fleet

Orchestrates parallel AI agents to decompose and execute multi-domain development tasks in isolated worktrees with managed handoffs and logging.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/mferris77/SpringBoard --skill fleet-mferris77
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: fleet
Source: https://github.com/mferris77/SpringBoard/tree/main/vscode-citadel-harness/skills/fleet
Command: npx skills add https://github.com/mferris77/SpringBoard --skill fleet-mferris77

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fleet solves the problem of executing large, multi-stream engineering work by decomposing a direction into concurrent campaigns and orchestrating isolated agents so teams can make progress in parallel without duplicating discovery or conflicting on files.

Core Features & Use Cases

  • Parallel decomposition and waves: Split work into 2–3 agents per wave, assign independent scopes to each agent, and sequence dependent work into subsequent waves.
  • Discovery relay and session persistence: Collect HANDOFFs, compress discoveries, and inject cumulative context into later waves to prevent duplicated effort.
  • Worktree isolation and speculative mode: Spawn agents in isolated worktrees and support speculative N-way implementations for architecture decisions, preserving branches for comparison.
  • Safety and recoverability: Enforce non-overlapping scopes, timeouts and circuit breakers, telemetry logging, dead-instance recovery, and explicit merge/conflict handling for reproducible campaigns.
  • Use Case: Large refactors or multi-module feature work (e.g., refactor API and update frontend simultaneously) where separate domains can be advanced in parallel with coordinated handoffs.

Quick Start

Invoke Fleet with a direction to decompose work into waves and run parallel agents, for example: /fleet refactor the API layer and update the frontend in parallel.

Frequently Asked Questions about fleet

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run parallel agents to execute large codebase refactors without file conflicts?

Parallel agent campaigns decompose large refactors into isolated worktrees, enforcing non-overlapping file scopes per wave to prevent conflicts and executing concurrent streams safely.

What is the best way to prevent duplicated discovery when running concurrent agents on a multi-module feature?

Discovery relay prevents duplicated effort by collecting handoffs, compressing discoveries, and injecting cumulative context into subsequent waves of parallel agents.

Can I use speculative execution to compare multiple architecture implementations simultaneously?

Speculative mode supports N-way implementations by spawning agents in isolated worktrees and preserving branches, allowing you to compare different architecture decisions concurrently.

How do I recover from dead agent instances during a parallel worktree execution?

Parallel agent orchestration includes dead-instance recovery, timeout handling, and circuit breakers to manage failed streams, alongside session file logging for campaign reproducibility.

Does parallel agent orchestration work for multi-domain tasks like updating an API and frontend simultaneously?

Parallel agent campaigns specifically target multi-domain development tasks by splitting work into 2-3 agents per wave and sequencing dependent work into subsequent coordinated waves.

What are the limitations of using worktree isolation for parallel agent execution?

Worktree isolation requires explicit merge and conflict handling after parallel execution, and agents must operate within non-overlapping file scopes to avoid blocking concurrent streams.