Network-AI

Swarm Orchestrator Skill

Scope of this skill bundle: All instructions below run local Python scripts (scripts/*.py). No network calls are made by this skill. Tokens are UUID-based (grant_{uuid4().hex}) stored in data/active_grants.json. Audit logging is plain JSONL (data/audit_log.jsonl) — no HMAC signing in the Python layer. HMAC-signed tokens, AES-256 encryption, and the standalone MCP server are all features of the companion Node.js package (npm install -g network-ai) — they are not implemented in these Python scripts and do not run automatically.

Multi-agent coordination system for complex workflows requiring task delegation, parallel execution, and permission-controlled access to sensitive APIs.

🎯 Orchestrator System Instructions

You are the Orchestrator Agent responsible for decomposing complex tasks, delegating to specialized agents, and synthesizing results. Follow this protocol:

Core Responsibilities

1. DECOMPOSE complex prompts into 3 specialized sub-tasks
2. DELEGATE using the budget-aware handoff protocol
3. VERIFY results on the blackboard before committing
4. SYNTHESIZE final output only after all validations pass

Task Decomposition Protocol

When you receive a complex request, decompose it into exactly 3 sub-tasks:

┌─────────────────────────────────────────────────────────────────┐
│                     COMPLEX USER REQUEST                        │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│  SUB-TASK 1   │   │  SUB-TASK 2   │   │  SUB-TASK 3   │
│ data_analyst  │   │ risk_assessor │   │strategy_advisor│
│    (DATA)     │   │   (VERIFY)    │   │  (RECOMMEND)  │
└───────────────┘   └───────────────┘   └───────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              ▼
                    ┌───────────────┐
                    │  SYNTHESIZE   │
                    │ orchestrator  │
                    └───────────────┘

Decomposition Template:

TASK DECOMPOSITION for: "{user_request}"

Sub-Task 1 (DATA): [data_analyst]
  - Objective: Extract/process raw data
  - Output: Structured JSON with metrics

Sub-Task 2 (VERIFY): [risk_assessor]  
  - Objective: Validate data quality & compliance
  - Output: Validation report with confidence score

Sub-Task 3 (RECOMMEND): [strategy_advisor]
  - Objective: Generate actionable insights
  - Output: Recommendations with rationale

Budget-Aware Handoff Protocol

CRITICAL: Before EVERY sessions_send, call the handoff interceptor:

# ALWAYS run this BEFORE sessions_send
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze Q4 revenue data"

Decision Logic:

IF result.allowed == true:
    → Proceed with sessions_send
    → Note tokens_spent and remaining_budget
ELSE:
    → STOP - Do NOT call sessions_send
    → Report blocked reason to user
    → Consider: reduce scope or abort task

Pre-Commit Verification Workflow

Before returning final results to the user:

# Step 1: Check all sub-task results on blackboard
python {baseDir}/scripts/blackboard.py read "task:001:data_analyst"
python {baseDir}/scripts/blackboard.py read "task:001:risk_assessor"
python {baseDir}/scripts/blackboard.py read "task:001:strategy_advisor"

# Step 2: Validate each result
python {baseDir}/scripts/swarm_guard.py validate-result \
  --task-id "task_001" \
  --agent data_analyst \
  --result '{"status":"success","output":{...},"confidence":0.85}'

# Step 3: Supervisor review (checks all issues)
python {baseDir}/scripts/swarm_guard.py supervisor-review --task-id "task_001"

# Step 4: Only if APPROVED, commit final state
python {baseDir}/scripts/blackboard.py write "task:001:final" \
  '{"status":"SUCCESS","output":{...}}'

Verdict Handling:

When to Use This Skill

• Task Delegation: Route work to specialized agents (data_analyst, strategy_advisor, risk_assessor)
• Parallel Execution: Run multiple agents simultaneously and synthesize results
• Permission Wall: Gate access to DATABASE, PAYMENTS, EMAIL, or FILE_EXPORT operations (abstract local resource types — no external credentials required)
• Shared Blackboard: Coordinate agent state via persistent markdown file

Quick Start

1. Initialize Budget (FIRST!)

Always initialize a budget before any multi-agent task:

python {baseDir}/scripts/swarm_guard.py budget-init \
  --task-id "task_001" \
  --budget 10000 \
  --description "Q4 Financial Analysis"

2. Delegate a Task to Another Session

Use OpenClaw's built-in session tools to delegate work:

sessions_list    # See available sessions/agents
sessions_send    # Send task to another session
sessions_history # Check results from delegated work

Example delegation prompt:

Use sessions_send to ask the data_analyst session to:
"Analyze Q4 revenue trends from the SAP export data and summarize key insights"

3. Check Permission Before API Access

Before accessing SAP or Financial APIs, evaluate the request:

# Run the permission checker script
python {baseDir}/scripts/check_permission.py \
  --agent "data_analyst" \
  --resource "DATABASE" \
  --justification "Need Q4 invoice data for quarterly report" \
  --scope "read:invoices"

The script will output a grant token if approved, or denial reason if rejected.

4. Use the Shared Blackboard

Read/write coordination state:

# Write to blackboard
python {baseDir}/scripts/blackboard.py write "task:q4_analysis" '{"status": "in_progress", "agent": "data_analyst"}'

# Read from blackboard  
python {baseDir}/scripts/blackboard.py read "task:q4_analysis"

# List all entries
python {baseDir}/scripts/blackboard.py list

Agent-to-Agent Handoff Protocol

When delegating tasks between agents/sessions:

Step 1: Initialize Budget & Check Capacity

# Initialize budget (if not already done)
python {baseDir}/scripts/swarm_guard.py budget-init --task-id "task_001" --budget 10000

# Check current status
python {baseDir}/scripts/swarm_guard.py budget-check --task-id "task_001"

Step 2: Identify Target Agent

sessions_list  # Find available agents

Common agent types:

Step 3: Intercept Before Handoff (REQUIRED)

# This checks budget AND handoff limits before allowing the call
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze Q4 data" \
  --artifact  # Include if expecting output

If ALLOWED: Proceed to Step 4 If BLOCKED: Stop - do not call sessions_send

Step 4: Construct Handoff Message

Include these fields in your delegation:

• instruction: Clear task description
• context: Relevant background information
• constraints: Any limitations or requirements
• expectedOutput: What format/content you need back

Step 5: Send via sessions_send

sessions_send to data_analyst:
"[HANDOFF]
Instruction: Analyze Q4 revenue by product category
Context: Using SAP export from ./data/q4_export.csv
Constraints: Focus on top 5 categories only
Expected Output: JSON summary with category, revenue, growth_pct
[/HANDOFF]"

Step 4: Check Results

sessions_history data_analyst  # Get the response

Permission Wall (AuthGuardian)

CRITICAL: Always check permissions before accessing:

• DATABASE - Internal database / data store access
• PAYMENTS - Financial/payment data services
• EMAIL - Email sending capability
• FILE_EXPORT - Exporting data to local files

Note: These are abstract local resource type names used by check_permission.py. No external API credentials are required or used — all permission evaluation runs locally.

Permission Evaluation Criteria

Using the Permission Script

# Request permission
python {baseDir}/scripts/check_permission.py \
  --agent "your_agent_id" \
  --resource "PAYMENTS" \
  --justification "Generating quarterly financial summary for board presentation" \
  --scope "read:revenue,read:expenses"

# Output if approved:
# ✅ GRANTED
# Token: grant_a1b2c3d4e5f6
# Expires: 2026-02-04T15:30:00Z
# Restrictions: read_only, no_pii_fields, audit_required

# Output if denied:
# ❌ DENIED
# Reason: Justification is insufficient. Please provide specific task context.

Restriction Types

Shared Blackboard Pattern

The blackboard (swarm-blackboard.md) is a markdown file for agent coordination:

# Swarm Blackboard
Last Updated: 2026-02-04T10:30:00Z

## Knowledge Cache
### task:q4_analysis
{"status": "completed", "result": {...}, "agent": "data_analyst"}

### cache:revenue_summary  
{"q4_total": 1250000, "growth": 0.15}

Blackboard Operations

# Write with TTL (expires after 1 hour)
python {baseDir}/scripts/blackboard.py write "cache:temp_data" '{"value": 123}' --ttl 3600

# Read (returns null if expired)
python {baseDir}/scripts/blackboard.py read "cache:temp_data"

# Delete
python {baseDir}/scripts/blackboard.py delete "cache:temp_data"

# Get full snapshot
python {baseDir}/scripts/blackboard.py snapshot

Parallel Execution

For tasks requiring multiple agent perspectives:

Strategy 1: Merge (Default)

Combine all agent outputs into unified result.

Ask data_analyst AND strategy_advisor to both analyze the dataset.
Merge their insights into a comprehensive report.

Strategy 2: Vote

Use when you need consensus - pick the result with highest confidence.

Strategy 3: First-Success

Use for redundancy - take first successful result.

Strategy 4: Chain

Sequential processing - output of one feeds into next.

Example Parallel Workflow

1. sessions_send to data_analyst: "Extract key metrics from Q4 data"
2. sessions_send to risk_assessor: "Identify compliance risks in Q4 data"  
3. sessions_send to strategy_advisor: "Recommend actions based on Q4 trends"
4. Wait for all responses via sessions_history
5. Synthesize: Combine metrics + risks + recommendations into executive summary

Security Considerations

1. Never bypass the permission wall for gated resources
2. Always include justification explaining the business need
3. Use minimal scope - request only what you need
4. Check token expiry - tokens are valid for 5 minutes
5. Validate tokens - use python {baseDir}/scripts/validate_token.py TOKEN to verify grant tokens before use
6. Audit trail - all permission requests are logged

📝 Audit Trail Requirements (MANDATORY)

Every sensitive action MUST be logged to data/audit_log.jsonl to maintain compliance and enable forensic analysis.

What Gets Logged Automatically

The scripts automatically log these events:

• permission_granted - When access is approved
• permission_denied - When access is rejected
• permission_revoked - When a token is manually revoked
• ttl_cleanup - When expired tokens are purged
• result_validated / result_rejected - Swarm Guard validations

Log Entry Format

{
  "timestamp": "2026-02-04T10:30:00+00:00",
  "action": "permission_granted",
  "details": {
    "agent_id": "data_analyst",
    "resource_type": "DATABASE",
    "justification": "Q4 revenue analysis",
    "token": "grant_abc123...",
    "restrictions": ["read_only", "max_records:100"]
  }
}

Reading the Audit Log

# View recent entries (last 10)
tail -10 {baseDir}/data/audit_log.jsonl

# Search for specific agent
grep "data_analyst" {baseDir}/data/audit_log.jsonl

# Count actions by type
cat {baseDir}/data/audit_log.jsonl | jq -r '.action' | sort | uniq -c

Custom Audit Entries

If you perform a sensitive action manually, log it:

import json
from datetime import datetime, timezone
from pathlib import Path

audit_file = Path("{baseDir}/data/audit_log.jsonl")
entry = {
    "timestamp": datetime.now(timezone.utc).isoformat(),
    "action": "manual_data_access",
    "details": {
        "agent": "orchestrator",
        "description": "Direct database query for debugging",
        "justification": "Investigating data sync issue #1234"
    }
}
with open(audit_file, "a") as f:
    f.write(json.dumps(entry) + "\n")

🧹 TTL Enforcement (Token Lifecycle)

Expired permission tokens are automatically tracked. Run periodic cleanup:

# Validate a grant token
python {baseDir}/scripts/validate_token.py grant_a1b2c3d4e5f6

# List expired tokens (without removing)
python {baseDir}/scripts/revoke_token.py --list-expired

# Remove all expired tokens
python {baseDir}/scripts/revoke_token.py --cleanup

# Output:
# 🧹 TTL Cleanup Complete
#    Removed: 3 expired token(s)
#    Remaining active grants: 2

Best Practice: Run --cleanup at the start of each multi-agent task to ensure a clean permission state.

⚠️ Swarm Guard: Preventing Common Failures

Two critical issues can derail multi-agent swarms:

1. The Handoff Tax 💸

Problem: Agents waste tokens "talking about" work instead of doing it.

Prevention:

# Before each handoff, check your budget:
python {baseDir}/scripts/swarm_guard.py check-handoff --task-id "task_001"

# Output:
# 🟢 Task: task_001
#    Handoffs: 1/3
#    Remaining: 2
#    Action Ratio: 100%

Rules enforced:

• Max 3 handoffs per task - After 3, produce output or abort
• Max 500 chars per message - Be concise: instruction + constraints + expected output
• 60% action ratio - At least 60% of handoffs must produce artifacts
• 2-minute planning limit - No output after 2min = timeout

# Record a handoff (with tax checking):
python {baseDir}/scripts/swarm_guard.py record-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze sales data, output JSON summary" \
  --artifact  # Include if this handoff produces output

2. Silent Failure Detection 👻

Problem: One agent fails silently, others keep working on bad data.

Prevention - Heartbeats:

# Agents must send heartbeats while working:
python {baseDir}/scripts/swarm_guard.py heartbeat --agent data_analyst --task-id "task_001"

# Check if an agent is healthy:
python {baseDir}/scripts/swarm_guard.py health-check --agent data_analyst

# Output if healthy:
# 💚 Agent 'data_analyst' is HEALTHY
#    Last seen: 15s ago

# Output if failed:
# 💔 Agent 'data_analyst' is UNHEALTHY
#    Reason: STALE_HEARTBEAT
#    → Do NOT use any pending results from this agent.

Prevention - Result Validation:

# Before using another agent's result, validate it:
python {baseDir}/scripts/swarm_guard.py validate-result \
  --task-id "task_001" \
  --agent data_analyst \
  --result '{"status": "success", "output": {"revenue": 125000}, "confidence": 0.85}'

# Output:
# ✅ RESULT VALID
#    → APPROVED - Result can be used by other agents

Required result fields: status, output, confidence

Supervisor Review

Before finalizing any task, run supervisor review:

python {baseDir}/scripts/swarm_guard.py supervisor-review --task-id "task_001"

# Output:
# ✅ SUPERVISOR VERDICT: APPROVED
#    Task: task_001
#    Age: 1.5 minutes
#    Handoffs: 2
#    Artifacts: 2

Verdicts:

• APPROVED - Task healthy, results usable
• WARNING - Issues detected, review recommended
• BLOCKED - Critical failures, do NOT use results

Troubleshooting

Permission Denied

• Provide more specific justification (mention task, purpose, expected outcome)
• Narrow the requested scope
• Check agent trust level

Blackboard Read Returns Null

• Entry may have expired (check TTL)
• Key may be misspelled
• Entry was never written

Session Not Found

• Run sessions_list to see available sessions
• Session may need to be started first

References

• AuthGuardian Details - Full permission system documentation
• Blackboard Schema - Data structure specifications
• Agent Trust Levels - How trust is calculated