Network AI

--- name: Network-AI description: "Python orchestration skill: local multi-agent workflows via blackboard file, permission gating, and token budget scripts. All execution is local — no network calls, no Node.js required. TypeScript/Node.js features (HMAC tokens, AES-256, MCP server, 15 adapters, CLI) are in the SEPARATE companion npm package (npm install -g network-ai) and are NOT part of this skill bundle." metadata: openclaw: emoji: "\U0001F41D" homepage: https://github.com/Jovancoding/Network-AI bundle_scope: "Python scripts only (scripts/*.py). The README.md in this repo describes the FULL project including the companion Node.js npm package — features documented there (HMAC tokens, AES-256 encryption, MCP server, 15 adapters, CLI) are NOT implemented in these Python scripts and are NOT part of this ClawHub skill. Install the npm package separately for those features." network_calls: none sessions_ops: "platform-provided — sessions_send, sessions_list, and sessions_history are OpenClaw host platform built-ins, not implemented or invoked by this skill's Python scripts" requires: bins: - python3 optional_bins: [] # No optional binaries required. Node.js is only needed for the SEPARATE companion npm package (not part of this skill). env: SWARM_TOKEN_SECRET: required: false description: "Node.js MCP server only — not used by these Python scripts. The Python permission layer uses UUID-based tokens stored in data/active_grants.json." SWARM_ENCRYPTION_KEY: required: false description: "Node.js MCP server only — not used by these Python scripts. The Python blackboard does not encrypt data at rest." OPENAI_API_KEY: required: false description: "Not used by these Python scripts. Only used by the optional Node.js demo examples when running the companion npm package." MINIMAX_API_KEY: required: false description: "Not used by these Python scripts. Only used by the optional Node.js MiniMaxAdapter when running the companion npm package." privacy: audit_log: path: data/audit_log.jsonl scope: local-only description: "Local append-only JSONL file recording operation metadata (agentId, action, timestamp, outcome). No data leaves the machine. Disable with --no-audit flag on network-ai-server, or pass auditLogPath: undefined in createSwarmOrchestrator config."

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.

Setup

No pip install required. All 6 scripts use Python standard library only — zero third-party packages.

Note on requirements.txt: The file exists for documentation purposes only — it lists the stdlib modules used and has no required packages. All listed deps are commented out as optional. You do not need to run pip install -r requirements.txt.

# Prerequisite: python3 (any version ≥ 3.8)
python3 --version

# That's it. Run any script directly:
python3 scripts/blackboard.py list
python3 scripts/swarm_guard.py budget-init --task-id "task_001" --budget 10000

# Optional: for cross-platform file locking on Windows production hosts
pip install filelock  # only needed if you see locking issues on Windows

The data/ directory is created automatically on first run. No configuration files, environment variables, or credentials are required.

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:

The 3-Layer Memory Model

Every agent in the swarm operates with three memory layers, each with a different scope and lifetime:

Layer 1 — Agent Context

Each agent's own context window: the current task instructions, conversation history, and immediate working memory. Managed automatically by the OpenClaw/LLM platform. Nothing to configure.

Layer 2 — Blackboard (Shared Coordination State)

A shared markdown file (swarm-blackboard.md) for real-time cross-agent coordination: task results, grant tokens, status flags, and TTL-scoped cache entries. Agents read and write via scripts/blackboard.py. Entries expire automatically.

Layer 3 — Project Context (Persistent Long-Term Memory)

A JSON file (data/project-context.json) that holds information every agent should know, regardless of what session or task is running:

• Goals — long-term objectives of the project
• Tech stack — languages, frameworks, infrastructure
• Milestones — completed, in-progress, and planned work
• Architecture decisions — design choices and their rationales
• Banned approaches — approaches that have been ruled out

Initialising Project Context

python {baseDir}/scripts/context_manager.py init \
  --name "MyProject" \
  --description "Multi-agent workflow automation" \
  --version "1.0.0"

Injecting Context into an Agent System Prompt

python {baseDir}/scripts/context_manager.py inject

Copy the output block to the top of your agent's system prompt. Every agent that receives this block shares the same long-term project awareness.

Recording a Decision

python {baseDir}/scripts/context_manager.py update \
  --section decisions \
  --add '{"decision": "Use atomic blackboard commits", "rationale": "Prevent race conditions in parallel agents"}'

Updating Milestones

# Mark a milestone complete
python {baseDir}/scripts/context_manager.py update \
  --section milestones --complete "Ship v2.0"

# Add a planned milestone
python {baseDir}/scripts/context_manager.py update \
  --section milestones --add '{"planned": "Integrate vector memory"}'

Setting the Tech Stack

python {baseDir}/scripts/context_manager.py update \
  --section stack \
  --set '{"language": "TypeScript", "runtime": "Node.js 18", "framework": "Network-AI v4.5"}'

Banning an Approach

python {baseDir}/scripts/context_manager.py update \
  --section banned \
  --add "Direct database writes from agent scripts (use permission gating)"

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

Platform note: sessions_list, sessions_send, and sessions_history are OpenClaw host platform built-ins — they are part of the OpenClaw runtime, not provided or invoked by this skill's Python scripts. This skill only runs local python scripts/*.py commands. The guidance below describes how to combine the platform's session tools with this skill's budget guard.

First check budget, then use the OpenClaw platform operation:

# 1. Check budget (this skill's Python script)
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" --from orchestrator --to data_analyst \
  --message "Analyze Q4 revenue data"

# 2. If allowed, delegate using the OpenClaw platform tool (not this skill):
#    sessions_list    → see available sessions/agents
#    sessions_send    → send task to another session
#    sessions_history → check results from delegated work

Example delegation prompt:

After running swarm_guard.py intercept-handoff and getting result.allowed == true,
use the OpenClaw sessions_send platform tool to ask the data_analyst session:
"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

5. Optional: Use the Node.js CLI

Scope note: The network-ai CLI is part of the companion npm package — it is a completely separate project and is NOT part of this ClawHub skill bundle. This skill's Python scripts work fully without it. See the Appendix at the bottom of this file for details.

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

Platform note: sessions_list is an OpenClaw host platform built-in, not provided by this skill.

sessions_list  # OpenClaw platform operation — 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 OpenClaw Platform Session Tool

Platform note: sessions_send is an OpenClaw host platform built-in — it is NOT implemented by this skill. This skill only provides the budget guard (swarm_guard.py) that must be run first.

# OpenClaw platform operation (not this skill):
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 6: Check Results

Platform note: sessions_history is an OpenClaw host platform built-in, not provided by this skill.

sessions_history data_analyst  # OpenClaw platform operation — 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

Platform note: sessions_send and sessions_history are OpenClaw host platform built-ins, not provided by this skill. This skill provides only the swarm_guard.py budget/handoff check that runs before each delegation.

# For each delegation below, first run:
#   python {baseDir}/scripts/swarm_guard.py intercept-handoff --task-id "task_001" --from orchestrator --to <agent> --message "<task>"
# Then, if allowed, use the OpenClaw platform tool:
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 (OpenClaw platform built-in) 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
• CLI Reference - Full network-ai CLI command reference (§ 10. CLI)

Appendix: Optional Node.js Companion (npm)

This section describes a SEPARATE project — not part of this ClawHub skill bundle. The Python scripts above work completely without any of this. Install only if you want MCP server integration with Claude/Cursor/VS Code.

npm install -g network-ai
npx network-ai-server --port 3001

The companion npm package (network-ai) provides:

• HMAC-signed audit tokens (vs UUID tokens in the Python layer)
• AES-256 blackboard encryption
• A standalone MCP server for IDE integration (Claude, Cursor, VS Code)
• 14 framework adapters (LangChain, AutoGen, CrewAI, DSPy, LlamaIndex, etc.)
• A full CLI (network-ai bb, network-ai auth, network-ai budget, network-ai audit)

None of the above are provided by this skill's Python scripts. No network calls are made by this skill.