Agent Swarm

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name: agent-swarm displayName: Agent Swarm | OpenClaw Skill description: IMPORTANT: OpenRouter is required. Routes tasks to the right model and always delegates work through sessions_spawn. Rejects prompt-injection patterns in task strings (v1.7.6+). version: 1.7.8

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Agent Swarm | OpenClaw Skill

Description

IMPORTANT: OpenRouter is required. Routes tasks to the right model and always delegates work through sessions_spawn.

Before installing

OPENCLAW_HOME: Not required. The skill uses OPENCLAW_HOME only if set; otherwise it defaults to ~/.openclaw. This is consistent in both metadata (_meta.json: listed in optionalEnv, not in env) and behavior.

openclaw.json read access: The skill reads the local file openclaw.json (at $OPENCLAW_HOME/openclaw.json or ~/.openclaw/openclaw.json). Only the fields tools.exec.host and tools.exec.node are used; no gateway secrets or API keys are read. Verify you are comfortable granting read access to that file before installing.

Examples

Single task

Router output: {"task":"write a poem","model":"openrouter/moonshotai/kimi-k2.5","sessionTarget":"isolated"}

Then call: sessions_spawn(task="write a poem", model="openrouter/moonshotai/kimi-k2.5", sessionTarget="isolated")

Parallel tasks

python3 workspace/skills/agent-swarm/scripts/router.py spawn --json --multi "fix bug and write poem"

This returns multiple spawn configs. Start one sub-agent per config.

Commands

Manual/CLI use only. The examples below pass the task as a single argument; for programmatic use with untrusted user input, always invoke the router via subprocess.run(..., [..., user_message], ...) with a list of arguments (see Security). Do not build a shell command string from user input.

python scripts/router.py default
python scripts/router.py classify "fix lint errors"
python scripts/router.py spawn --json "write a poem"
python scripts/router.py spawn --json --multi "fix bug and write poem"
python scripts/router.py models

What this skill does

Agent Swarm is a traffic cop for AI models. It picks the best model for each task, then starts a sub-agent to do the work.

IMPORTANT: OpenRouter is required

Required Platform Configuration:

OpenRouter API key: Must be configured in OpenClaw platform settings (not provided by this skill)

OPENCLAW_HOME (optional): Environment variable pointing to OpenClaw workspace root. If not set, defaults to ~/.openclaw

openclaw.json access: The router reads tools.exec.host and tools.exec.node from openclaw.json (located at $OPENCLAW_HOME/openclaw.json or ~/.openclaw/openclaw.json). Only these two fields are accessed; no gateway secrets or API keys are read.

Model Requirements:

Model IDs must use openrouter/... prefix

If OpenRouter is not configured in OpenClaw, delegation will fail

Why this helps

Faster replies (cheap orchestrator, smart sub-agent routing)

Better quality (code tasks go to code models, writing tasks go to writing models)

Lower cost (you do not run every task on the most expensive model)

Core rule (non-negotiable)

For user tasks, the orchestrator must delegate. It must NOT answer the task itself.

Use this flow every time:

1. Run router. From orchestrator code, use subprocess with a list of arguments (never shell interpolation with user input):

   import subprocess
   result = subprocess.run(
       ["python3", "/path/to/workspace/skills/agent-swarm/scripts/router.py", "spawn", "--json", user_message],
       capture_output=True,
       text=True
   )
   data = json.loads(result.stdout) if result.returncode == 0 else {}
   

CLI only (manual testing; do not use from code with untrusted user input): python3 workspace/skills/agent-swarm/scripts/router.py spawn --json "your task here" Use OPENCLAW_HOME or absolute path for the script when not in workspace root. 2. If needs_config_patch is true: stop and report that patch to the user. 3. Otherwise call: sessions_spawn(task=..., model=..., sessionTarget=...) 4. Wait for sessions_spawn result. 5. Return the sub-agent result to the user.

If sessions_spawn fails, return only a delegation failure message. Do not do the task yourself.

Config basics

Edit config.json in the skill root (parent of scripts/) to change routing.

What you can change

| What | Key | Purpose | |------|-----|--------| | Orchestrator / session default | default_model | Main agent and new sessions (e.g. Gemini 2.5 Flash) | | Task-specific model per tier | routing_rules..primary | Model used when a task matches that tier | | Backup models if primary fails | routing_rules..fallback | Array of model IDs to try next |

All task-specific tiers (change the model for each)

| Tier | Key to change primary | Typical use | |------|------------------------|-------------| | FAST | routing_rules.FAST.primary | Simple tasks: check, list, status, fetch | | REASONING | routing_rules.REASONING.primary | Logic, math, step-by-step analysis | | CREATIVE | routing_rules.CREATIVE.primary | Writing, stories, UI/UX, design | | RESEARCH | routing_rules.RESEARCH.primary | Research, search, fact-finding | | CODE | routing_rules.CODE.primary | Code, debug, refactor, implement | | QUALITY | routing_rules.QUALITY.primary | Complex/architecture tasks | | COMPLEX | routing_rules.COMPLEX.primary | Multi-step / complex system tasks | | VISION | routing_rules.VISION.primary | Image analysis, screenshots, visual |

To change all task-specific models: edit each routing_rules..primary above. Use model IDs from the models array in config.json (must start with openrouter/).

Simple config examples

Orchestrator only (keep defaults for tiers):

{
  "default_model": "openrouter/google/gemini-2.5-flash"
}

(Other keys like routing_rules and models can stay as in the shipped config.json.)

Change one tier (e.g. CODE to MiniMax):

"routing_rules": {
  "CODE": {
    "primary": "openrouter/minimax/minimax-m2.5",
    "fallback": ["openrouter/qwen/qwen3-coder-flash"]
  }
}

Change multiple tiers (primaries only):

"routing_rules": {
  "CREATIVE": { "primary": "openrouter/moonshotai/kimi-k2.5", "fallback": [] },
  "CODE":     { "primary": "openrouter/z-ai/glm-4.7-flash", "fallback": ["openrouter/minimax/minimax-m2.5"] },
  "RESEARCH": { "primary": "openrouter/x-ai/grok-4.1-fast", "fallback": [] }
}

Only include tiers you want to override; the rest are read from the full config.json.

Security

Input Validation

The router validates and sanitizes all inputs to prevent injection attacks:

Task strings: Validated for length (max 10KB), null bytes; rejects prompt-injection patterns (script tags, javascript: protocol, event-handler attributes). Invalid tasks raise ValueError with a clear message.

Config patches: Only allows modifications to tools.exec.host and tools.exec.node (whitelist approach)

Labels: Validated for length and null bytes

Safe Execution

Critical: When calling router.py from orchestrator code, always use subprocess with a list of arguments, never shell string interpolation:

# ✅ SAFE: Use subprocess with list arguments
import subprocess
result = subprocess.run(
    ["python3", "/path/to/router.py", "spawn", "--json", user_message],
    capture_output=True,
    text=True
)
❌ UNSAFE: Shell string interpolation (vulnerable to injection)
import os
os.system(f'python3 router.py spawn --json "{user_message}"')  # DON'T DO THIS

The router uses Python's argparse, which safely handles arguments when passed as a list. Shell string interpolation is vulnerable to command injection if the user message contains shell metacharacters.

Config Patch Safety

The recommended_config_patch only modifies safe fields:

tools.exec.host (must be 'sandbox' or 'node')

tools.exec.node (only when host is 'node')

All config patches are validated before being returned. The orchestrator should validate patches again before applying them to openclaw.json.

Prompt Injection Mitigation

The router rejects task strings that contain prompt-injection patterns (e.g.