openclaw-agent-orchestrator — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited openclaw-agent-orchestrator (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Multi-agent orchestration for AI agent teams. 5 tools. Parallel execution. Automatic result aggregation. Zero infrastructure.
| Tool | Purpose | Free |
|---|---|---|
spawn_agent | Launch a new agent with a role, model, and initial task | Pro |
list_agents | List all active agents with status, current task, and resource usage | Yes |
dispatch_task | Send a task to the best available agent or a specific agent ID | Yes |
get_agent_status | Get detailed status for a specific agent — task, progress, errors | Yes |
aggregate_results | Collect and merge outputs from multiple agents into one response | Yes |
{
"openclaw-agent-orchestrator": {
"type": "streamable-http",
"url": "https://openclaw-agent-orchestrator-mcp.yagami8095.workers.dev/mcp"
}
}Add to Claude Desktop, Cursor, Windsurf, VS Code, or any MCP-compatible client. Zero install. Works immediately.
This skill activates when a user says any of these:
The intelligent routing engine for multi-agent task dispatch:
Routing algorithm:
Agent roles:
| Role | Best For | Model Recommendation |
|---|---|---|
analyst | Research, data analysis, reports | DeepSeek R1 |
coder | Code generation, debugging, review | DeepSeek R1 |
writer | Content, documentation, emails | Llama 3.3 70B |
scraper | Web data collection, extraction | Llama 3.3 70B |
monitor | Health checks, alerts, status | Llama 3.1 8B |
general | Mixed tasks, catch-all | DeepSeek R1 |
Continuous health tracking for all spawned agents:
| Check | Frequency | Action on Failure |
|---|---|---|
| Heartbeat ping | 30s | Mark as unresponsive after 3 misses |
| Task timeout | Per task (configurable) | Auto-reassign to next available agent |
| Error rate | Rolling 10-task window | Quarantine if >30% error rate |
| Memory usage | 60s | Alert if approaching limit |
| Auto-restart | On crash | Restore from last checkpoint, up to 3 times |
How aggregate_results synthesizes multi-agent outputs:
spawn_agent (Pro) -- Launch Agent via FleetCommand ProtocolCreate a new agent with a specified role, model, and initial task. Returns agent ID for subsequent dispatch and status calls.
Wrong / Right:
WRONG: Running 5 tasks sequentially in one agent, waiting 10min for results
RIGHT: spawn_agent({ role: "analyst", model: "deepinfra/deepseek-ai/DeepSeek-R1-0528",
task: "Analyze Q1 revenue trends in our Stripe data" })
spawn_agent({ role: "writer", task: "Draft the investor update email" })
spawn_agent({ role: "scraper", task: "Pull competitor pricing from 5 sites" })
-> All 3 run in parallel. Done in 3min instead of 10min.
WRONG: Spawning 20 agents for a simple 2-step task
RIGHT: Spawn agents only when parallelism provides actual speedup.
Single sequential tasks are faster in one agent session.list_agents -- Fleet StatusList all active agents with their current status, assigned role, current task, uptime, and resource usage.
Wrong / Right:
WRONG: Not knowing which agents are running or what they're doing
RIGHT: list_agents()
-> [{ id: "agent-7f3a", role: "analyst", status: "running",
task: "Analyzing Stripe data", started: "14min ago", cpu_pct: 42 },
{ id: "agent-2b8c", role: "writer", status: "idle", ... }]
-> Full fleet visibility in one call
WRONG: Assuming agents are running when they may have crashed
RIGHT: Check list_agents before dispatching critical tasks.
AgentHealth Monitor auto-restarts crashed agents, but there can be a 30–60s gap.dispatch_task -- Task Routing via FleetCommand ProtocolSend a task to a specific agent ID, or let FleetCommand route it to the best available agent.
Parameters: task (string), agent_id (optional — omit for auto-routing), role_hint (optional), priority (low/normal/high)
get_agent_status -- Agent DetailGet detailed status for a specific agent: current task, progress percentage, errors, estimated completion time, and last 5 log lines.
Returns: agent_id, role, status, current_task, progress_pct, errors (last 3), eta_sec, uptime_sec, last_heartbeat
aggregate_results -- Multi-Agent Result Synthesis via ResultMerge EngineCollect and synthesize outputs from multiple agents. Pass a list of agent IDs and optionally a synthesis instruction.
Parameters: agent_ids (array), synthesis_instruction (optional), wait_for_completion (bool, default true), timeout_sec (default 120)
| Tier | Calls/Day | Price | Includes |
|---|---|---|---|
| Free | 20 | $0 | list_agents + dispatch_task + get_agent_status + aggregate_results |
| Pro | 1,000 | $9/mo | All 5 tools + spawn_agent + 8 concurrent agents + all 9 OpenClaw servers |
| x402 | Pay-per-call | $0.05 USDC | No account needed, crypto-native |
Get Pro Key: https://buy.stripe.com/4gw5na5U19SP9TW288
| Server | Tools | Best For |
|---|---|---|
| Agent Orchestrator | 5 | Multi-agent spawn, coordinate, aggregate |
| Task Queue | 5 | Persistent agent tasks, assignment, tracking |
| Health Monitor | 4 | 24/7 uptime, SLA reports, Telegram alerts |
| Telegram Bot | 5 | Messages, alerts, reports, command handling |
| Database Toolkit | 5 | D1/PostgreSQL/MongoDB query and manage |
| Web Scraper | 5 | Stealth scraping, structured extraction |
| Content Autopilot | 5 | AI writing, multi-platform publishing |
| API Monitor | 5 | Rate limits, cost tracking, provider analytics |
| Market Intelligence | 6 | AI market trends, GitHub stats, competitor analysis |
All 9 servers share one Pro key. $9/mo = 49 tools.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.