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curiositech/windags-skills

基于环境优先分析、协调压力、承诺调整和知识层级来选择多智能体架构。适用于自主性设计、协议选择和协调失效分析。不适用于单智能体规划、集中调度器或纯提示词蜂群。

windags-skills 是什么?

这个技能将多智能体系统的设计从“选择一种时髦的框架”转变为“根据环境约束进行严谨的架构决策”。它提供了一套系统的思维模型,包括环境属性如何决定架构、自主性意味着控制反转、协调来源于约束而非假设、承诺策略必须匹配环境动态性、以及知识层级(个体知识、共同知识、分布式知识)的精确选择。该技能通过决策树引导用户从环境特性出发选择反应式、慎思式还是混合式架构,并识别了五种常见失败模式(如架构优先设计、拟人化自主性、无目的协调、隐藏的集中控制器、不可能的知识假设)。它适用于任何需要真正自主性和协作的场景,如代码审查蜂群或分布式车辆监控,并提供了明确的质量门禁和行话识别来确保设计严谨性。主要平台支持包括使用LLM进行架构咨询或自主代码生成的通用AI开发工具。

兼容平台✓Claude Code~Codex CLI~Cursor✓Antigravity
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/ai-wiley-wooldridge-an-introduction-to-multi-agent-systems

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文档

Wooldridge 多智能体系统

当问题不再是“如何让多个模型对话”,而是“这个环境迫使我构建什么样的自治系统”时,请使用此技能。

何时使用

  • 你必须在反应式、慎思式或混合式智能体架构之间做出选择。
  • 协调不断失败,因为没有人拥有完整信息或无可争议的控制权。
  • 智能体在不确定性下需要谈判、分配或承诺策略。
  • 设计取决于智能体知道什么、每个人都知道什么,或者共同知识是否可实现。
  • 你需要诊断一个“多智能体”设计是否实际上只是异步对象编排。

不适用于

  • 没有协调需求的单智能体规划或优化问题。
  • 集中调度器,其中一个控制器已拥有全局决策权。
  • 纯提示词角色扮演蜂群,它们没有独立的目标、信念或控制边界。
  • 当智能体自主性和知识不对称性无关紧要时的一般分布式系统设计。

核心思维模型

环境属性驱动架构

从可观察性、确定性、动态性和时间压力开始。架构选择是环境形状的下游产物,而非个人对时髦智能体模式的偏好。

自主性是控制反转

智能体不仅仅是异步对象。它们决定是否以及何时服从,这意味着请求需要语义、拒绝和协调逻辑,而不是隐含的服从。

协调源于约束

部分可观察性、资源竞争和相互依赖的目标产生了协调压力。好的协议设计始于命名这种压力,而不是假设协作总是可取的。

承诺策略必须匹配动态性

激进的智能体在快速变化的环境中过度承诺;谨慎的智能体在稳定的环境中摇摆不定。重新考虑频率是一个与环境动态性相关的设计参数,而非普遍的最佳实践。

知识层级很重要

个体知识、人人皆知、共同知识和分布式知识不可互换。要求错误的层级可能使协议不可能实现或代价过高。

决策点

flowchart TD
  A[需要智能体架构] --> B{环境完全可观察且稳定?}
  B -->|是| C[简单的反应式或基于规划器的设计可能足够]
  B -->|否| D{需要在不确定性下实时反应?}
  D -->|是| E[混合架构]
  D -->|否| F[慎思式架构]
  E --> G{智能体为何需要协调?}
  F --> G
  G -->|部分信息| H[信息共享协议]
  G -->|资源竞争| I[谈判或分配机制]
  G -->|任务依赖| J[承诺或委托协议]
  G -->|无真正自主性| K[使用更简单的集中式设计]
  • 在选择智能体架构之前先描述环境。
  • 如果智能体不能拒绝、延迟或重新解释请求,就不要假装拥有智能体自主性。
  • 有意识地选择知识需求。许多系统需要分布式知识,而非共同知识。
  • 根据环境变化率和重新考虑成本调整承诺大胆程度。

失败模式

架构优先设计

线索:团队在任何人能描述环境之前就想用BDI、辩论或花哨的智能体框架。

修复:强制先进行环境描述。

拟人化自主性

线索:设计谈论信念和欲望,但无法将它们映射到局部状态、观察或协议义务。

修复:将心智语言建立在具体的计算状态之上。

为协调而协调

线索:通信流量上升,但没人能解释哪种环境压力导致了这种通信。

修复:识别真正的驱动力,并设计解决它的最小协议。

隐藏的集中控制器

线索:一个“智能体”默默做出所有重要决策,而其他智能体只是执行。

修复:要么承认架构是集中的,要么重新分配真正的决策权。

不可能的知识假设

线索:协议的正确性依赖于每个智能体都知道其他每个智能体知道某个事实,但网络是不可靠的。

修复:根据环境允许,放宽为分布式知识、确认或最终一致性。

工作示例

专家LLM审查蜂群

一个编码系统使用安全、性能和风格智能体。安全审查依赖于与风格审查不同的证据,没有一个审查者拥有全局可见性。使用混合协调器加有针对性的信息共享,而不是假设每个审查者都应看到整个上下文。

分布式车辆监控

传感器智能体覆盖重叠但不完整的区域。协调是必要的,因为没有单个智能体可以维持端到端的轨迹连续性。围绕部分可观察性而非通用“协作”来设计协议。

质量门禁

  • 在架构选择之前明确描述环境。
  • 设计指出协调压力的真正来源。
  • 知识需求在正确的层级上陈述。
  • 承诺策略与波动性和重新考虑成本相关联。
  • 任何声称的自主性都包括拒绝、延迟或对请求的局部解释。

行话识别

  • 如果有人称一个系统为多智能体,但每个重要决策仍然通过一个控制器,他们是在重新命名分布式执行,而不是设计自主性。
  • 如果“共同知识”在没有通信模型的情况下被随意使用,协议很可能是不明确的。
  • 如果团队说不出智能体为什么必须协调,那么设计可能根本不需要智能体。

参考路由

  • references/environment-characterization-drives-architecture.md:当架构选择是主要问题时加载。
  • references/coordination-as-necessity-not-luxury.md:当必须证明协调的合理性或最小化协调时加载。
  • references/commitment-strategies-and-environment-dynamics.md:当大胆程度或重新规划频率是主要调整问题时加载。
  • references/grounded-epistemic-logic-for-distributed-agents.md:当知识主张影响安全性或正确性时加载。
  • references/negotiation-and-resource-allocation-mechanisms.md:当自主性与稀缺资源冲突时加载。

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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