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curiositech/windags-skills

时序区间代数,用于规划和知识表示中的时间关系推理

windags-skills 是什么?

该技能实现了Allen(1983)提出的时序区间代数,为AI agent提供了一套处理定性时间关系的系统方法。当面对事件排序、重叠检测、约束传播和不确定性下的时间推理等问题时,此技能尤为有用。它支持13种基础区间关系(如before、meets、overlaps等),并能够处理析取性不确定性和层次化时间范围管理。主要平台包括Claude Code、Cursor等基于AI的编程助手。该技能的核心工作流程包括:检测新断言与现有约束的一致性、通过传递闭包进行约束传播、维护析取标签以反映真实证据,以及利用参考区间层级防止全局传播导致的性能爆炸。它特别强调避免‘点化’(将区间简化为时间点)、过早承诺(在证据不足时选择单一关系)和空标签否认(在发现不一致后继续推理)等反模式。提供的示例(如会议日程冲突检测和部署流水线阶段依赖分析)清晰展示了专家与新手推理过程的差异。该技能明确设定了边界:不适用于简单时间戳比较、度量时长计算或实时调度优化,并给出了相应的替代技能建议。

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

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打开一个已预加载此 Agent Skill 的新对话。

文档

SKILL.md:时序区间推理(Allen 1983)

何时使用此技能

遇到以下情况时加载此技能:

触发场景为何Allen适用
“任务A是否在任务B开始前完成?”需要区间关系分类(before, meets, overlaps…)
“我不确切知道X何时发生,只知道它发生在Y期间”13种关系的析取不确定性
“如果我们添加此约束,计划是否仍然可行?”约束传播 + 不一致检测
“事件A每天发生;事件B每年发生——它们如何相互作用?”参考区间层级,用于范围管理

不要使用,如果你只需要比较两个绝对时间戳且没有不确定性——普通算术就足够了。


决策要点

添加新时序断言时

IF 断言与现有约束冲突:
    → 在传播过程中检测到空弧标签
    → 立即停止,报告不一致的区间对
    → 不要在状态不一致时继续

IF 断言一致:
    → 从受影响的弧开始运行约束传播
    → 通过交集传递性结果更新标签
    → 持续直到无法再细化

处理时序不确定性

IF 证据仅支持多种关系:
    → 维护完整的析取标签 {before, meets, overlaps}
    → 在没有证据的情况下绝不缩减为单一关系
    → 记录约束集缩小的时间和原因

IF 必须在不确定性下行动:
    → 直接使用析取标签进行规划
    → 将所有假设标记为可废止的
    → 避免过早承诺

给定时序推理范围

IF 处理跨越大相径庭时间尺度的事件:
    → 确定参考区间层级(年→月→日)
    → 在每个簇内分别进行推理
    → 在边界处创建显式的桥接约束
    → 避免将所有内容扁平化为一个全局图

IF 局部推理感觉足够:
    → 验证查询范围与参考区间层级匹配
    → 仅在相关簇内传播
    → 仅在检测到依赖时升级到跨簇

状态持久性

IF 状态持续进行,没有明确的结束边界:
    → 应用持久性默认值并标记为可废止
    → 任何未来的结束边界断言将覆盖默认值
    → 永远不要将持久性假设视为硬约束

IF 提供了明确的时间边界:
    → 覆盖任何现有的持久性默认值
    → 正常传播新约束

失败模式

反模式症状诊断修复
点化将事件简化为时间戳,丢失重叠/包含信息使用time=14:30而不是[14:30, 14:45]区间将每个事件建模为具有开始和结束的区间,即使持续时间未知
过早承诺在证据支持多种关系时选择单一关系在只知道“not during”时断言“before”维护完整的析取标签,直到证据强制缩小范围
全局传播爆炸每次更新的O(N²)成本,性能随KB大小下降为局部查询在整个图上运行约束传播使用参考区间层级限制传播范围
空标签否认检测到不一致后继续推理得到无意义的结果,因为冲突的约束被忽略将空弧标签视为成功(不一致被及早发现),立即停止
持久性刚性无法覆盖“持续到改变为止”的假设新的时间边界因与持久性冲突而被拒绝将所有持久性默认值标记为可废止,允许显式断言覆盖

检测规则:

  • 如果你看到没有持续时间的时间戳 → 点化
  • 如果你看到任意选择的单一关系 → 过早承诺
  • 如果传播成本随总KB大小增长 → 全局传播爆炸
  • 如果在空标签后继续推理 → 空标签否认
  • 如果新的时间事实被拒绝 → 持久性刚性

工作示例

示例1:会议日程冲突检测

场景: 在已有“客户电话”和“工程晨会”的日历中添加“团队评审”

初始状态:

客户电话 (I₁): [10:00, 11:00]
工程晨会 (I₂): [11:30, 12:00]
已知: I₁ {before} I₂

新断言: 团队评审 (I₃): [10:45, 11:15]

专家推理过程:

  1. 分类新关系:

    • I₃ vs I₁: 开始于10:45,I₁结束于11:00 → {overlaps}
    • I₃ vs I₂: 结束于11:15,I₂开始于11:30 → {before}
  2. 约束传播:

    • 从 I₁ {before} I₂ 和 I₃ {overlaps} I₁
    • 传递性:overlaps ∘ before = {before, overlaps, meets, starts, during}
    • 新约束:I₃ {before, overlaps, meets, starts, during} I₂
    • 与 I₃ {before} I₂ 取交集:标签变为 {before}
  3. 一致性检查: 所有标签非空 → 计划可行但存在重叠

新手可能遗漏:

  • 可能不会检查I₃与I₁的重叠是否造成资源冲突
  • 可能不会验证传递性是否保持一致性
  • 可能不会区分“可行排序”与“无资源冲突”

专家发现:

  • 重叠检测通过关系分类立即标记
  • 传播验证全局一致性得以维持
  • 清晰区分时间可行性与资源可用性

示例2:流程阶段依赖

场景: 任务完成时间不确定的部署流水线

初始状态:

构建阶段 (B): 持续时间未知,必须在部署前完成
测试阶段 (T): 与构建结束重叠,持续时间不确定
部署阶段 (D): 在B和T都完成后开始

约束网络:

T {overlaps, finishes} B  (测试可以与构建同时完成或在构建之前完成)
B {before, meets} D       (构建必须在部署前完成)
T {before, meets} D       (测试必须在部署前完成)

新信息: “测试发现严重错误,延长2小时”

专家推理:

  1. 更新影响范围: 测试延长将T的终点向后推
  2. 传播: 如果最初 T {finishes} B,现在 T {overlaps} B(延伸到构建结束之后)
  3. 检查一致性: T {overlaps} B 仍然允许 B {before, meets} D 和 T {before, meets} D
  4. 结果: 部署延迟,但流水线逻辑不变

新手可能遗漏:

  • 可能假定测试完成不影响构建-部署依赖
  • 可能不会将测试延长传播到部署时间
  • 可能将“测试延长”视为孤立事实,而非约束网络更新

专家发现:

  • 认识到测试-构建关系必须重新评估
  • 将时间影响传播到整个约束网络
  • 适当保持不确定性(部署可以在构建完成后立即开始,或在延长的测试之后开始)

质量门

当时序推理任务在以下方面都完成时:

  • 所有区间以显式的开始/结束边界表示(没有点事件)
  • 每对区间具有非空的13种关系弧标签
  • 约束传播已运行完成(没有待处理的传递性更新)
  • 所有析取标签如实反映当前证据(没有任意承诺)
  • 为多尺度推理建立了参考区间层级
  • 持久性默认值在适用处标记为可废止
  • 不一致检测已验证(空标签被捕获并报告)
  • 显式定义了跨参考范围桥接
  • 所有时序断言可追溯到证据来源
  • 查询范围与推理粒度级别相匹配

不适用边界

不要在此情况下使用Allen的区间代数:

  • 简单时间戳比较 → 使用算术:if (t1 < t2)
  • 度量持续时长计算 → 使用日历算术:end_date - start_date
  • 实时调度优化 → 使用带有数值域的约束满足
  • 概率时序推理 → 使用时序概率网络
  • 连续时间动态 → 使用微分方程
  • 高频事件流 → 使用流处理框架

改用:

  • 对于度量约束:使用数值域约束满足
  • 对于概率时序推理:使用贝叶斯时序网络
  • 对于实时系统:使用速率单调调度
  • 对于连续动态:使用时序逻辑模型检查**
  • 对于事件流处理:使用复杂事件处理

Allen擅长: 在增量知识更新和层级范围管理下,处理带有不确定性的定性时间推理。

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.

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