447 lines
24 KiB
JSON
447 lines
24 KiB
JSON
{
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"schema_version": "1.0",
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"type": "ai_coding_knowledge_graph",
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"name": "AI 辅助编程效率知识库",
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"description": "整合当前最有效的 AI 辅助编程方法、工具、工作流与反模式。机器可解析：AI 代理在每次任务开始前加载本文件（优先按 entity.type / applies_to / id 过滤），作为协作方法论；本文件不描述业务，业务知识见 .trae/knowledge_graph.jsonl。",
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"version": "1.0.0",
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"updated_at": "2026-10-01T16:58:00",
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"project": "追光AI",
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"machine_contract": {
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"entity_required_fields": ["id", "type", "name", "summary"],
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"entity_id_format": "{type}:{kebab-case-slug}",
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"entity_types": ["tool", "method", "workflow", "principle", "anti_pattern"],
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"relation_fields": ["from", "to", "type"],
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"load_priority": ["principle", "method", "workflow", "anti_pattern", "tool"],
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"note": "所有字段值为稳定英文 key 或中文短句；AI 可仅依赖 id/type/category/relations 做推理，summary 用于人类复核。"
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},
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"bound_to_project": {
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"toolchain": ["Trae IDE", "Next.js 14 App Router", "TypeScript", "Prisma 7.8", "Vitest", "Docker"],
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"deterministic_gates": ["npx tsc --noEmit", "npx vitest run"],
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"persistent_context": [".trae/rules/*.md", ".trae/ai_coding_knowledge.json", ".trae/knowledge_graph.jsonl"],
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"auto_record_entry": "node scripts/update-knowledge.mjs",
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"note": "以上为本项目把通用方法落地的具体载体，替换其他项目时只需替换本块。"
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},
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"sources": [
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"AGENTS.md 开放标准（Agentic AI Foundation / Linux Foundation）",
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"OpenAI Codex 工程团队指南（2026）",
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"Anthropic Claude Code 最佳实践（2026）",
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"Cursor Rules / .mdc 规范（2026）",
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"Trae IDE 官方规则与 MCP 文档（2026）"
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],
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"entities": [
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{
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"id": "principle:stateless-agent",
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"type": "principle",
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"name": "Agent 无状态",
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"summary": "AI 代理每次会话从零开始，不记得上次讨论。规则文件与知识库的唯一作用是注入跨会话持久上下文。",
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"applies_to": ["理解为什么必须维护 rules + 知识库"],
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"consequence": "上下文不落盘 = 每次都重新犯错；落盘 = 迭代效率复利"
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},
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{
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"id": "principle:enforcement-pyramid",
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"type": "principle",
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"name": "执行金字塔（Advisory → Gate → Hook）",
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"summary": "规则文件只是建议层。关键标准必须逐级下沉：rules（建议）→ CI/测试门禁（强制）→ hooks（确定性）。",
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"applies_to": ["关键规范落地", "防止规则被忽略"],
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"levels": ["advisory: .trae/rules/*.md", "gate: npm test / tsc --noEmit / code review", "hook: git pre-commit / post-commit / CI"]
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},
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{
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"id": "principle:context-budget",
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"type": "principle",
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"name": "上下文是预算",
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"summary": "只注入与本任务相关的文件与最近变更；大仓库用分层索引，避免一次性灌入冗余历史。",
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"applies_to": ["大代码库协作", "降低 token 成本与跑偏率"],
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"levels": ["L0 索引: rules/README.md + 本文件 id 列表", "L1 规范: ai_collaboration.md / ai_agent_rules.md", "L2 项目知识: knowledge_graph.jsonl", "L3 细节: 具体源码文件"]
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},
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{
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"id": "principle:single-source-of-truth",
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"type": "principle",
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"name": "单一事实源",
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"summary": "同一份知识只允许一个权威载体，其他位置派生或引用；否则必然漂移。",
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"applies_to": ["规则与知识库治理"],
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"antipattern_ref": "antipattern:divergent-copies"
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},
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{
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"id": "principle:observability-first",
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"type": "principle",
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"name": "可观测优先",
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"summary": "AI 生成的代码默认只优化「能跑」而非「可排查」。必须显式要求日志/指标/错误上下文，否则凌晨排障无窗口。",
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"applies_to": ["服务端代码", "定时任务脚本"],
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"project_binding": "scripts/lib/logger.mjs + TaskLog 表"
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},
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{
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"id": "method:task-decomposition",
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"type": "method",
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"name": "任务拆解",
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"summary": "把大任务拆成 Epic→Story→Task→Step，每步有明确输入/输出契约，避免黑盒式长推理。",
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"how": [
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"窄范围 > 大而模糊：『修 /api/tools 分页 total 字段』优于『改进工具模块』",
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"每步定义输入（依赖文件/数据）与输出（验收标准）",
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"状态机驱动：Pending → Running → Blocked → Done / Failed"
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],
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"evidence": "Agent 在大而模糊的任务上跑偏率显著更高；窄范围单目标任务成功率最高",
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"project_binding": "TodoWrite 工具 + 本文件 workflow:plan-first"
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},
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{
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"id": "method:context-engineering",
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"type": "method",
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"name": "上下文工程",
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"summary": "为 AI 提供强结构化的项目上下文（架构/规范/示例/反例），防止它用通用默认猜测。",
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"how": [
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"持久上下文写入 rules 文件，而非每次对话重复交代",
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"文档化架构决策、命名约定、错误处理规则、禁止事项",
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"措辞精确可验证，给出正例与反例",
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"项目专属事实写进知识图谱，方法层写进本文件"
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],
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"evidence": "缺乏上下文时 AI 易混用 API、忽略错误处理、用过时库",
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"project_binding": [
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".trae/rules/project_rules.md（工程标准）",
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".trae/rules/ai_collaboration.md（协作流程）",
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".trae/ai_coding_knowledge.json（本文件，方法层）",
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".trae/knowledge_graph.jsonl（事实层）"
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]
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},
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{
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"id": "method:acceptance-criteria",
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"type": "method",
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"name": "验收标准",
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"summary": "每个任务开始前必须定义可验证的验收标准，否则 AI 无法可靠收敛。",
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"how": [
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"例：『npx tsc --noEmit 零错误 + npx vitest run 全绿 + 无 N+1』",
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"verification 与 implementation 一样是交付物的一部分"
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],
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"applies_to": ["所有 AI 代理任务"],
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"antipattern_ref": "antipattern:no-acceptance"
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},
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{
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"id": "method:plan-first",
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"type": "method",
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"name": "Plan-first 工作流",
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"summary": "先规划后执行：Explore → Plan → Implement → Verify 四阶段，提升一致性与可预测性。",
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"how": [
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"Explore：先读代码/规格/知识图谱，理解现状",
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"Plan：输出结构化计划（步骤 + 依赖 + 验收标准），复杂任务先给计划再动手",
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"Implement：按计划分步实现，一步一验证",
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"Verify：跑门禁；失败回到 Implement"
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],
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"applies_to": ["复杂功能开发", "重构", "跨模块改动"],
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"workflow_ref": "workflow:plan-first-loop"
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},
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{
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"id": "method:review-diff",
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"type": "method",
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"name": "看 diff 不看对话",
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"summary": "审查 AI 产出必须看实际 git diff，而不是它自己的解释。",
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"how": ["用 git diff / IDE diff 视图逐行审查", "AI 的自我描述可信度低于实际改动", "对照验收标准核验，而非对照 AI 的说法"],
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"evidence": "AI 可能误报自己做了什么；diff 才是真相",
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"antipattern_ref": "antipattern:skip-review"
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},
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{
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"id": "method:model-tiering",
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"type": "method",
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"name": "模型成本分级",
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"summary": "高难步骤用强模型，常规步骤用轻量模型，控制时延与成本。",
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"how": [
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"补全/格式/单文件改动 → 轻量模型",
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"架构决策/疑难根因/跨模块重构 → 强模型",
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"任务开始先用轻量，按需升级"
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],
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"evidence": "分级可省 40-70% 成本而不明显降低质量"
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},
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{
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"id": "method:logging-standard",
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"type": "method",
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"name": "日志标准（最高 ROI 规则）",
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"summary": "强制 AI 产出可观测日志，因为 LLM 天然少打日志。",
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"how": ["规则层强制日志格式，禁止 print('done') 式输出", "记录关键状态、失败原因、输入输出摘要", "脚本统一走 scripts/lib/logger.mjs，结果写 TaskLog"],
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"applies_to": ["服务端代码", "定时任务脚本"],
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"project_binding": "workspace 规则 §8.7 日志规范"
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},
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{
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"id": "method:failure-classification",
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"type": "method",
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"name": "失败分型",
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"summary": "把 AI 失败分为规划失败/执行失败/质量失败，分型后再优化，避免盲目调 prompt。",
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"how": [
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"规划失败 → 重新拆解任务",
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"执行失败 → 修工具/环境/依赖",
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"质量失败 → 收紧验收标准或门禁"
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],
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"applies_to": ["AI 任务复盘"],
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"evidence": "分型后针对优化，比反复调 prompt 有效得多"
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},
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{
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"id": "method:spec-driven",
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"type": "method",
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"name": "规格驱动开发",
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"summary": "先写规格（输入/输出/边界/验收），再让 AI 实现；规格是 AI 与人的共同契约。",
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"how": ["复杂需求先落到 .trae/specs/ 或设计文档", "规格含验收标准与不变量", "实现阶段只对照规格，不临场改需求"],
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"applies_to": ["新模块", "接口变更", "结构重构"]
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},
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{
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"id": "method:test-first-gate",
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"type": "method",
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"name": "测试先行 + 门禁",
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"summary": "先写失败测试再实现，让 AI 有确定的收敛目标；门禁命令必须可一键复现。",
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"how": ["先补一条会失败的用例", "实现到用例转绿", "提交前跑 tsc + vitest 双门禁"],
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"project_binding": "Vitest；门禁：npx tsc --noEmit && npx vitest run"
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},
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{
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"id": "method:incremental-refactor",
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"type": "method",
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"name": "小步重构",
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"summary": "行为不变前提下小步改动，每步可回滚，禁止一次性大重构。",
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"how": ["一次只改一个关注点", "每步跑门禁保持全绿", "大重构拆成可独立验证的若干步"]
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},
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{
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"id": "workflow:plan-first-loop",
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"type": "workflow",
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"name": "Plan-first 主循环",
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"summary": "本项目标准工作流：加载上下文 → 探索 → 规划 → 分步实现 → 验证 → 自动记录。",
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"stages": ["load_context", "explore", "plan", "implement", "verify", "auto_record"],
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"how": [
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"load_context：读 rules/README.md → 本文件 → knowledge_graph.jsonl（按需）",
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"verify：npx tsc --noEmit + npx vitest run，失败回 implement",
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"auto_record：node scripts/update-knowledge.mjs（见 workflow:auto-record）"
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],
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"evidence": "可复用的 plan-first 流程让产出可预测、可验证"
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},
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{
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"id": "workflow:edit-test-loop",
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"type": "workflow",
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"name": "Edit-Test Loop（编辑-测试循环）",
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"summary": "让 AI 自主形成『写→测→读报错→修→重跑』闭环，是最核心的提效模式。",
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"stages": ["写代码", "跑测试", "读报错", "修复", "重跑直到通过"],
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"how": [
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"给一条完整指令：实现 + 补测试 + 测试通过再报告",
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"允许 AI 自动运行安全测试命令",
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"通常 2-5 轮收敛到全绿"
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],
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"evidence": "让 AI 自闭环比人工逐步确认 diff 效率高数倍",
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"tool_fit": ["tool:trae", "tool:claude-code"]
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},
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{
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"id": "workflow:subagent-fanout",
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"type": "workflow",
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"name": "子代理并行扇出",
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"summary": "把互相独立的任务拆成多个子代理并行执行，主代理只做编排与合并，保护主上下文。",
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"stages": ["识别独立任务", "并行派发子代理", "汇总结果", "主代理复核与合并"],
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"how": [
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"仅对无共享状态的独立任务并行（如同时核查 3 个仓库/3 个目录）",
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"探索型任务交给搜索代理，避免污染主上下文",
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"子代理返回结论而非原始日志"
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],
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"evidence": "并行 + 上下文隔离，是大仓库提效的关键手段"
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},
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{
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"id": "workflow:auto-record",
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"type": "workflow",
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"name": "开发完成自动记录",
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"summary": "每次开发完成后自动捕获三类信息并沉淀，形成持续更新的知识库。",
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"stages": ["scan_capabilities", "collect_dev_process", "capture_artifacts", "upsert_knowledge_graph"],
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"how": [
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"命令：node scripts/update-knowledge.mjs（可加 --dry-run 预览）",
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"能力特征 → .trae/knowledge/capabilities.json（API/页面/组件/模型/脚本/任务/技术栈快照）",
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"开发过程 → .trae/knowledge/dev_process.jsonl（按 commit 追加）",
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"模型成果 → .trae/knowledge/artifacts.jsonl（迁移/脚本/提交统计等交付物）",
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"写回 .trae/knowledge_graph.json（权威）+ .trae/knowledge_graph.jsonl（MCP 读取）"
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],
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"trigger": ["git post-commit hook（本机自动）", "AI 任务收尾手动执行", "npm run knowledge:update"],
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"evidence": "不落盘的迭代 = 每次重新开始；落盘后 AI 可直接检索历史决策与踩坑",
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"project_binding": "scripts/update-knowledge.mjs"
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},
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{
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"id": "workflow:persistent-context-load",
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"type": "workflow",
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"name": "持久上下文加载顺序",
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"summary": "AI 每次任务开始按固定顺序加载上下文，避免重复交代与遗漏。",
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"stages": ["rules/README.md(索引)", "ai_agent_rules.md(启动规则)", "ai_coding_knowledge.json(方法层)", "knowledge_graph.jsonl(事实层,按需)", "具体源码"],
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"how": ["先索引后详情，按 context-budget 原则只加载相关部分"],
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"evidence": "固定加载顺序让 AI 行为可预测，减少『忘记项目约定』类返工"
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},
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{
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"id": "tool:trae",
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"type": "tool",
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"category": "ai_ide",
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"name": "Trae",
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"summary": "本项目实际使用的 AI IDE，规则体系（.trae/rules）+ MCP + 技能 + 子代理。",
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"observations": [
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".trae/rules/*.md 规则体系（工作区级 + 项目级）",
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".trae/mcp.json 注册 MCP 服务（含 Knowledge Graph Memory）",
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"内置子代理（搜索/前端/后端/测试等）与 TodoWrite 任务管理",
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"支持的 Skills 可封装可复用流程"
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],
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"use_cases": ["本项目日常开发", "规则驱动的 AI 协作", "知识库维护"],
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"strengths": ["规则+知识图谱+子代理一体", "中文语境好"],
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"weaknesses": ["生态较新", "部分能力依赖版本"]
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},
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{
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"id": "tool:claude-code",
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"type": "tool",
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"category": "terminal_agent",
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"name": "Claude Code",
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"summary": "终端 Agent-first 工具，大上下文，可读代码库、改文件、跑命令、自修复，端到端完成任务。",
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"observations": ["MCP 原生支持", ".claude/skills 跨会话复用约定", "hooks 做确定性约束", "自动读报错→定位→修复→重跑测试闭环"],
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"use_cases": ["大型重构", "跨文件修改", "疑难 Bug 根因定位", "代码审查"],
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"strengths": ["自主性最高", "终端串联顺手", "MCP 生态完整"],
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"weaknesses": ["无 IDE 补全体验", "长任务易跑偏需纠偏"]
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},
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{
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"id": "tool:cursor",
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"type": "tool",
|
||
"category": "ai_ide",
|
||
"name": "Cursor",
|
||
"summary": "AI 原生 IDE，Tab 补全 + 内联编辑 + 多文件 Agent，零学习曲线。",
|
||
"observations": ["Tab 补全业界领先", ".cursor/rules/*.mdc 按 glob 自动激活规则", "多模型自由切换", "2025 年底起原生读 AGENTS.md"],
|
||
"use_cases": ["日常编码", "小粒度修改", "保持心流"],
|
||
"strengths": ["补全最顺滑", "可视化 Diff", "有免费层"],
|
||
"weaknesses": ["超大重构易跑偏", "CI/脚本集成弱"]
|
||
},
|
||
{
|
||
"id": "tool:codex",
|
||
"type": "tool",
|
||
"category": "cloud_agent",
|
||
"name": "OpenAI Codex",
|
||
"summary": "云端异步编码 Agent，沙箱执行，超大上下文，适合批量异步任务。",
|
||
"observations": ["异步任务模式：提交后云端执行，完成后通知", "AGENTS.md 标准发起者", "可并行批量处理", "支持只读/全自动权限分级"],
|
||
"use_cases": ["批量修改", "自动提 PR", "异步后台任务", "CI/CD 集成"],
|
||
"strengths": ["异步并行", "沙箱安全", "上下文最大"],
|
||
"weaknesses": ["非实时", "沙箱无本地文件直访"]
|
||
},
|
||
{
|
||
"id": "tool:copilot",
|
||
"type": "tool",
|
||
"category": "ide_extension",
|
||
"name": "GitHub Copilot",
|
||
"summary": "IDE 插件，补全 + agent 模式，通过 copilot-instructions.md 注入项目上下文。",
|
||
"observations": ["补全速度快", "GitHub 深度集成", "agent 模式支持多文件编辑与建 PR"],
|
||
"use_cases": ["日常补全", "快速原型", "GitHub 工作流"],
|
||
"strengths": ["补全快", "集成深"],
|
||
"weaknesses": ["自主性弱于终端 Agent"]
|
||
},
|
||
{
|
||
"id": "tool:mcp",
|
||
"type": "tool",
|
||
"category": "protocol",
|
||
"name": "MCP（Model Context Protocol）",
|
||
"summary": "AI 与外部工具/数据源的标准接口层，让代理具备确定性能力（读库、查图、调 API）。",
|
||
"observations": [
|
||
"本项目 .trae/mcp.json 注册服务",
|
||
"Knowledge Graph Memory：结构化长期记忆（读写 .trae/knowledge_graph.jsonl）",
|
||
"可接数据库/仓库/浏览器等外部能力",
|
||
"工具描述文件需先读 schema 再调用"
|
||
],
|
||
"use_cases": ["长期记忆", "确定性数据访问", "跨会话知识复用"],
|
||
"strengths": ["标准化", "可组合", "确定性优于纯 prompt"],
|
||
"weaknesses": ["需按 schema 调用", "服务未挂载则退化为文件维护"]
|
||
},
|
||
{
|
||
"id": "tool:agents-md",
|
||
"type": "tool",
|
||
"category": "standard",
|
||
"name": "AGENTS.md 开放标准",
|
||
"summary": "跨工具的项目上下文单一事实源，symlink 到各工具专属文件，解决配置碎片化。",
|
||
"observations": ["ln -s AGENTS.md CLAUDE.md / .cursorrules", "已被数万开源项目采用", "Cursor/Codex 原生支持"],
|
||
"use_cases": ["多工具并存的项目", "上下文统一治理"],
|
||
"strengths": ["一次编写多工具复用"],
|
||
"weaknesses": ["各工具支持度仍有差异"]
|
||
},
|
||
{
|
||
"id": "anti_pattern:one-shot-big",
|
||
"type": "anti_pattern",
|
||
"name": "一次性生成大量代码",
|
||
"why_bad": "Agent 在大任务里易跑偏，产出难审查、难验证",
|
||
"fix": "任务拆解，一次只给一个窄范围目标，分步实现 + 验证",
|
||
"detect": "单个任务改动文件数 > 15 或 diff > 800 行时需重新拆解"
|
||
},
|
||
{
|
||
"id": "anti_pattern:no-acceptance",
|
||
"type": "anti_pattern",
|
||
"name": "没有验收标准",
|
||
"why_bad": "AI 无法判断任务是否完成，会无限发散或过早收工",
|
||
"fix": "每个任务先定义可验证标准（tsc 零错误 / vitest 全绿 / 性能不回归）",
|
||
"detect": "任务开始前说不出『怎么算完成』即触发"
|
||
},
|
||
{
|
||
"id": "anti_pattern:skip-review",
|
||
"type": "anti_pattern",
|
||
"name": "不审查 AI 生成代码",
|
||
"why_bad": "可能含 bug、安全风险、过时实践、臆造 API",
|
||
"fix": "看 diff 逐行审查 + 类型检查 + 测试三重门禁",
|
||
"detect": "直接 git commit 且未跑门禁即触发"
|
||
},
|
||
{
|
||
"id": "anti_pattern:vague-task",
|
||
"type": "anti_pattern",
|
||
"name": "模糊的大任务",
|
||
"why_bad": "『优化一下社区模块』这类任务 Agent 必然迷失",
|
||
"fix": "改成窄范围明确目标，并给出验收标准",
|
||
"detect": "任务描述无具体文件/接口/行为即触发"
|
||
},
|
||
{
|
||
"id": "anti_pattern:context-overload",
|
||
"type": "anti_pattern",
|
||
"name": "一次性灌入冗余历史",
|
||
"why_bad": "上下文超载导致抓不住重点、成本飙升、准确率下降",
|
||
"fix": "只注入必要文件与最近变更，分层索引按需加载",
|
||
"detect": "单次注入 > 10 个无关文件即触发"
|
||
},
|
||
{
|
||
"id": "anti_pattern:model-is-everything",
|
||
"type": "anti_pattern",
|
||
"name": "把模型更强当系统更稳",
|
||
"why_bad": "没有编排、门禁与观测，强模型也会产生不可控波动",
|
||
"fix": "模型层 + 编排层 + 门禁/观测层三层闭环，而非只换更强模型",
|
||
"detect": "复盘只归因于『换个模型就好』且无门禁改进即触发"
|
||
},
|
||
{
|
||
"id": "anti_pattern:divergent-copies",
|
||
"type": "anti_pattern",
|
||
"name": "同一知识多份副本各自漂移",
|
||
"why_bad": "规则/图谱/文档多份并存且内容冲突，AI 加载到哪份全靠运气",
|
||
"fix": "确立单一事实源（其余派生）+ 增量化同步脚本 + 定期一致性巡检",
|
||
"detect": "同一事实在两处以上出现且可被独立编辑即触发",
|
||
"project_evidence": "2026-10-01 巡检发现：两份 project_rules.md 并存、根 modules.md 与实际规格文件不一致"
|
||
},
|
||
{
|
||
"id": "anti_pattern:memory-rot",
|
||
"type": "anti_pattern",
|
||
"name": "知识库只写不更新",
|
||
"why_bad": "知识图谱/规则过期后，AI 会依据错误事实决策，比没有知识更危险",
|
||
"fix": "开发完成强制运行 auto_record；关键节点做『图谱 vs 代码』一致性巡检",
|
||
"detect": "knowledge_graph.updated_at 落后于最近 commit 超过 7 天即触发",
|
||
"project_binding": "workflow:auto-record + node scripts/update-knowledge.mjs"
|
||
}
|
||
],
|
||
"relations": [
|
||
{"from": "method:context-engineering", "to": "principle:stateless-agent", "type": "SOLVES"},
|
||
{"from": "method:context-engineering", "to": "anti_pattern:context-overload", "type": "AVOIDS"},
|
||
{"from": "method:task-decomposition", "to": "anti_pattern:one-shot-big", "type": "AVOIDS"},
|
||
{"from": "method:task-decomposition", "to": "anti_pattern:vague-task", "type": "AVOIDS"},
|
||
{"from": "method:acceptance-criteria", "to": "anti_pattern:no-acceptance", "type": "AVOIDS"},
|
||
{"from": "method:review-diff", "to": "anti_pattern:skip-review", "type": "AVOIDS"},
|
||
{"from": "method:failure-classification", "to": "anti_pattern:model-is-everything", "type": "AVOIDS"},
|
||
{"from": "principle:single-source-of-truth", "to": "anti_pattern:divergent-copies", "type": "AVOIDS"},
|
||
{"from": "workflow:auto-record", "to": "anti_pattern:memory-rot", "type": "AVOIDS"},
|
||
{"from": "workflow:auto-record", "to": "workflow:persistent-context-load", "type": "FEEDS"},
|
||
{"from": "workflow:plan-first-loop", "to": "method:task-decomposition", "type": "USES"},
|
||
{"from": "workflow:plan-first-loop", "to": "method:plan-first", "type": "IMPLEMENTS"},
|
||
{"from": "workflow:plan-first-loop", "to": "workflow:edit-test-loop", "type": "CONTAINS"},
|
||
{"from": "workflow:plan-first-loop", "to": "workflow:auto-record", "type": "ENDS_WITH"},
|
||
{"from": "workflow:edit-test-loop", "to": "method:test-first-gate", "type": "USES"},
|
||
{"from": "method:test-first-gate", "to": "principle:enforcement-pyramid", "type": "IMPLEMENTS"},
|
||
{"from": "method:logging-standard", "to": "principle:observability-first", "type": "IMPLEMENTS"},
|
||
{"from": "method:spec-driven", "to": "method:acceptance-criteria", "type": "REQUIRES"},
|
||
{"from": "method:plan-first", "to": "method:spec-driven", "type": "RELATES_TO"},
|
||
{"from": "method:incremental-refactor", "to": "anti_pattern:one-shot-big", "type": "AVOIDS"},
|
||
{"from": "workflow:subagent-fanout", "to": "principle:context-budget", "type": "IMPLEMENTS"},
|
||
{"from": "tool:trae", "to": "workflow:plan-first-loop", "type": "BEST_AT"},
|
||
{"from": "tool:claude-code", "to": "workflow:edit-test-loop", "type": "BEST_AT"},
|
||
{"from": "tool:cursor", "to": "method:task-decomposition", "type": "NEEDS"},
|
||
{"from": "tool:codex", "to": "tool:agents-md", "type": "ORIGINATES"},
|
||
{"from": "tool:trae", "to": "tool:mcp", "type": "SUPPORTS"},
|
||
{"from": "tool:trae", "to": "workflow:auto-record", "type": "HOSTS"},
|
||
{"from": "tool:mcp", "to": "workflow:auto-record", "type": "STORES"}
|
||
]
|
||
}
|