": "<string>",
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"discord_channel": "<string>"
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}
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```
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### Meeting_Conclusion.json（會議結論）
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```json
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{
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"meeting_id": "<string>",
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"meeting_type": "<string>",
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"topic": "<string>",
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"participants": ["<agent_id>"],
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"moderator": "<agent_id>",
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"total_rounds": "<number>",
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"conclusion": "<string>",
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"consensus_reached": "<boolean>",
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"key_arguments": [
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{
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"agent_id": "<string>",
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"position": "<string>",
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"key_points": ["<string>"]
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}
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],
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"action_items": [
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{
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"owner": "<agent_id>",
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"task": "<string>"
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}
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],
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"dissenting_opinions": ["<string>"],
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"next_steps": ["<string>"]
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}
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```
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