Network-AI

Python 编排技能：通过黑板文件、权限门控和令牌预算脚本实现本地多代理工作流程。所有执行都是本地的——无需网络调用，无需 Node.js。 TypeScript/Node.js 功能（HMAC 令牌、AES-256、MCP 服务器、14 个适配器、CLI）位于单独的配套 npm 包 (npm install -g network-ai) 中，并且不属于此技能包的一部分。

## 概述（中文）

Python 编排技能：通过黑板文件、权限门控和令牌预算脚本实现本地多代理工作流程。所有执行都是本地的——无需网络调用，无需 Node.js。 TypeScript/Node.js 功能（HMAC 令牌、AES-256、MCP 服务器、14 个适配器、CLI）位于单独的配套 npm 包 (npm install -g network-ai) 中，并且不属于此技能包的一部分。

## 原文

# 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:

```bash
# 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:

```bash
# 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:**
| Verdict | Action |
|---------|--------|
| `APPROVED` | Commit and return results to user |
| `WARNING` | Review issues, fix if possible, then commit |
| `BLOCKED` | Do NOT return results. Report failure. |

---

## 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:**

```bash
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:

```bash
# 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:

```bash
# 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. Use the Node.js CLI (optional — requires `npm install -g network-ai`)

The CLI gives direct terminal access to all four subsystems without running a server:

```bash
# Blackboard
network-ai bb get task:q4_analysis
network-ai bb set task:q4_analysis '{"status": "complete"}' --agent orchestrator
network-ai bb list
network-ai bb snapshot

# Permissions
network-ai auth token data_analyst --resource DATABASE --action read \
  --justification "Need Q4 invoices for revenue report"
network-ai auth check grant_a1b2c3...
network-ai auth revoke grant_a1b2c3...

# Budget
network-ai budget status
network-ai budget set-ceiling 50000

# Audit log
network-ai audit log --limit 50
network-ai audit tail          # live-stream as events arrive
```

Global flags: `--data <path>` (override data directory) · `--json` (machine-readable output)

## Agent-to-Agent Handoff Protocol

When delegating tasks between agents/sessions:

### Step 1: Initialize Budget & Check Capacity
```bash
# 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:
| Agent | Specialty |
|-------|-----------|
| `data_a