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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).

Qu'est-ce que windags-skills ?

windags-skills is a Antigravity agent skill that 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).

Compatible avec~Claude Code~Codex CLI~Cursor✓Antigravity
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/agentic-calendar-coordination

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Documentation

Agentic Calendar Coordination

Build AI agents that negotiate meetings, protect focus time, and infer context from calendar data. Navigate timezone complexity, implement agent-to-agent scheduling, and architect custom calendar logic using Google Calendar API and MCP.

Decision Points

When Conflict Detected

Incoming meeting request conflicts with existing event
├─ Sender = manager/exec → Auto-accept, suggest moving existing
├─ Meeting = 1:1/urgent tagged → Propose alternative within 24h
├─ Focus block collision
│  ├─ Block priority = high (deep work) → Auto-decline
│  └─ Block priority = medium → Propose alternative time
└─ Double-booking with peer meeting → Counter with 3 alternative slots

Auto-Decline vs. Propose-Alternative Decision

Factors: sender_tier (exec/manager/peer/external) × meeting_type (1:1/team/all-hands) × focus_block_priority
If exec + any_type → Always propose alternative
If manager + (1:1 OR urgent) → Propose alternative
If peer + recurring + low_attendance → Auto-decline
If external + no_prior_relationship → Auto-decline with "use calendar link"
If during high_priority_focus_block → Auto-decline regardless of sender

Timezone-Aware Slot Selection

Multi-participant scheduling:
├─ All same timezone → Use local working hours (9-17)
├─ 2 timezones, <6h apart → Find overlap window
├─ 2 timezones, >6h apart → Early/late split (one takes 8am, other takes 6pm)
└─ 3+ timezones → Propose async alternative OR rotating schedule

Energy Model Slot Ranking

Time of day priority (descending):
1. 9-11 AM (peak cognitive) → Reserve for deep work, decline routine meetings
2. 11 AM-12 PM (sustained focus) → Allow important meetings only
3. 2-4 PM (collaboration sweet spot) → Prefer for team meetings
4. 4-5 PM (wrap-up) → Good for 1:1s, status updates
5. 1-2 PM (post-lunch dip) → Avoid complex meetings

Failure Modes

Timezone-Aware Recurring Conflicts

Symptoms: Weekly 9 AM meeting shows conflicts in winter but not summer; cross-timezone recurring fails after DST change Detection: if recurring_event && timezone_conversion && conflict_pattern_seasonal Fix: Always expand recurring with singleEvents: true, convert each instance to UTC for conflict checking, re-evaluate after DST transitions

Buffer Enforcement Thrashing

Symptoms: Agent repeatedly reschedules same meeting trying to create buffers; meetings bounce between slots Detection: if same_meeting_rescheduled > 2 times in 24h for buffer_reasons Fix: Lock in meeting after first reschedule, adjust buffer requirements dynamically based on meeting importance

Energy Model Cold-Start

Symptoms: New agent schedules meetings at user's worst cognitive times; ignores established patterns Detection: if meeting_satisfaction_score < 3 for meetings_scheduled_by_agent Fix: Bootstrap with explicit user preferences, learn from decline patterns, require 2-week observation period before auto-scheduling

Schema Bloat Privacy Leak

Symptoms: Agent shares too much calendar detail with external systems; event descriptions leak to LLMs Detection: if external_api_call contains event.description OR attendee.email Fix: Implement data minimization layer, strip PII before external calls, use free/busy only for scheduling

Recursive Negotiation Loop

Symptoms: Two agents endlessly counter-propose; no convergence on meeting time Detection: if negotiation_rounds > 3 && no_accepted_slot Fix: Escalate to human after 2 counters, implement "good enough" acceptance threshold, time-bound negotiations

Worked Examples

Cross-Timezone Meeting Negotiation

Scenario: Your agent (Chicago) needs 30min with peer agent (Tokyo) for API review. Both have energy constraints.

Step 1: Initial Analysis

  • Chicago: 9 AM-5 PM CST = 12 AM-6 AM JST (next day)
  • Tokyo: 9 AM-6 PM JST = 7 PM-4 AM CST (previous day)
  • Overlap window: 7-8 PM CST = 9-10 AM JST

Step 2: Energy Model Check

  • Chicago 7 PM = end of workday (energy: 6/10)
  • Tokyo 9 AM = peak cognitive hours (energy: 9/10)
  • Imbalance detected → Need compromise

Step 3: Proposal Generation

Agent Chicago → Agent Tokyo: PROPOSE
Slots: [
  "2024-03-19T01:00Z" (Chi: 7PM, Tok: 10AM),
  "2024-03-19T23:00Z" (Chi: 5PM, Tok: 8AM),  
  "2024-03-20T00:00Z" (Chi: 6PM, Tok: 9AM)
]
Priority: normal
Topic: "API review - timezone complexity discussion"

Step 4: Counter-Proposal

Agent Tokyo → Agent Chicago: COUNTER
Reason: "Prefer later Tokyo morning for complex technical discussion"
Alternatives: [
  "2024-03-19T02:00Z" (Chi: 8PM, Tok: 11AM),
  "2024-03-20T01:00Z" (Chi: 7PM, Tok: 10AM)
]
Constraints: "Need 15min buffer before 11:30 AM JST standup"

Step 5: Resolution Chicago agent accepts 8 PM CST / 11 AM JST slot, creates event with both timezones in description: "API Review - 8:00 PM CST / 11:00 AM JST+1"

Novice vs Expert:

  • Novice: Schedules at Chicago-convenient time without checking Tokyo cognitive hours
  • Expert: Recognizes energy imbalance, proposes late-in-Chicago-day when Tokyo is fresh

Quality Gates

  • All timestamps stored in UTC internally, converted only at display/API boundaries
  • IANA timezone names used throughout (no UTC offset strings like "-05:00")
  • Free/busy queries strip event details, sharing only time blocks with external agents
  • Buffer enforcement creates minimum 10-15 minutes between consecutive meetings
  • Focus blocks marked as "busy" but with lower priority than actual meetings for negotiation
  • Energy model considers user's cognitive peak hours and meeting fatigue patterns
  • OAuth tokens encrypted at rest with automatic refresh flow implemented
  • Rate limiting handles Google Calendar API quotas with exponential backoff
  • Recurring events expanded to individual instances before conflict detection
  • Multi-calendar merging tested across personal/work calendars with different permissions
  • Agent negotiations time-bound with human escalation after failed rounds
  • Audit log captures all calendar mutations with rollback capability

NOT-FOR Boundaries

This skill is NOT for:

  • Building calendar UI components → Use form-validation-architect
  • Project Gantt charts or milestone tracking → Use project-management-guru-adhd
  • Pomodoro timers or time tracking apps → Use adhd-daily-planner
  • General agent architecture patterns → Use agentic-patterns
  • Building the base agent framework → Use agent-creator

Delegate to other skills when:

  • User needs ADHD-specific time management → adhd-daily-planner
  • Building persistent agent memory → always-on-agent-architecture
  • Multi-agent coordination beyond calendar → multi-agent-coordination
  • Background daemon architecture → daemon-development

This skill focuses specifically on calendar data structures, scheduling algorithms, and meeting coordination logic.

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

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

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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