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curiositech/windags-skills

選擇多智能體架構,採用環境優先分析、協調壓力、承諾調校與知識層級。適用於自主性設計、協議選擇與協調失敗診斷。不適用於單一智能體規劃、集中式排程器或僅靠提示的群體。

windags-skills 是什麼?

此技能基於 Wooldridge 多智能體系統理論,提供一套嚴謹的方法論,用於分析與設計具有真正自主性的多智能體系統。它的核心主張是:智能體架構的選擇應由環境的屬性(如可觀測性、確定性、動態性)驅動,而非開發者的偏好。技能詳細說明了何時使用反應式、慎思式或混合式架構,並深入探討協調壓力(來自局部可觀測性、資源競爭與任務相依性)如何影響協議設計。此外,它引入了「承諾策略」與「知識層級」(個體知識、共同知識、共通知識與分散知識)等關鍵概念,幫助開發者避免常見的設計陷阱,例如為協作而協作、隱藏的中央控制器或不可能的知識假設。支援的平台包括 Claude Code、Cursor 等代理開發工具,適用於需要評估與改善多智能體系統架構的場景。

相容平台✓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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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

Wooldridge 多智能體系統

請在以下情況使用此技能:問題不是「如何讓多個模型對話」,而是「這種環境強迫我建立哪一種自主系統?」

使用時機

  • 您必須在反應式、慎思式或混合式智能體架構之間做出選擇。
  • 協調持續失敗,因為沒有人擁有完整資訊或無爭議的控制權。
  • 智能體需要在不確定性下進行協商、分配或承諾策略。
  • 設計取決於智能體知道什麼、大家知道什麼,或者共通知識是否可達成。
  • 您需要診斷一個「多智能體」設計是否實際上只是非同步的物件編排。

不適用於

  • 無協調需求的單一智能體規劃或最佳化問題。
  • 集中式排程器,其中一個控制器已擁有全域決策權。
  • 僅靠提示的角色扮演群體,它們沒有獨立的目標、信念或控制邊界。
  • 當智能體自主性與知識不對稱性不重要時的一般分散式系統設計。

核心心智模型

環境屬性驅動架構

從可觀測性、確定性、動態性與時間壓力開始。架構選擇取決於環境的形狀,而非個人對流行智能體模式的偏好。

自主性是控制反轉

智能體不僅僅是非同步物件。它們決定是否以及何時遵守,這意味著作請求需要語義、拒絕與協調邏輯,而不是隱含的服從。

協調源於約束

局部可觀測性、資源競爭與相互依賴的目標會產生協調壓力。良好的協議設計從命名這種壓力開始,而不是假設協作總是可取的。

承諾策略必須匹配波動性

大膽的智能體在快速變化的環境中過度承諾;謹慎的智能體在穩定的環境中搖擺不定。重新考慮的頻率是一個與環境動態相關的設計參數,而非通用的最佳實踐。

知識層級至關重要

個體知識、大家知道、共通知識與分散知識不可互換。要求錯誤的層級可能使協議不可能實現或代價高昂。

決策點

flowchart TD
  A[需要智能體架構] --> B{環境完全可觀測且穩定?}
  B -->|是| C[簡單的反應式或規劃器為主的設計可能足夠]
  B -->|否| D{需要在不確定性下即時反應?}
  D -->|是| E[混合式架構]
  D -->|否| F[慎思式架構]
  E --> G{為什麼智能體需要協調?}
  F --> G
  G -->|部分資訊| H[資訊共享協議]
  G -->|資源競爭| I[協商或分配機制]
  G -->|任務相依性| J[承諾或委派協議]
  G -->|無真正的自主性| K[使用更簡單的集中式設計]
  • 在選擇智能體架構之前,先描述環境特性。
  • 如果智能體不能拒絕、延遲或重新解釋請求,就不要假裝擁有智能體自主性。
  • 刻意選擇知識需求。許多系統需要分散知識,而非共通知識。
  • 根據環境的變化率和重新考慮的成本來調整承諾的大膽程度。

失敗模式

架構優先設計

徵兆:團隊在任何人能描述環境之前就想要 BDI、辯論或一個花俏的智能體框架。

修正:先強制進行環境描述。

擬人化自主性

徵兆:設計中談論信念與慾望,但卻無法將它們映射到局部狀態、觀測或協議義務。

修正:將心智語言根植於具體的計算狀態。

為協作而協作

徵兆:通訊流量增加,但無人能解釋哪種環境壓力需要它。

修正:識別真正的驅動因素,並設計解決該問題的最小協議。

隱藏的中央控制器

徵兆:一個「智能體」默默地做出所有重要決策,而其他智能體只是執行。

修正:承認架構是集中式的,或者重新分配真正的決策權。

不可能的知識假設

徵兆:協議的正確性依賴於每個智能體都知道其他每個智能體都知道某個事實,但網路不可靠。

修正:在環境允許的情況下,放寬為分散知識、確認或最終一致性。

實際範例

專家 LLM 審查群體

一個程式碼系統使用安全、效能與風格智能體。安全審查依賴於與風格審查不同的證據,且沒有任何單一審查者擁有全域可見性。使用混合協調器加上有針對性的資訊共享,而不是假設每個審查者都應該看到整個上下文。

分散式車輛監控

感測器智能體覆蓋重疊但不完整的區域。由於沒有單一智能體能維持端到端的軌跡連續性,因此需要協調。圍繞局部可觀測性而非通用的「協作」來設計協議。

品質閘門

  • 在選擇架構之前,環境已被明確描述。
  • 設計明確指出協調壓力的真正來源。
  • 知識需求以正確的層級陳述。
  • 承諾策略與波動性和重新考慮成本相關聯。
  • 任何聲稱的自主性都包括拒絕、延遲或對請求的局部解釋。

識別標語

  • 如果某人將一個系統稱為多智能體,但每個重要決策仍然透過一個控制器路由,那他們只是在重新命名分散式執行,而不是在設計自主性。
  • 如果「共通知識」被隨意使用而沒有溝通模型,那麼協議很可能未充分指定。
  • 如果團隊無法說明為什麼智能體必須協調,那麼設計很可能根本不需要智能體。

參考路由

  • references/environment-characterization-drives-architecture.md:當架構選擇是主要問題時載入。
  • references/coordination-as-necessity-not-luxury.md:當您必須證明或最小化協調時載入。
  • references/commitment-strategies-and-environment-dynamics.md:當大膽程度或重新規劃節奏是主要調整問題時載入。
  • references/grounded-epistemic-logic-for-distributed-agents.md:當知識主張影響安全性或正確性時載入。
  • references/negotiation-and-resource-allocation-mechanisms.md:當自主性與稀缺資源衝突時載入。

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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