Loading...
Loading...
Found 114 Skills
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Dispatch independent subagents in parallel for unrelated problems spanning different subsystems. Use when 2+ failures have independent root causes, multiple subsystems are broken independently, or user requests concurrent investigation. Use for "parallel", "multiple failures", "independent bugs", "fix these concurrently". Do NOT use for related failures, shared-state problems, or exploratory debugging where root cause is unknown.
Agent skill for collective-intelligence-coordinator - invoke with $agent-collective-intelligence-coordinator
Agent skill for swarm-issue - invoke with $agent-swarm-issue
Use when an approved current phase has 3 or more independent ready tasks and parallel execution will materially reduce cycle time. Orchestrates bounded workers, monitors blockers and file conflicts, coordinates rescues, and hands off to planning or reviewing when the current execution scope is complete. Use for prompts about swarming, parallel workers, launching multiple agents, coordinating a worker pool, or running approved current-phase work at scale.
Manages parent/child agent relationships with task delegation and result aggregation. Supports sequential chains, parallel fans, conditional routing, retry logic, timeout handling, and YAML-based visual workflow definition.
Bootstrap a fresh Ubuntu VPS into a complete multi-agent AI development environment with safety tools and coordination infrastructure in 30 minutes
Use when dealing with 2 or more tasks that can be performed independently, with no shared state or sequential dependencies
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
Use when coordinating complex tasks with orchestration, delegation, or parallel workstreams - provides structured workflows for orchestrate:brainstorm, orchestrate:spawn, and orchestrate:task.
Use when coordinating multi-agent work with dependencies, parallel workstreams, or complex handoffs requiring milestone tracking
AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles.