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智能日历协调

基于AI代理的日历管理与日程协调。涵盖日历API(Google Calendar、CalDAV/iCal)、AI日程助手(Reclaim、Clockwise、Motion、Cal.com)、使用MCP构建自定义日历代理、多日历合并、时区管理、专注时间保护、会议疲劳检测以及代理间会议协商协议。

智能日历协调 是什么?

此技能旨在帮助开发者构建具备高级日历协调能力的AI代理。其核心功能包括:自动处理会议冲突(根据发送者层级、会议类型和专注块优先级决定接受、拒绝或提议替代时间)、智能选择时区感知的会议时段、以及应用能量模型(根据用户认知峰值时段优先安排或拒绝会议)。该技能支持Google Calendar API和MCP,可用于如cursor、Claude Code等平台,并详细描述了故障模式(如时区递归冲突、缓冲区强制执行抖动)及对应解决方案。最终目标是通过Q&A确保代理高效、隐私安全地管理日程。

兼容平台~Claude Code~Codex CLI~Cursor✓Antigravity
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/agentic-calendar-coordination

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文档

代理式日历协调

构建能够协商会议、保护专注时间并从日历数据中推断上下文的AI代理。驾驭时区复杂性,实现代理间日程安排,并使用Google Calendar API和MCP构建自定义日历逻辑。

决策点

检测到冲突时

收到与现有事件冲突的会议请求
├─ 发送者 = 经理/高管 → 自动接受,建议移动现有事件
├─ 会议 = 一对一/紧急标记 → 在24小时内提议替代时间
├─ 专注块冲突
│  ├─ 块优先级 = 高(深度工作) → 自动拒绝
│  └─ 块优先级 = 中等 → 提议替代时间
└─ 与同级别会议双重预订 → 提供3个替代时段作为回复

自动拒绝与提议替代决策

因素:发送者层级(高管/经理/同级别/外部)× 会议类型(一对一/团队/全员会议)× 专注块优先级
如果 高管 + 任何类型 → 总是提议替代
如果 经理 +(一对一或紧急)→ 提议替代
如果 同级别 + 定期 + 低出席率 → 自动拒绝
如果 外部 + 无先前关系 → 自动拒绝并回复“请使用日历链接”
如果 处于高优先级专注块期间 → 无论发送者是谁,一律自动拒绝

时区感知时段选择

多参与者日程安排:
├─ 所有参与者同一时区 → 使用本地工作时段(9-17点)
├─ 2个时区,且小于6小时差距 → 寻找重叠窗口
├─ 2个时区,且大于6小时差距 → 早晚分拆(一方早8点,另一方晚6点)
└─ 3个及以上时区 → 提议异步替代方案或轮换安排

能量模型时段排名

时间段优先级(降序):
1. 上午9-11点(认知高峰期)→ 保留给深度工作,拒绝常规会议
2. 上午11-12点(持续专注)→ 仅允许重要会议
3. 下午2-4点(协作黄金时段)→ 偏好用于团队会议
4. 下午4-5点(收尾时段)→ 适合一对一、状态更新
5. 下午1-2点(午饭后低谷期)→ 避免复杂会议

故障模式

时区感知的递归冲突

症状:每周上午9点会议冬季显示冲突但夏季不显示;跨时区递归会议在夏令时变更后失败 检测:if recurring_event && timezone_conversion && conflict_pattern_seasonal 修复:始终使用 singleEvents: true 展开递归事件,将每个实例转换为UTC进行冲突检查,并在夏令时过渡后重新评估

缓冲区强制执行抖动

症状:代理反复重新安排同一会议以创建缓冲区;会议在多个时段间来回跳动 检测:if same_meeting_rescheduled > 2 times in 24h for buffer_reasons 修复:首次重新安排后锁定会议,根据会议重要性动态调整缓冲区要求

能量模型冷启动

症状:新代理在用户认知能力最差的时间段安排会议;忽略已建立的行为模式 检测:if meeting_satisfaction_score < 3 for meetings_scheduled_by_agent 修复:使用明确的用户偏好进行引导,从拒绝模式中学习,需要2周观察期后才允许自动调度

架构膨胀与隐私泄露

症状:代理向外部系统分享过多日历细节;事件描述泄露给LLM 检测:if external_api_call contains event.description OR attendee.email 修复:实现数据最小化层,在外部调用前剥离个人身份信息,仅使用忙/闲状态进行调度

递归协商循环

症状:两个代理无休止地互相提出反提案;无法就会议时间达成一致 检测:if negotiation_rounds > 3 && no_accepted_slot 修复:在两次反提案后升级给人类,实施“足够好”接受阈值,为协商设定时间限制

工作示例

跨时区会议协商

场景:你的代理(芝加哥)需要与同级别代理(东京)进行30分钟的API审查。双方都有能量约束。

步骤1:初步分析

  • 芝加哥:上午9点-下午5点CST = 次日上午12点-6点JST
  • 东京:上午9点-下午6点JST = 前一天下午7点-次日上午4点CST
  • 重叠窗口:CST下午7-8点 = JST上午9-10点

步骤2:能量模型检查

  • 芝加哥下午7点 = 工作日结束(能量:6/10)
  • 东京上午9点 = 认知高峰期(能量:9/10)
  • 检测到不平衡 → 需要折衷

步骤3:提案生成

Agent Chicago → Agent Tokyo: PROPOSE
时段:[  
  "2024-03-19T01:00Z" (芝:晚上7点,东:上午10点),
  "2024-03-19T23:00Z" (芝:下午5点,东:上午8点),
  "2024-03-20T00:00Z" (芝:下午6点,东:上午9点)
]
优先级:普通
主题:“API审查 - 时区复杂性讨论”

步骤4:反提案

Agent Tokyo → Agent Chicago: COUNTER
理由:“更偏好东京晚些时候的上午来处理复杂技术讨论”
替代方案:[
  "2024-03-19T02:00Z" (芝:晚上8点,东:上午11点),
  "2024-03-20T01:00Z" (芝:晚上7点,东:上午10点)
]
约束:“在JST上午11:30站立会议前需要15分钟缓冲区”

步骤5:解决 芝加哥代理接受CST晚上8点/JST上午11点的时段,在描述中创建包含两个时区的事件: “API审查 - 晚上8:00 CST / 上午11:00 JST+1”

新手与专家对比:

  • 新手:在芝加哥方便的时间安排,而不检查东京的认知小时
  • 专家:识别能量不平衡,提议芝加哥较晚的一天时间,此时东京精力充沛

质量门禁

  • 所有时间戳内部以UTC存储,仅在显示/API边界处转换
  • 整个过程中使用IANA时区名称(不使用如“-05:00”的UTC偏移量字符串)
  • 忙/闲查询剥离事件详情,仅与外部代理共享时间块
  • 缓冲区强制执行在连续会议之间创建至少10-15分钟的时间间隔
  • 专注块标记为“忙碌”,但在协商中优先级低于实际会议
  • 能量模型考虑用户的认知高峰时间和会议疲劳模式
  • OAuth令牌在静态时加密,并实现自动刷新流程
  • 速率限制通过指数退避处理Google Calendar API配额
  • 在冲突检测前将递归事件展开为单个实例
  • 多日历合并经过个人/工作日历及不同权限的测试
  • 代理协商有时间限制,失败轮次后升级给人类
  • 审计日志捕获所有日历变更,并具备回滚能力

非适用边界

此技能不适用于:

  • 构建日历UI组件 → 使用 form-validation-architect
  • 项目甘特图或里程碑跟踪 → 使用 project-management-guru-adhd
  • 番茄钟或时间跟踪应用 → 使用 adhd-daily-planner
  • 通用代理架构模式 → 使用 agentic-patterns
  • 构建基础代理框架 → 使用 agent-creator

在以下情况委托给其他技能:

  • 用户需要ADHD特定的时间管理 → adhd-daily-planner
  • 构建持久代理记忆 → always-on-agent-architecture
  • 日历之外的多个代理协调 → multi-agent-coordination
  • 后台守护进程架构 → daemon-development

此技能专门关注日历数据结构、调度算法和会议协调逻辑。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

curiositech/windags-skills

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

curiositech/windags-skills

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

curiositech/windags-skills

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

curiositech/windags-skills

Implement WCAG 2.2 AA/AAA compliance with automated testing, keyboard navigation, screen reader support, and focus management. Activate on: accessibility audit, WCAG compliance, keyboard navigation, screen reader, aria attributes, axe-core, focus trap. NOT for: design-level accessibility review (use design-accessibility-auditor), color contrast only (use css-in-js-architect).

curiositech/windags-skills

Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

curiositech/windags-skills

Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.

curiositech/windags-skills

>- Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

curiositech/windags-skills

Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.

curiositech/windags-skills

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

curiositech/windags-skills

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

curiositech/windags-skills

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

curiositech/windags-skills

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

curiositech/windags-skills

Automated discovery and matching of agent skills for dynamic task routing and capability assessment

curiositech/windags-skills

Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

curiositech/windags-skills

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

curiositech/windags-skills

Logic-based agent programming language implementing BDI architecture for practical autonomous agent development

curiositech/windags-skills

>- Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

curiositech/windags-skills

Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

curiositech/windags-skills

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

curiositech/windags-skills

license: Apache-2.0 NOT for unrelated tasks outside this domain.

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