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curiositech/windags-skills

以 AgentSpeak(L) 風格設計具備上下文防護(context guard)計畫、選擇函數與意圖堆疊的 BDI 代理架構。適用於可中斷的自動化、代理策略以及在動態環境中的多代理協調。不適用於簡單規則引擎、靜態規劃器或集中式工作流程。

windags-skills 是什麼?

此技能提供一個完整的 BDI(信念-願望-意圖)架構設計指南,靈感來自 AgentSpeak(L) 形式模型。它幫助開發者建構能同時兼顧反應性與持續承諾的自主代理:代理維護世界狀態的信念、候選目標的願望,以及已提交資源的具體意圖堆疊。這些分離的表示使代理行為不僅可檢查,還能解釋為何選擇特定任務或計畫。技能強調將策略(透過選擇函數)與領域知識(透過計畫與情境防護)分離,讓急迫性、公平性或風險容忍度等政策可獨立於領域邏輯變更。情境感知計畫讓相同目標能根據當前信念觸發不同行為,而意圖的可中斷特性則允許代理暫停長時間任務處理緊急中斷,事後無縫恢復。工作範例涵蓋多中斷編碼助手與無中央調度器的倉庫協調等場景。技能也提供決策流程圖、常見失敗模式(如目標-意圖崩潰、政策隱藏於計畫、單體計畫、失敗視為當機而非事件、無根據的情境防護)及具體的修復建議。它還包括質量關卡(如信念、願望、意圖需分開表示;每個事件至少有一個帶情境防護的計畫;失敗路徑應觸發事件或復原計畫)以及行話檢查(如無法區分「目標」與「已承諾的意圖」、「策略」變更需要改寫計畫本體、代理無法說出被什麼中斷及接下來要恢復什麼)。最後提供五份參考指南,分別深入探討代理週期、計畫結構、選擇函數、意圖管理與失敗模式。

相容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/agentspeak-l-bdi-agents-speak-out-in-a-logical-computable

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說明文件

AgentSpeak(L) BDI 架構

當困難點不在於編寫另一個工作流程,而是在於決定自主代理在變動條件下該如何反應、承諾、暫停工作並解釋其選擇時,請使用此技能。

使用時機

  • 系統必須對新事件做出反應,但不能放棄更長期的任務承諾。
  • 您需要一個計畫庫,其中包含可根據當前上下文選擇的重複使用行為,而非單一的大型控制器。
  • 代理策略必須與領域知識分離,以便在不重寫計畫的情況下改變急迫性、公平性或風險容忍度。
  • 失敗應觸發復原行為與重新規劃,而非導致整個代理崩潰。
  • 您需要一個可檢查的心智模型,以了解代理為何選擇某個任務或計畫而非另一個。

不適用於

  • 簡單規則引擎,不需要持久承諾或意圖堆疊。
  • 靜態規劃問題,環境不會中斷執行。
  • 集中式工作流程系統,已有一個排程器決定每一步。
  • 僅提示詞的代理循環,從不分別表示信念、計畫或策略。

核心心智模型

信念、願望與意圖是不同的物件

信念模擬當前世界,願望定義候選結果,而意圖是當前正在消耗執行預算的具體已承諾計畫堆疊。如果這些概念合併為一塊,代理將變得無法解釋。

計畫是情境化的知識

AgentSpeak 計畫並非通用程序。它們是由事件觸發的食譜(recipes),帶有上下文防護條件,因此同一個目標可以根據代理當前所相信的內容而觸發不同的行為。

選擇函數承載策略

事件選擇器、選項選擇器與意圖選擇器是急迫性、公平性與風險容忍度所屬之處。計畫應該編碼「如何做」;選擇函數則應編碼「策略」。

可中斷性是一項功能,而非錯誤

意圖是部分執行的堆疊,可以交錯、暫停與恢復。這使得具有反應能力的代理能夠處理中斷,而無需將每個長時間任務都變成重新啟動。

從運作中的系統向上形式化

從 AgentSpeak(L) 中學到的實際教訓是:從一個可運作的代理循環開始,然後形式化它實際所做的行為。不要編寫一個優雅的抽象理論,之後再將其近似為執行時行為。

決策點

flowchart TD
  A[需要自主行為設計] --> B{需要持久承諾?}
  B -->|否| C[使用規則、狀態機或簡單工作流程]
  B -->|是| D{環境會中斷正在進行的工作?}
  D -->|否| E[傳統規劃器可能足夠]
  D -->|是| F{需要將策略與計畫分離?}
  F -->|否| G[自訂代理循環,但預期會緊密耦合]
  F -->|是| H[使用 AgentSpeak 風格的 BDI]
  H --> I{當前問題}
  I -->|選擇了錯誤的任務| J[重新檢視 SI 或 SE]
  I -->|選擇了錯誤的計畫| K[重新檢視 SO 或上下文防護]
  I -->|失敗導致執行中斷| L[添加失敗事件與復原計畫]
  I -->|難以審計| M[公開意圖堆疊與選擇軌跡]
  • 當系統必須平衡反應性與承諾,而非只選擇其一時,請使用 AgentSpeak 風格的建模。
  • 將世界假設放在信念查詢與計畫防護中,而非深埋在動作內部的條件判斷中。
  • 如果策略改變但領域知識不變,請在重寫計畫之前先更改選擇函數。
  • 將長時間任務分解為子目標,以便意圖堆疊保持可檢查與可中斷。

失敗模式

目標-意圖崩潰

提示:設計中說代理「有一個目標」,但無法顯示它是否真正針對該目標提交了資源。

修復:分別表示候選目標與當前活躍的意圖堆疊。

策略隱藏在計畫中

提示:每個計畫本體都包含優先級、公平性或急迫性的分支邏輯。

修復:將這些選擇移至事件、選項或意圖選擇函數中。

單體計畫

提示:計畫過長,以致任何中斷都迫使整個序列在心智上重新啟動。

修復:將長行為拆分為較短的計畫,並帶有明確的子目標邊界。

失敗視為當機,而非事件

提示:子目標失敗導致整個代理中止或悄無聲息地消失。

修復:將失敗建模為一個可以觸發復原、重試或放棄計畫的事件。

無根據的上下文防護

提示:計畫基於模糊的世界假設進行匹配,而這些假設從未與真實的信念或觀察相關聯。

修復:使信念更新路徑明確,並保持防護條件可針對當前信念狀態進行查詢。

工作範例

具備緊急中斷功能的工具型 LLM 代理

一個程式碼代理正在進行長時間的重構工作,此時收到一個高嚴重性的生產環境警報。將重構建模為一個意圖堆疊,將警報建模為一個新事件。讓選擇函數先佔重構任務,之後再恢復它,而不遺失狀態。

無中央調度器的倉庫協調

多個移動代理共享關於通道擁塞與庫存狀態的信念庫。它們的領域知識保留在計畫中,而選擇函數則編碼在擁塞情況下哪些緊急揀貨任務優於補貨工作。

質量關卡

  • 信念、願望與意圖被分別表示。
  • 每個主要觸發事件至少有一個帶有明確上下文防護的計畫。
  • 設計中明確指出 SE、SO 與 SI 策略所處的位置。
  • 失敗路徑會建立事件或復原計畫,而非無聲崩潰。
  • 審查者可以檢查活躍的意圖並解釋為何選擇一個計畫而非另一個。

行話檢查

  • 如果有人無法解釋「代理想要的目標」與「代理已承諾的意圖」之間的差異,則他們尚未內化這個模型。
  • 如果「策略」改變需要重寫計畫本體,代表策略與知識從未被分離。
  • 如果代理無法說出是什麼中斷了它,以及接下來要恢復什麼,則它並非真正在使用意圖堆疊。

參考資料路由

  • references/bdi-architecture-for-agent-orchestration.md:載入以了解完整的代理循環以及 B/D/I 之間的交互。
  • references/context-sensitive-plans-as-agent-knowledge.md:當計畫結構與防護設計是主要問題時載入。
  • references/selection-functions-as-agent-policy.md:當優先級、公平性或風險容忍度需要明確的策略處理時載入。
  • references/intention-management-and-goal-decomposition.md:當暫停/恢復、子目標或意圖可審計性為核心時載入。
  • references/failure-modes-in-bdi-systems.md:當現有設計已存在且您正在除錯故障時載入。

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

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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