team

A command that divides a requested coding task into several work streams and coordinates multiple specialised agents in parallel. It then combines their results into one response.

In plain words
What is it for?
Use it to coordinate agents for feature development, application or system work, code investigation, refactoring analysis, test planning, research, and implementation.
Why use it?
It reduces the need to manually split a large implementation or investigation into separate assignments. Parallel work can cover different parts of a task at the same time.

Command

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/ruizrica/toolkit/team
Clone the repo
git clone --depth 1 https://github.com/ruizrica/toolkit
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 565 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00565
Opus 5 $0.00000 $0.00282
Sonnet 5 $0.00000 $0.00113
Haiku 4.5 $0.00000 $0.00056

Measured 2d ago against content hash bc6116cb9af6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

team scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

docs/commands/team.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/team

Coordinate a multi-agent team to implement features, code, or solutions in parallel. This command automatically analyzes tasks, decomposes them into parallel work streams, and spawns multiple specialized agents simultaneously.

Usage

/team [what to implement - feature/app/system/etc]

Arguments

Argument Required Description
task description Yes What you want to implement (feature, app, system, etc.)

How It Works

  1. Analyze - Parses the task and breaks it into parallel work streams
  2. Decompose - Creates 3-6 parallel sub-tasks based on the request
  3. Assign - Selects appropriate agent types for each sub-task
  4. Execute - Spawns multiple agents in parallel using a single response
  5. Synthesize - Collects results and combines them into a cohesive output

Available Agents

The /team command can spawn any of these specialized agents:

Investigation & Research:

  • gemini-agent - Large codebase analysis, research, documentation, web search
  • cursor-agent - Code review, refactoring analysis, test planning

Implementation:

  • codex-agent - Complex algorithms, advanced features, sophisticated logic
  • qwen-agent - Performance optimization, data structures, complex refactoring
  • opencode-agent - API development, standard CRUD, cost-effective bulk generation
  • groq-agent - Rapid prototyping, boilerplate, simple utilities

Example

/team Implement user authentication with OAuth support

This might spawn:

  • gemini-agent to research OAuth best practices
  • cursor-agent to analyze existing auth code
  • codex-agent to design the auth architecture
  • qwen-agent to implement the core OAuth flow

Key Behaviors

  • Parallel Execution - Spawns all agents in a single message for maximum parallelism
  • No Confirmation - Acts immediately without waiting for user confirmation
  • Automatic Selection - Chooses the right agents based on task requirements
  • Result Synthesis - Combines all agent outputs into a unified response

Read the full file on GitHub · 73 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 73 lines · 0 tokens per session scan A bc6116cb9af6

Subscribe to this mod's changes

team is a command published in the GitHub repository ruizrica/toolkit (5 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 565 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.