Communitygithub.com

curiositech/windags-skills

時間區間代數,用於在規劃與知識表示中推理時間關係。

windags-skills 是什麼?

此技能實作 Allen(1983)的區間代數,用於處理時間規劃與知識表示中的定性時間推理。它處理時間不確定性、約束傳播以及不一致性偵測。此技能支援 Claude Code 等 AI 代理,提供結構化的工作流程,包含觸發情境、決策點、反模式與失敗模式、實際範例以及品質檢查。核心運作方式是將事件建模為區間(而非時間點),並維護完整的 13 種關係辭彙表,在證據不足時保持析取標籤(如 {before, meets, overlaps})。它採用參考區間層次結構(年→月→日)進行跨尺度推理,並以可廢棄的預設值處理持續狀態。提供的範例涵蓋會議排程衝突偵測與流程相依性分析。

相容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/allen-cacm1983

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

SKILL.md:時間區間推理(Allen 1983)

何時使用此技能

遇到以下情況時載入此技能:

觸發情境Allen 適用的原因
"任務 A 是否在任務 B 開始前結束?"需要區間關係分類(before, meets, overlaps…)
"我不確知 X 何時發生,只知道它發生在 Y 期間"對 13 種關係存在析取不確定性
"如果加入此約束,排程是否仍可行?"約束傳播 + 不一致性偵測
"事件 A 每天發生;事件 B 每年發生 — 它們如何互動?"用於範圍管理的參考區間層次結構

請勿使用,如果你只需要比較兩個絕對時間戳記且無不確定性 — 純算術即可。


決策點

新增時間斷言

IF 斷言與現有約束衝突:
    → 在傳播期間偵測到空的弧標籤
    → 立即停止,報告不一致的區間配對
    → 不要在不一致的狀態下繼續

IF 斷言一致:
    → 從受影響的弧執行約束傳播
    → 透過交集傳遞性結果更新標籤
    → 繼續直到不再有精煉可能

處理時間不確定性

IF 證據僅支援多重關係:
    → 維持完整的析取標籤 {before, meets, overlaps}
    → 在沒有證據時絕不縮減為單一關係
    → 記錄何時/為何約束集合縮小

IF 必須在不確定的情況下行動:
    → 直接使用析取標籤進行規劃
    → 將任何假設標記為可廢棄
    → 避免過早承諾

界定時間推理範圍

IF 處理時間尺度差異極大的事件:
    → 識別參考區間層次結構(年→月→日)
    → 在各個叢集內分別推理
    → 在邊界建立明確的橋接約束
    → 避免將所有東西扁平化為單一全域圖譜

IF 局部推理感覺足夠:
    → 驗證查詢範圍符合參考區間層級
    → 僅在相關叢集內傳播
    → 僅在偵測到相依性時升級跨叢集處理

狀態持續性

IF 狀態持續且沒有明確的結束邊界:
    → 套用持續性預設值 且 將其標記為可廢棄
    → 任何未來的結束邊界斷言會覆寫預設值
    → 絕不將持續性假設視為硬性約束

IF 提供了明確的時間邊界:
    → 覆寫任何現有的持續性預設值
    → 正常傳播新約束

失敗模式

反模式症狀診斷修正
點化將事件縮減為時間戳記,失去重疊/包含資訊使用 time=14:30 而非 [14:30, 14:45] 區間將每個事件建模為具有開始/結束的區間,即使持續時間未知
過早承諾當證據支援多重關係時,選擇單一關係當只知道「不在期間內」時斷言「之前」維持完整的析取標籤,直到證據強制縮小
全域傳播炸彈每次更新 O(N²) 成本,效能隨知識庫大小而下降針對局部查詢在整個圖譜上執行約束傳播使用參考區間層次結構來限制傳播範圍
空標籤否認在偵測到不一致後繼續推理因為衝突的約束被忽略而得到無意義的結果將空的弧標籤視為成功(及早捕獲不一致),立即停止
持續性僵化無法覆寫「持續直到變更」的假設新的時間邊界因與持續性衝突而被拒絕將所有持續性預設值標記為可廢棄,允許透過明確斷言覆寫

偵測規則:

  • 如果你看到沒有持續時間的時間戳記 → 點化
  • 如果你看到任意選擇的單一關係 → 過早承諾
  • 如果傳播成本隨總知識庫大小增長 → 全域傳播炸彈
  • 如果在空標籤後繼續推理 → 空標籤否認
  • 如果新的時間事實被拒絕 → 持續性僵化

實際範例

範例 1:會議排程衝突偵測

情境: 在已有「客戶電話」和「工程站立會議」的行事曆中加入「團隊審查」

初始狀態:

客戶電話 (I₁):[10:00, 11:00]
工程站立會議 (I₂):[11:30, 12:00]
已知:I₁ {before} I₂

新斷言: 團隊審查 (I₃):[10:45, 11:15]

專家推理軌跡:

  1. 分類新關係:

    • I₃ vs I₁:從 10:45 開始,I₁ 於 11:00 結束 → {overlaps}
    • I₃ vs I₂:於 11:15 結束,I₂ 於 11:30 開始 → {before}
  2. 約束傳播:

    • 從 I₁ {before} I₂ 和 I₃ {overlaps} I₁
    • 傳遞性:overlaps ∘ before = {before, overlaps, meets, starts, during}
    • 新約束:I₃ {before, overlaps, meets, starts, during} I₂
    • 與 I₃ {before} I₂ 交集:標籤變為 {before}
  3. 一致性檢查: 所有標籤非空 → 排程可行但存在重疊

新手可能會錯過:

  • 可能不會檢查 I₃ 與 I₁ 重疊造成資源衝突
  • 可能不會驗證傳遞性維持一致性
  • 可能不會區分「可行順序」與「無資源衝突」

專家會捕捉到:

  • 透過關係分類立即標記重疊偵測
  • 傳播驗證全域一致性維持
  • 清楚區分時間可行性與資源可用性

範例 2:流程階段相依性

情境: 任務完成時間不確定的部署管線

初始狀態:

建置階段 (B):持續時間未知,必須在部署前完成
測試階段 (T):與建置結尾重疊,持續時間不確定
部署階段 (D):在 B 和 T 都完成後開始

約束網路:

T {overlaps, finishes} B  (測試可以隨建置結束或在其之前結束)
B {before, meets} D       (建置必須在部署前完成)
T {before, meets} D       (測試必須在部署前完成)

新資訊:「測試發現關鍵錯誤,延長 2 小時」

專家推理:

  1. 更新影響範圍: 測試延長將 T 的結束點推後
  2. 傳播: 如果 T {finishes} B 原本成立,現在 T {overlaps} B(延長超過建置結束)
  3. 檢查一致性: T {overlaps} B 仍允許 B {before, meets} D 和 T {before, meets} D
  4. 結果: 部署延遲但管線邏輯不變

新手可能會錯過:

  • 可能假設測試完成不影響建置-部署相依性
  • 可能不會將測試延長傳播到部署時序
  • 可能將「測試延長」視為孤立事實,而非約束網路更新

專家會捕捉到:

  • 認識到測試-建置關係必須重新評估
  • 將時序影響透過完整約束網路傳播
  • 適當地維持不確定性(部署可以在建置後立即開始,或在延長的測試後開始)

品質檢查

時間推理任務完成時:

  • 所有區間以明確的開始/結束邊界表示(無點事件)
  • 每個區間配對具有非空的 13 種關係辭彙表弧標籤
  • 約束傳播執行至完成(無待處理的傳遞性更新)
  • 所有析取標籤誠實反映當前證據(無任意承諾)
  • 為多尺度推理建立參考區間層次結構
  • 持續性預設值在適用處標記為可廢棄
  • 不一致性偵測已驗證(捕獲並報告空標籤)
  • 跨參考範圍橋接已明確定義
  • 所有時間斷言可追溯至證據來源
  • 查詢範圍符合推理粒度層級

不屬於此技能的範圍

請勿將 Allen 的區間代數用於:

  • 簡單時間戳記比較 → 使用算術:if (t1 < t2)
  • 度量持續時間計算 → 使用日曆算術:end_date - start_date
  • 即時排程最佳化 → 使用具有數值域的約束滿足
  • 機率性時間推理 → 使用時間機率網路
  • 連續時間動態 → 使用微分方程式
  • 高頻事件串流 → 使用串流處理架構

改為委託:

  • 度量約束:使用 constraint-satisfaction-with-numeric-domains
  • 機率性時間推理:使用 bayesian-temporal-networks
  • 即時系統:使用 rate-monotonic-scheduling
  • 連續動態:使用 temporal-logic-model-checking
  • 事件串流處理:使用 complex-event-processing

Allen 擅長: 在不確定性下進行定性時間推理,具有增量知識更新和層次化範圍管理。

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

AI-powered calendar management and agent-based scheduling coordination. Covers calendar APIs (Google Calendar, CalDAV/iCal), AI scheduling assistants (Reclaim, Clockwise, Motion, Cal.com), building custom calendar agents with MCP, multi-calendar merging, timezone management, focus block protection, meeting fatigue detection, and agent-to-agent meeting negotiation protocols. Activate on: "calendar agent", "AI scheduling", "calendar coordination", "meeting scheduling", "calendar API", "focus time protection", "calendar optimization", "Google Calendar MCP", "Reclaim", "Clockwise", "Motion", "Cal.com", "smart scheduling", "calendar-aware agent", "timezone scheduling", "agent negotiation meetings". NOT for: manual calendar UI component design (use form-validation-architect), project management scheduling or Gantt charts (use project-management-guru-adhd), general time-tracking or pomodoro apps (use adhd-daily-planner for time-awareness), building the agent itself from scratch (use agent-creator).

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

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

相關技能