By baxtercooper
Observable subagent orchestration with structured contracts, verification loops, and deviation tracking
Execute atomic tasks with full observability. Returns structured YAML with understood/approach/observations/blockers/output/confidence/evidence. Use for isolated task execution where you need deterministic, verifiable behavior.
Two-stage code review agent. Stage 1 checks spec compliance, Stage 2 checks code quality. Stage 2 is BLOCKED until Stage 1 passes. Use after implementation to validate work.
Use when entering an unfamiliar codebase, starting a new feature, or before any implementation task. Provides structured exploration protocol to understand before acting.
Use when tasks fail due to transient errors. Provides structured retry strategies and graceful degradation patterns. Invoke on network errors, rate limits, or intermittent failures.
Use before committing to a task. Provides complexity analysis and scope assessment. Invoke when user asks "how complex" or before large features.
Use when all tasks are complete. Presents completion options and enforces test gate. Work is never left in limbo.
This skill should be used when orchestrating complex tasks, decomposing work into atomic units, dispatching to subagents, verifying outputs, or when discussing task verification and deviation tracking patterns.
Uses power tools
Uses Bash, Write, or Edit tools
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub joshuarweaver/cascade-ai-ml-agents-misc-1 --plugin baxtercooper-nexusProduction ADK orchestrator for A2A protocol and multi-agent coordination on Vertex AI
Intelligent orchestration platform for AI coding tools — routes tasks to the best model, learns from outcomes, and enforces quality through multi-model consensus. 46 MCP tools for agent management, research, memory, consensus voting, codebase intelligence, and a full dev pipeline.
Context management and multi-agent orchestration with performance optimization tools
Multi-agent orchestrator — supervisor loop that launches agents to implement plans
Multi-agent orchestrator for Claude Code. Use when user mentions gastown, gas town, gt commands, convoys, polecats, rigs, slinging work, multi-agent coordination.
Feature development with code-architect/explorer/reviewer agents, CLAUDE.md audit and session learnings, and Agent Skills creation with eval benchmarking from Anthropic.