Nelson: A Claude Code Plugin for Coordinating AI Agents Like a Naval Fleet
Nelson is a Claude Code plugin that coordinates AI agent work using organizational principles inspired by 19th-century naval fleets. It structures missions into sailing orders (success criteria, constraints, when to stop), forms squadrons by picking execution modes and sizing teams, and creates battle plans where every task has an owner and file ownership to prevent conflicts.
Core Features
The plugin offers three execution modes:
- Single-session for sequential work
- Subagents where workers report back to a coordinator
- Agent teams where workers communicate with each other (using Claude Code's experimental teams feature)
Risk Management System
Every task receives a station level classification:
- Station 0: "patrol" - low risk, easy rollback
- Station 3: "Trafalgar" - reserved for irreversible actions requiring failure-mode checklists, rollback plans, and human confirmation
- Intermediate stations receive proportional safeguards
A circuit breaker prevents destructive actions before they occur.
Recent Updates
The biggest addition is hull integrity monitoring. Nelson reads token counts from Claude Code's session JSONL files and tracks context consumption across the entire squadron. Green status means continue, while red triggers relief on station: exhausted agents write turnover briefs to files, and fresh replacements read these files to continue the work. Briefs go to files rather than messages to preserve the replacement agent's context window.
Prevention Mechanisms
Standing order gates check for 16 documented anti-patterns during squadron formation and battle planning, including:
- "admiral at the helm" - coordinator doing implementation
- "split keel" - two agents editing the same file
- "skeleton crew" - task too small for a full agent
Crew Structure
Ships now have a three-tier hierarchy with actual Royal Navy warship names:
- Admiral coordinating
- Captains on named ships
- Specialist crew roles aboard each ship:
- Navigating Officer - read-only research
- Principal Warfare Officer - implementation
- Marine Engineering Officer - testing
Royal Marines serve as short-lived sub-agents for quick reconnaissance or targeted fixes.
Damage Control
Procedures include:
- Man overboard for stuck agents
- Session resumption after interruptions
- Partial rollback when one task is faulty but others are fine
- "scuttle and reform" when the entire mission isn't working
📖 Read the full source: r/ClaudeAI
👀 See Also
Local AI Agent Achieves Sub-Second STT and TTS Latency with Open-Source Servers
A developer achieved ~0.2s STT latency using Whisper large-v3-turbo with hybrid thread-managed GPU architecture and ~250ms TTS latency with Coqui-TTS optimized for low-latency synthesis. Both implementations are fully self-hosted and open-sourced.
Engram Memory SDK: Graph-Based Memory for AI Agents with Local Models
Engram Memory SDK is an open-source graph memory system for AI agents that works with local models via LiteLLM. It requires only one LLM call for ingestion, then uses vector search and graph traversal for recall with zero ongoing LLM costs.
Argus: Open-Source VS Code Extension for Real-Time Claude Code Observability
Argus visualizes Claude Code agent steps in real-time inside VS Code, showing timeline, dependency graph, and cost/loop detection to debug token-wasting behavior.
lazyclaude: A TUI for Managing Claude Code Configuration
lazyclaude is a terminal user interface tool inspired by lazygit that provides a single view for managing all Claude Code configuration stored on disk, including memory files, skills, agents, MCP servers, settings, permissions, hooks, keybindings, sessions, stats, plugins, and todos.