auto-agent

An agent-planning command that reads a task description, estimates its complexity, and chooses how many agents and what kinds of agents to use.

In plain words
What is it for?
Use it to plan or start agents for tasks such as building an authenticated REST API, debugging a performance issue, or refactoring a codebase. It can also analyze a task without starting agents.
Why use it?
It removes the need to decide manually how to divide a task among agents while still letting you set minimums, maximums, or a selection strategy.

Command for Claude Code

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/ruvnet/ruview/auto-agent
Clone the repo
git clone --depth 1 https://github.com/ruvnet/RuView

Made for: Claude Code.

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 621 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 $0.00621
Opus 5 $0.00000 $0.00311
Sonnet 5 $0.00000 $0.00124
Haiku 4.5 $0.00000 $0.00062

Measured yesterday against content hash 40f23f854dc0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

auto-agent 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 yesterday.

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.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

.claude/commands/automation/auto-agent.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

auto agent

Automatically spawn and manage agents based on task requirements.

Usage

npx claude-flow auto agent [options]

Options

  • --task, -t <description> - Task description for agent analysis
  • --max-agents, -m <number> - Maximum agents to spawn (default: auto)
  • --min-agents <number> - Minimum agents required (default: 1)
  • --strategy, -s <type> - Selection strategy: optimal, minimal, balanced
  • --no-spawn - Analyze only, don't spawn agents

Examples

Basic auto-spawning

npx claude-flow auto agent --task "Build a REST API with authentication"

Constrained spawning

npx claude-flow auto agent -t "Debug performance issue" --max-agents 3

Analysis only

npx claude-flow auto agent -t "Refactor codebase" --no-spawn

Minimal strategy

npx claude-flow auto agent -t "Fix bug in login" -s minimal

How It Works

  1. Task Analysis

    • Parses task description
    • Identifies required skills
    • Estimates complexity
    • Determines parallelization opportunities
  2. Agent Selection

    • Matches skills to agent types
    • Considers task dependencies
    • Optimizes for efficiency
    • Respects constraints
  3. Topology Selection

    • Chooses optimal swarm structure
    • Configures communication patterns
    • Sets up coordination rules
    • Enables monitoring
  4. Automatic Spawning

    • Creates selected agents
    • Assigns specific roles
    • Distributes subtasks
    • Initiates coordination

Agent Types Selected

  • Architect: System design, architecture decisions
  • Coder: Implementation, code generation
  • Tester: Test creation, quality assurance
  • Analyst: Performance, optimization
  • Researcher: Documentation, best practices
  • Coordinator: Task management, progress tracking

Strategies

Optimal

  • Maximum efficiency
  • May spawn more agents
  • Best for complex tasks
  • Highest resource usage

Minimal

  • Minimum viable agents
  • Conservative approach
  • Good for simple tasks
  • Lowest resource usage

Read the full file on GitHub · 123 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. yesterday First seen · 123 lines · 0 tokens per session scan A 40f23f854dc0

Subscribe to this mod's changes

auto-agent is a command published in the GitHub repository ruvnet/RuView (92,079 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 621 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 2026-08-30.