Skip to content
Automation
Skill

/dev-team

On-demand spawning of multiple engineering agents in parallel for large-context work. Use when a task is too big for a single agent and the work can be split into independent streams.

From plugin
evo-nexus
520193 skills38 agents40 commands9 MCP
Install
$ npx -y skills add evolution-foundation/evo-nexus --skill dev-team --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/dev-team

Context preview

The summary Claude sees to decide when to auto-load this skill.

On-demand spawning of multiple engineering agents in parallel for large-context work. Use when a task is too big for a single agent and the work can be split into independent streams.

SKILL.md

dev-team.SKILL.md
name: dev-team
description: On-demand spawning of multiple engineering agents in parallel for large-context work. Use when a task is too big for a single agent and the work can be split into independent streams.

Dev Team

Derived from oh-my-claudecode (MIT, Yeachan Heo). Adapted for the EvoNexus Engineering Layer.

On-demand parallel agent spawning. When a task is too big for one agent and the work can be split into independent streams, spin up multiple engineering agents working in parallel.

Use When

  • Multi-file refactor across 10+ files in different modules
  • Codebase audit that benefits from parallel exploration
  • Performance investigation across multiple subsystems
  • Documentation pass across many files

Do Not Use When

  • Sequential dependencies (B needs A's output)
  • Single-file change
  • Task fits within a single agent's context

Workflow

1. **Decompose** the task into N independent streams 2. **Assign** each stream to the most appropriate agent (apex / bolt / lens / etc.) 3. **Spawn** agents in parallel via Task tool 4. **Collect** results as they return 5. **Synthesize** into a unified output 6. Save to `workspace/development/research/[C]team-{topic}-{date}.md`

Streams Example

For "audit the entire auth module":

  • **Stream 1:** `@scout-explorer` → map all auth files
  • **Stream 2:** `@vault-security` → security audit
  • **Stream 3:** `@lens-reviewer` → code quality review
  • **Stream 4:** `@grid-tester` → test coverage analysis
  • **Stream 5:** `@apex-architect` → architecture analysis

All spawn in parallel, results combine into one report.

Output

## Team Investigation — {topic}

### Streams
1. {stream 1 result summary}
2. {stream 2 result summary}
...

### Cross-stream Findings
[Insights that emerged from combining results]

### Recommendation
[Unified next step]

Pairs With

  • Any of the 19 engineering agents (you pick which to spawn)
  • `dev-autopilot` (which can spawn its own team internally)
  • `@compass-planner` (often the consumer of team output)
Read more
Ships withevo-nexus

The open source operating system for AI-powered businesses

Get the whole plugin

Other skills on evo-nexus.