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curiositech/windags-skills

>- Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.

windags-skills とは?

windags-skills is a Claude Code agent skill that >- Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.

対応✓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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ドキュメント

Wooldridge Multi-Agent Systems

Use this skill when the question is not "how do I make several models talk," but "what kind of autonomous system does this environment force me to build?"

When to Use

  • You must choose between reactive, deliberative, or hybrid agent architectures.
  • Coordination keeps failing because no one has complete information or uncontested control.
  • Agents need negotiation, allocation, or commitment strategies under uncertainty.
  • A design depends on what agents know, what everyone knows, or whether common knowledge is even achievable.
  • You need to diagnose whether a "multi-agent" design is actually just asynchronous object orchestration.

NOT for

  • Single-agent planning or optimization problems with no coordination requirement.
  • Centralized schedulers where one controller already owns global decision rights.
  • Prompt-only roleplay swarms that do not have separate goals, beliefs, or control boundaries.
  • General distributed-systems design when agent autonomy and knowledge asymmetry are irrelevant.

Core Mental Models

Environment Properties Drive Architecture

Start with observability, determinism, dynamics, and time pressure. Architecture choice is downstream of environment shape, not personal preference for a fashionable agent pattern.

Autonomy Is Control Inversion

Agents are not just asynchronous objects. They decide whether and when to comply, which means requests need semantics, refusals, and coordination logic rather than implicit obedience.

Coordination Emerges from Constraint

Partial observability, resource contention, and interdependent goals create coordination pressure. Good protocol design begins by naming that pressure instead of assuming collaboration is always desirable.

Commitment Strategy Must Match Volatility

Bold agents overcommit in fast-changing environments; cautious agents thrash in stable ones. Reconsideration frequency is a design parameter tied to environment dynamics, not a universal best practice.

Knowledge Levels Matter

Individual knowledge, everyone-knows, common knowledge, and distributed knowledge are not interchangeable. Asking for the wrong level can make a protocol impossible or wastefully expensive.

Decision Points

flowchart TD
  A[Need agent architecture] --> B{Environment fully observable and stable?}
  B -->|Yes| C[Simple reactive or planner-heavy design may suffice]
  B -->|No| D{Need real-time reaction under uncertainty?}
  D -->|Yes| E[Hybrid architecture]
  D -->|No| F[Deliberative architecture]
  E --> G{Why do agents need to coordinate?}
  F --> G
  G -->|Partial information| H[Information-sharing protocol]
  G -->|Resource contention| I[Negotiation or allocation mechanism]
  G -->|Task dependency| J[Commitment or delegation protocol]
  G -->|No real autonomy| K[Use a simpler centralized design]
  • Characterize the environment before choosing an agent architecture.
  • If agents cannot refuse, delay, or reinterpret requests, do not pretend you have agent autonomy.
  • Choose knowledge requirements intentionally. Many systems need distributed knowledge, not common knowledge.
  • Tune commitment boldness to the environment's change rate and the cost of reconsideration.

Failure Modes

Architecture-First Design

Cue: the team wants BDI, debate, or a fancy agent framework before anyone can describe the environment.

Fix: force environment characterization first.

Anthropomorphic Autonomy

Cue: the design talks about beliefs and desires but cannot map them to local state, observations, or protocol obligations.

Fix: ground mental language in concrete computational state.

Coordination for Its Own Sake

Cue: communication traffic rises, but no one can explain which environmental pressure requires it.

Fix: identify the actual forcing function and design the minimal protocol that addresses it.

Hidden Central Controller

Cue: one "agent" silently makes all important choices while the others just execute.

Fix: either admit the architecture is centralized or redistribute genuine decision rights.

Impossible Knowledge Assumptions

Cue: protocol correctness depends on every agent knowing that every other agent knows a fact, but the network is unreliable.

Fix: relax to distributed knowledge, acknowledgments, or eventual consistency as the environment allows.

Worked Examples

Specialist LLM Review Swarm

A coding system uses security, performance, and style agents. Security reviews depend on different evidence than style review, and no single reviewer has global visibility. Use a hybrid coordinator plus targeted information-sharing instead of assuming every reviewer should see the entire context.

Distributed Vehicle Monitoring

Sensor agents cover overlapping but incomplete regions. Coordination is necessary because no single agent can maintain end-to-end track continuity. Design the protocol around partial observability rather than generic "collaboration."

Quality Gates

  • The environment is characterized explicitly before architecture choice.
  • The design names the real source of coordination pressure.
  • Knowledge requirements are stated at the right level.
  • Commitment strategy is tied to volatility and reconsideration cost.
  • Any claimed autonomy includes refusal, delay, or local interpretation of requests.

Shibboleths

  • If someone calls a system multi-agent but every important decision still routes through one controller, they are renaming distributed execution, not designing autonomy.
  • If "common knowledge" is used casually with no communication model, the protocol is probably underspecified.
  • If the team cannot say why agents must coordinate, the design probably does not need agents at all.

Reference Routing

  • references/environment-characterization-drives-architecture.md: load when architecture choice is the main question.
  • references/coordination-as-necessity-not-luxury.md: load when you must justify or minimize coordination.
  • references/commitment-strategies-and-environment-dynamics.md: load when boldness or replanning cadence is the main tuning issue.
  • references/grounded-epistemic-logic-for-distributed-agents.md: load when knowledge claims drive safety or correctness.
  • references/negotiation-and-resource-allocation-mechanisms.md: load when autonomy collides with scarce resources.

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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