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curiositech/windags-skills

AI 驅動的行事曆管理和基於代理的排程協調。涵蓋行事曆 API(Google Calendar、CalDAV/iCal)、AI 排程助手(Reclaim、Clockwise、Motion、Cal.com)、使用 MCP 構建自訂行事曆代理、多行事曆合併、時區管理、專注時段保護、會議疲勞檢測以及代理間會議協商協議。

windags-skills 是什麼?

這項技能提供了一套全面的架構,用於構建能夠智慧化協商會議、保護專注時段並從行事曆數據中推斷上下文的 AI 代理。它專注於以開發者為中心的模式,用於實現時區感知排程、代理對代理會議協商協議,以及通過 MCP 和 Google Calendar API 整合自訂行事曆邏輯。支援的關鍵平台包括 Google Calendar、CalDAV,以及 Reclaim、Clockwise、Motion 和 Cal.com 等 AI 排程助手。該技能詳細說明了應對衝突的策略,包含基於發送者層級、會議類型和專注區塊優先級的決策樹,以及一個時區感知的時段選擇演算法,能夠有效地在跨時區協調會議。它還引入了一個「能量模型」來根據一天中時間的使用者認知峰值對時段進行排序,並解決常見的故障模式,例如時區感知的循環衝突、緩衝執行震盪和遞迴協商迴圈。提供的工作示例演示了實際的跨時區會議協商。該技能明確標示其範圍,適用於排程協調和行事曆數據操作,而非 UI 設計或專案管理。

相容平台~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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說明文件

代理化行事曆協調

建構能夠協商會議、保護專注時段並從行事曆資料推斷上下文的 AI 代理。導航時區複雜性,實現代理對代理排程,並使用 Google Calendar API 和 MCP 架構自訂行事曆邏輯。

決策點

偵測到衝突時

傳入的會議請求與現有事件衝突
├─ 發送者 = 主管/高階主管 → 自動接受,建議移動現有事件
├─ 會議 = 標記為 1:1/緊急 → 在 24 小時內提出替代方案
├─ 專注區塊衝突
│  ├─ 區塊優先級 = 高 (深度工作) → 自動拒絕
│  └─ 區塊優先級 = 中 → 提出替代時間
└─ 與同儕會議雙重預約 → 回覆 3 個替代時段

自動拒絕 vs. 提出替代方案決策

因素:發送者層級 (主管/經理/同儕/外部) × 會議類型 (1:1/團隊/全員) × 專注區塊優先級
如果是 主管 + 任何類型 → 始終提出替代方案
如果是 經理 + (1:1 或 緊急) → 提出替代方案
如果是 同儕 + 週期性 + 低出席率 → 自動拒絕
如果是 外部 + 無先前關係 → 自動拒絕並附上「使用行事曆連結」
如果在 高優先級專注區塊 期間 → 無論發送者為何都自動拒絕

時區感知時段選擇

多位參與者排程:
├─ 全部相同時區 → 使用本地工作時間 (9-17)
├─ 2個時區,相差 <6小時 → 尋找重疊時窗
├─ 2個時區,相差 >6小時 → 早/晚分開 (一方取早上8點,另一方取下午6點)
└─ 3個以上時區 → 提出非同步替代方案 或 輪換時間表

能量模型時段排序

一天中時間的優先級 (降序):
1. 上午 9-11 點 (認知巔峰) → 保留給深度工作,拒絕例行會議
2. 上午 11 點 - 中午 12 點 (持續專注) → 僅允許重要會議
3. 下午 2-4 點 (協作最佳時段) → 偏好用於團隊會議
4. 下午 4-5 點 (收尾時段) → 適合 1:1、狀態更新
5. 下午 1-2 點 (午餐後的低迷) → 避免複雜會議

故障模式

時區感知的週期性衝突

症狀:每週上午 9 點的會議在冬季顯示衝突但在夏季卻沒有;跨時區的週期性會議在 DST 變更後失敗 偵測:if 週期性事件 && 時區轉換 && 衝突模式季節性 修正:始終使用 singleEvents: true 展開週期性事件,將每個實例轉換為 UTC 以進行衝突檢查,在 DST 轉換後重新評估

緩衝執行震盪

症狀:代理為了建立緩衝而反覆重新排程同一個會議;會議在時段之間來回跳動 偵測:if 同一個會議在 24 小時內因緩衝原因被重新排程 > 2 次 修正:在首次重新排程後鎖定會議,根據會議重要性動態調整緩衝需求

能量模型冷啟動

症狀:新代理在使用者認知最差的時間安排會議;忽略已建立的使用模式 偵測:if 對於由代理安排的會議,會議滿意度分數 < 3 修正:使用明確的使用者偏好進行引導,從拒絕模式中學習,在自動排程前需要 2 週的觀察期

結構膨脹與隱私洩露

症狀:代理與外部系統分享過多行事曆細節;事件描述洩漏給 LLM 偵測:if 外部 API 呼叫包含 event.description 或 attendee.email 修正:實作資料最小化層,在外部呼叫前剝離 PII,僅使用空閒/忙碌狀態進行排程

遞迴協商迴圈

症狀:兩個代理無休止地提出反提案;無法就會議時間達成一致 偵測:if 協商回合 > 3 且 無接受的時段 修正:在 2 次反提案後升級給人類,實作「夠好」的接受門檻,為協商設定時間限制

工作示例

跨時區會議協商

場景:你的代理 (芝加哥) 需要與同儕代理 (東京) 進行 30 分鐘的 API 審查。雙方都有能量約束。

步驟 1:初步分析

  • 芝加哥:上午 9 點 - 下午 5 點 CST = 凌晨 12 點 - 早上 6 點 JST (隔天)
  • 東京:上午 9 點 - 下午 6 點 JST = 下午 7 點 - 凌晨 4 點 CST (前一天)
  • 重疊時窗:下午 7-8 點 CST = 上午 9-10 點 JST

步驟 2:能量模型檢查

  • 芝加哥下午 7 點 = 工作日結束 (能量:6/10)
  • 東京上午 9 點 = 認知巔峰時段 (能量:9/10)
  • 偵測到不平衡 → 需要妥協

步驟 3:提案生成

代理芝加哥 → 代理東京:提案
時段:[
  "2024-03-19T01:00Z" (芝加哥:下午7點,東京:上午10點),
  "2024-03-19T23:00Z" (芝加哥:下午5點,東京:上午8點),
  "2024-03-20T00:00Z" (芝加哥:下午6點,東京:上午9點)
]
優先級:正常
主題:「API 審查 - 時區複雜度討論」

步驟 4:反提案

代理東京 → 代理芝加哥:反提案
原因:「對於複雜技術討論偏好東京較晚的上午時段」
替代方案:[
  "2024-03-19T02:00Z" (芝加哥:下午8點,東京:上午11點),
  "2024-03-20T01:00Z" (芝加哥:下午7點,東京:上午10點)
]
約束條件:「在 JST 上午 11:30 站立會議前需要 15 分鐘緩衝」

步驟 5:解決方案 芝加哥代理接受下午 8 點 CST / 上午 11 點 JST 時段,在事件描述中使用兩個時區建立事件: 「API 審查 - 下午 8:00 CST / 上午 11:00 JST+1」

新手 vs 專家:

  • 新手:在對芝加哥方便的時間進行排程,未檢查東京的認知時段
  • 專家:識別能量不平衡,在東京精神飽滿時提出芝加哥的晚間時段

品質關卡

  • 所有時間戳記在內部儲存為 UTC,僅在顯示/API 邊界進行轉換
  • 整個過程使用 IANA 時區名稱 (不使用像是 "-05:00" 的 UTC 偏移字串)
  • 空閒/忙碌查詢剝離事件詳細資訊,僅與外部代理共用時間區塊
  • 緩衝執行在連續會議之間建立最少 10-15 分鐘
  • 專注區塊標記為「忙碌」,但對於協商而言優先級低於實際會議
  • 能量模型考慮使用者的認知巔峰時段和會議疲勞模式
  • OAuth 令牌在靜止時加密,並實作自動重新整理流程
  • 速率限制透過指數退避處理 Google Calendar API 配額
  • 在衝突偵測前將週期性事件展開為單獨的實例
  • 多行事曆合併需在具有不同權限的個人/工作日曆上進行測試
  • 代理協商具有時間限制,在失敗回合後可升級給人類
  • 稽核日誌記錄所有行事曆變更,並具有回滾能力

非此技能範圍的邊界

此技能並非用於:

  • 建置行事曆 UI 元件 → 使用 form-validation-architect
  • 專案甘特圖或里程碑追蹤 → 使用 project-management-guru-adhd
  • 蕃茄鐘計時器或時間追蹤應用程式 → 使用 adhd-daily-planner
  • 通用代理架構模式 → 使用 agentic-patterns
  • 建置基礎代理框架 → 使用 agent-creator

當以下情況時委派給其他技能:

  • 使用者需要 ADHD 特定的時間管理 → adhd-daily-planner
  • 建置持久性代理記憶體 → always-on-agent-architecture
  • 超出行事曆範圍的多代理協調 → multi-agent-coordination
  • 背景守護行程架構 → daemon-development

此技能專門專注於行事曆資料結構、排程演算法和會議協調邏輯。

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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