agentdb-learning-plugins
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
brainstorming
Socratic questioning protocol + user communication. MANDATORY for complex requests, new features, or unclear requirements. Includes progress reporting and error handling.
workflow-composer
Chain Vibery commands, skills, and agents into repeatable automated workflows. Outputs: Executable workflow files, hook configurations, CI/CD integration. Use when user has repetitive multi-step processes to automate. Triggers: create workflow, automate this process, chain these commands, /workflow
web-ui-link
Generate URLs for the LimaCharlie web application interface. Quickly open the web UI for any feature: dashboard, sensors, detections, D&R rules, FP rules, secrets, outputs, lookups, payloads, YARA rules, artifacts, investigations, extensions, adapters, installation keys, billing, users, playbooks, AI agents, and more. For sensor-specific pages: timeline, console, processes, network, file-system, live-feed. For groups: members, organizations, permissions. Use for "open dashboard", "link to detections", "web UI for sensor", "open D&R rules page", "browser link", "app link", "open in web", "show me URL for", "go to".
create-memo
This skill helps capture unstructured thoughts (via text or voice) across multiple turns and converts them into structured memos that are saved to a Notion inbox database. Use when the user wants to create a memo, capture thoughts, or provide content that should be documented verbatim without analysis or response.
sensor-tasking
Send tasks (commands) to EDR sensors to gather data or take action. Handles offline agents via reliable-tasking, collects responses via LCQL queries, and creates D&R rules for automated response handling. Use for live response, data collection, forensic acquisition, or fleet-wide operations like "get OS version from all Windows servers" or "isolate all hosts with tag X".
cc-pai-optimiser
Review and optimize PAI (Personal AI Infrastructure) codebases as Claude Code evolves. Use when analyzing PAI repositories against 12-factor agent principles, checking for Claude Code feature compatibility, auditing context management patterns, or generating upgrade recommendations. Triggers on requests involving PAI optimization, Claude Code feature adoption, agent architecture review, or context engineering improvements.
speculator-orchestration
Multi-agent task orchestration using the spec CLI. Guides agents on claiming tasks, parallel execution, blocking, and coordination. Use when working with multiple agents, claiming tasks, or managing parallel work.
moai-alfred-expertise-detection
Enterprise AI-powered user expertise detection with behavioral analysis, communication pattern recognition, code complexity assessment, Context7 integration, and adaptive response calibration; activates for personalized guidance generation, complexity adjustment, tutorial depth selection, and communication style matching
rag-pipeline
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
openapi-toolkit-ollama-dart
Automates updating ollama_dart when Ollama OpenAPI spec changes. Fetches latest spec, compares against current, generates changelogs and prioritized implementation plans. Use for: (1) Checking for API updates, (2) Generating implementation plans for spec changes, (3) Creating new models/endpoints from spec, (4) Syncing local spec with upstream. Triggers: "update api", "sync openapi", "new endpoints", "api changes", "check for updates", "update spec", "api version", "fetch spec", "compare spec", "what changed in the api", "implementation plan".
spawn-agent
Spawn PAI agents via MCP factory tool. Loads identity, injects RAG context, validates spawn chain, and executes via Task(). The bridge between MCP tools and Claude Code's agent spawning.
debug-mode
Hypothesis-driven debugging with hybrid dual-track parallel execution (Opus 4.5 + GPT 5.2). Spawns two independent chains of subagents where each reviews and improves upon its own previous work, then synthesizes findings from both tracks. Use when debugging hard-to-reproduce bugs, CI/E2E test failures, flaky tests, or when standard fixes have failed.
moai-cc-settings
Claude Code settings management, preference customization, and user experience optimization. Use when customizing Claude Code behavior, managing user preferences, or optimizing development experience.
context-engineering
Master context engineering for AI agents - token optimization, degradation patterns, compression, memory systems, multi-agent coordination, evaluation. Use when designing agents, debugging context failures, or building LLM pipelines.
domain-phd-expert
Creates PhD-grade expert agents when no suitable agent exists. Produces highly specialized agents with deep domain expertise.
lesson-creator
Creates comprehensive, detailed lesson plans for AI/prompt engineering classes. Use when the user asks to create a new lesson, develop class content, or needs a detailed teaching plan for a session. Generates minute-by-minute breakdowns with activities, differentiation, and assessment.
skill-builder
Guide users through creating new Claude Code Skills with proper structure, validation, and best practices
