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Coordinates multiple agents, decomposes tasks, manages shared state via a local blackboard file, and enforces permission walls before sensitive operations. All execution is local and sandboxed.\nmetadata:\n  openclaw:\n    emoji: \"\\U0001F41D\"\n    homepage: https://github.com/jovanSAPFIONEER/Network-AI\n    requires:\n      bins:\n        - python3\n---\n\n# Swarm Orchestrator Skill\n\nMulti-agent coordination system for complex workflows requiring task delegation, parallel execution, and permission-controlled access to sensitive APIs.\n\n## 🎯 Orchestrator System Instructions\n\n**You are the Orchestrator Agent** responsible for decomposing complex tasks, delegating to specialized agents, and synthesizing results. Follow this protocol:\n\n### Core Responsibilities\n\n1. **DECOMPOSE** complex prompts into 3 specialized sub-tasks\n2. **DELEGATE** using the budget-aware handoff protocol\n3. **VERIFY** results on the blackboard before committing\n4. **SYNTHESIZE** final output only after all validations pass\n\n### Task Decomposition Protocol\n\nWhen you receive a complex request, decompose it into exactly **3 sub-tasks**:\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│                     COMPLEX USER REQUEST                        │\n└─────────────────────────────────────────────────────────────────┘\n                              │\n                              ▼\n        ┌─────────────────────┼─────────────────────┐\n        │                     │                     │\n        ▼                     ▼                     ▼\n┌───────────────┐   ┌───────────────┐   ┌───────────────┐\n│  SUB-TASK 1   │   │  SUB-TASK 2   │   │  SUB-TASK 3   │\n│ data_analyst  │   │ risk_assessor │   │strategy_advisor│\n│    (DATA)     │   │   (VERIFY)    │   │  (RECOMMEND)  │\n└───────────────┘   └───────────────┘   └───────────────┘\n        │                     │                     │\n        └─────────────────────┼─────────────────────┘\n                              ▼\n                    ┌───────────────┐\n                    │  SYNTHESIZE   │\n                    │ orchestrator  │\n                    └───────────────┘\n```\n\n**Decomposition Template:**\n```\nTASK DECOMPOSITION for: \"{user_request}\"\n\nSub-Task 1 (DATA): [data_analyst]\n  - Objective: Extract/process raw data\n  - Output: Structured JSON with metrics\n\nSub-Task 2 (VERIFY): [risk_assessor]  \n  - Objective: Validate data quality & compliance\n  - Output: Validation report with confidence score\n\nSub-Task 3 (RECOMMEND): [strategy_advisor]\n  - Objective: Generate actionable insights\n  - Output: Recommendations with rationale\n```\n\n### Budget-Aware Handoff Protocol\n\n**CRITICAL:** Before EVERY `sessions_send`, call the handoff interceptor:\n\n```bash\n# ALWAYS run this BEFORE sessions_send\npython {baseDir}/scripts/swarm_guard.py intercept-handoff \\\n  --task-id \"task_001\" \\\n  --from orchestrator \\\n  --to data_analyst \\\n  --message \"Analyze Q4 revenue data\"\n```\n\n**Decision Logic:**\n```\nIF result.allowed == true:\n    → Proceed with sessions_send\n    → Note tokens_spent and remaining_budget\nELSE:\n    → STOP - Do NOT call sessions_send\n    → Report blocked reason to user\n    → Consider: reduce scope or abort task\n```\n\n### Pre-Commit Verification Workflow\n\nBefore returning final results to the user:\n\n```bash\n# Step 1: Check all sub-task results on blackboard\npython {baseDir}/scripts/blackboard.py read \"task:001:data_analyst\"\npython {baseDir}/scripts/blackboard.py read \"task:001:risk_assessor\"\npython {baseDir}/scripts/blackboard.py read \"task:001:strategy_advisor\"\n\n# Step 2: Validate each result\npython {baseDir}/scripts/swarm_guard.py validate-result \\\n  --task-id \"task_001\" \\\n  --agent data_analyst \\\n  --result '{\"status\":\"success\",\"output\":{...},\"confidence\":0.85}'\n\n# Step 3: Supervisor review (checks all issues)\npython {baseDir}/scripts/swarm_guard.py supervisor-review --task-id \"task_001\"\n\n# Step 4: Only if APPROVED, commit final state\npython {baseDir}/scripts/blackboard.py write \"task:001:final\" \\\n  '{\"status\":\"SUCCESS\",\"output\":{...}}'\n```\n\n**Verdict Handling:**\n| Verdict | Action |\n|---------|--------|\n| `APPROVED` | Commit and return results to user |\n| `WARNING` | Review issues, fix if possible, then commit |\n| `BLOCKED` | Do NOT return results. Report failure. |\n\n---\n\n## When to Use This Skill\n\n- **Task Delegation**: Route work to specialized agents (data_analyst, strategy_advisor, risk_assessor)\n- **Parallel Execution**: Run multiple agents simultaneously and synthesize results\n- **Permission Wall**: Gate access to SAP_API, FINANCIAL_API, or DATA_EXPORT operations\n- **Shared Blackboard**: Coordinate agent state via persistent markdown file\n\n## Quick Start\n\n### 1. Initialize Budget (FIRST!)\n\n**Always initialize a budget before any multi-agent task:**\n\n```bash\npython {baseDir}/scripts/swarm_guard.py budget-init \\\n  --task-id \"task_001\" \\\n  --budget 10000 \\\n  --description \"Q4 Financial Analysis\"\n```\n\n### 2. Delegate a Task to Another Session\n\nUse OpenClaw's built-in session tools to delegate work:\n\n```\nsessions_list    # See available sessions/agents\nsessions_send    # Send task to another session\nsessions_history # Check results from delegated work\n```\n\n**Example delegation prompt:**\n```\nUse sessions_send to ask the data_analyst session to:\n\"Analyze Q4 revenue trends from the SAP export data and summarize key insights\"\n```\n\n### 3. Check Permission Before API Access\n\nBefore accessing SAP or Financial APIs, evaluate the request:\n\n```bash\n# Run the permission checker script\npython {baseDir}/scripts/check_permission.py \\\n  --agent \"data_analyst\" \\\n  --resource \"SAP_API\" \\\n  --justification \"Need Q4 invoice data for quarterly report\" \\\n  --scope \"read:invoices\"\n```\n\nThe script will output a grant token if approved, or denial reason if rejected.\n\n### 4. Use the Shared Blackboard\n\nRead/write coordination state:\n\n```bash\n# Write to blackboard\npython {baseDir}/scripts/blackboard.py write \"task:q4_analysis\" '{\"status\": \"in_progress\", \"agent\": \"data_analyst\"}'\n\n# Read from blackboard  \npython {baseDir}/scripts/blackboard.py read \"task:q4_analysis\"\n\n# List all entries\npython {baseDir}/scripts/blackboard.py list\n```\n\n## Agent-to-Agent Handoff Protocol\n\nWhen delegating tasks between agents/sessions:\n\n### Step 1: Initialize Budget & Check Capacity\n```bash\n# Initialize budget (if not already done)\npython {baseDir}/scripts/swarm_guard.py budget-init --task-id \"task_001\" --budget 10000\n\n# Check current status\npython {baseDir}/scripts/swarm_guard.py budget-check --task-id \"task_001\"\n```\n\n### Step 2: Identify Target Agent\n```\nsessions_list  # Find available agents\n```\n\nCommon agent types:\n| Agent | Specialty |\n|-------|-----------|\n| `data_analyst` | Data processing, SQL, analytics |\n| `strategy_advisor` | Business strategy, recommendations |\n| `risk_assessor` | Risk analysis, compliance checks |\n| `orchestrator` | Coordination, task decomposition |\n\n### Step 3: Intercept Before Handoff (REQUIRED)\n\n```bash\n# This checks budget AND handoff limits before allowing the call\npython {baseDir}/scripts/swarm_guard.py intercept-handoff \\\n  --task-id \"task_001\" \\\n  --from orchestrator \\\n  --to data_analyst \\\n  --message \"Analyze Q4 data\" \\\n  --artifact  # Include if expecting output\n```\n\n**If ALLOWED:** Proceed to Step 4\n**If BLOCKED:** Stop - do not call sessions_send\n\n### Step 4: Construct Handoff Message\n\nInclude these fields in your delegation:\n- **instruction**: Clear task description\n- **context**: Relevant background information\n- **constraints**: Any limitations or requirements\n- **expectedOutput**: What format/content you need back\n\n### Step 5: Send via sessions_send\n\n```\nsessions_send to data_analyst:\n\"[HANDOFF]\nInstruction: Analyze Q4 revenue by product category\nContext: Using SAP export from ./data/q4_export.csv\nConstraints: Focus on top 5 categories only\nExpected Output: JSON summary with category, revenue, growth_pct\n[/HANDOFF]\"\n```\n\n### Step 4: Check Results\n\n```\nsessions_history data_analyst  # Get the response\n```\n\n## Permission Wall (AuthGuardian)\n\n**CRITICAL**: Always check permissions before accessing:\n- `SAP_API` - SAP system connections\n- `FINANCIAL_API` - Financial data services\n- `EXTERNAL_SERVICE` - Third-party APIs\n- `DATA_EXPORT` - Exporting sensitive data\n\n### Permission Evaluation Criteria\n\n| Factor | Weight | Criteria |\n|--------|--------|----------|\n| Justification | 40% | Must explain specific task need |\n| Trust Level | 30% | Agent's established trust score |\n| Risk Assessment | 30% | Resource sensitivity + scope breadth |\n\n### Using the Permission Script\n\n```bash\n# Request permission\npython {baseDir}/scripts/check_permission.py \\\n  --agent \"your_agent_id\" \\\n  --resource \"FINANCIAL_API\" \\\n  --justification \"Generating quarterly financial summary for board presentation\" \\\n  --scope \"read:revenue,read:expenses\"\n\n# Output if approved:\n# ✅ GRANTED\n# Token: grant_a1b2c3d4e5f6\n# Expires: 2026-02-04T15:30:00Z\n# Restrictions: read_only, no_pii_fields, audit_required\n\n# Output if denied:\n# ❌ DENIED\n# Reason: Justification is insufficient. Please provide specific task context.\n```\n\n### Restriction Types\n\n| Resource | Default Restrictions |\n|----------|---------------------|\n| SAP_API | `read_only`, `max_records:100` |\n| FINANCIAL_API | `read_only`, `no_pii_fields`, `audit_required` |\n| EXTERNAL_SERVICE | `rate_limit:10_per_minute` |\n| DATA_EXPORT | `anonymize_pii`, `local_only` |\n\n## Shared Blackboard Pattern\n\nThe blackboard (`swarm-blackboard.md`) is a markdown file for agent coordination:\n\n```markdown\n# Swarm Blackboard\nLast Updated: 2026-02-04T10:30:00Z\n\n## Knowledge Cache\n### task:q4_analysis\n{\"status\": \"completed\", \"result\": {...}, \"agent\": \"data_analyst\"}\n\n### cache:revenue_summary  \n{\"q4_total\": 1250000, \"growth\": 0.15}\n```\n\n### Blackboard Operations\n\n```bash\n# Write with TTL (expires after 1 hour)\npython {baseDir}/scripts/blackboard.py write \"cache:temp_data\" '{\"value\": 123}' --ttl 3600\n\n# Read (returns null if expired)\npython {baseDir}/scripts/blackboard.py read \"cache:temp_data\"\n\n# Delete\npython {baseDir}/scripts/blackboard.py delete \"cache:temp_data\"\n\n# Get full snapshot\npython {baseDir}/scripts/blackboard.py snapshot\n```\n\n## Parallel Execution\n\nFor tasks requiring multiple agent perspectives:\n\n### Strategy 1: Merge (Default)\nCombine all agent outputs into unified result.\n```\nAsk data_analyst AND strategy_advisor to both analyze the dataset.\nMerge their insights into a comprehensive report.\n```\n\n### Strategy 2: Vote\nUse when you need consensus - pick the result with highest confidence.\n\n### Strategy 3: First-Success\nUse for redundancy - take first successful result.\n\n### Strategy 4: Chain\nSequential processing - output of one feeds into next.\n\n### Example Parallel Workflow\n\n```\n1. sessions_send to data_analyst: \"Extract key metrics from Q4 data\"\n2. sessions_send to risk_assessor: \"Identify compliance risks in Q4 data\"  \n3. sessions_send to strategy_advisor: \"Recommend actions based on Q4 trends\"\n4. Wait for all responses via sessions_history\n5. Synthesize: Combine metrics + risks + recommendations into executive summary\n```\n\n## Security Considerations\n\n1. **Never bypass the permission wall** for gated resources\n2. **Always include justification** explaining the business need\n3. **Use minimal scope** - request only what you need\n4. **Check token expiry** - tokens are valid for 5 minutes\n5. **Validate tokens** - use `python {baseDir}/scripts/validate_token.py TOKEN` to verify grant tokens before use\n6. **Audit trail** - all permission requests are logged\n\n## 📝 Audit Trail Requirements (MANDATORY)\n\n**Every sensitive action MUST be logged to `data/audit_log.jsonl`** to maintain compliance and enable forensic analysis.\n\n### What Gets Logged Automatically\n\nThe scripts automatically log these events:\n- `permission_granted` - When access is approved\n- `permission_denied` - When access is rejected\n- `permission_revoked` - When a token is manually revoked\n- `ttl_cleanup` - When expired tokens are purged\n- `result_validated` / `result_rejected` - Swarm Guard validations\n\n### Log Entry Format\n\n```json\n{\n  \"timestamp\": \"2026-02-04T10:30:00+00:00\",\n  \"action\": \"permission_granted\",\n  \"details\": {\n    \"agent_id\": \"data_analyst\",\n    \"resource_type\": \"DATABASE\",\n    \"justification\": \"Q4 revenue analysis\",\n    \"token\": \"grant_abc123...\",\n    \"restrictions\": [\"read_only\", \"max_records:100\"]\n  }\n}\n```\n\n### Reading the Audit Log\n\n```bash\n# View recent entries (last 10)\ntail -10 {baseDir}/data/audit_log.jsonl\n\n# Search for specific agent\ngrep \"data_analyst\" {baseDir}/data/audit_log.jsonl\n\n# Count actions by type\ncat {baseDir}/data/audit_log.jsonl | jq -r '.action' | sort | uniq -c\n```\n\n### Custom Audit Entries\n\nIf you perform a sensitive action manually, log it:\n\n```python\nimport json\nfrom datetime import datetime, timezone\nfrom pathlib import Path\n\naudit_file = Path(\"{baseDir}/data/audit_log.jsonl\")\nentry = {\n    \"timestamp\": datetime.now(timezone.utc).isoformat(),\n    \"action\": \"manual_data_access\",\n    \"details\": {\n        \"agent\": \"orchestrator\",\n        \"description\": \"Direct database query for debugging\",\n        \"justification\": \"Investigating data sync issue #1234\"\n    }\n}\nwith open(audit_file, \"a\") as f:\n    f.write(json.dumps(entry) + \"\\n\")\n```\n\n## 🧹 TTL Enforcement (Token Lifecycle)\n\nExpired permission tokens are automatically tracked. Run periodic cleanup:\n\n```bash\n# Validate a grant token\npython {baseDir}/scripts/validate_token.py grant_a1b2c3d4e5f6\n\n# List expired tokens (without removing)\npython {baseDir}/scripts/revoke_token.py --list-expired\n\n# Remove all expired tokens\npython {baseDir}/scripts/revoke_token.py --cleanup\n\n# Output:\n# 🧹 TTL Cleanup Complete\n#    Removed: 3 expired token(s)\n#    Remaining active grants: 2\n```\n\n**Best Practice**: Run `--cleanup` at the start of each multi-agent task to ensure a clean permission state.\n\n## ⚠️ Swarm Guard: Preventing Common Failures\n\nTwo critical issues can derail multi-agent swarms:\n\n### 1. The Handoff Tax 💸\n\n**Problem**: Agents waste tokens \"talking about\" work instead of doing it.\n\n**Prevention**:\n```bash\n# Before each handoff, check your budget:\npython {baseDir}/scripts/swarm_guard.py check-handoff --task-id \"task_001\"\n\n# Output:\n# 🟢 Task: task_001\n#    Handoffs: 1/3\n#    Remaining: 2\n#    Action Ratio: 100%\n```\n\n**Rules enforced**:\n- **Max 3 handoffs per task** - After 3, produce output or abort\n- **Max 500 chars per message** - Be concise: instruction + constraints + expected output\n- **60% action ratio** - At least 60% of handoffs must produce artifacts\n- **2-minute planning limit** - No output after 2min = timeout\n\n```bash\n# Record a handoff (with tax checking):\npython {baseDir}/scripts/swarm_guard.py record-handoff \\\n  --task-id \"task_001\" \\\n  --from orchestrator \\\n  --to data_analyst \\\n  --message \"Analyze sales data, output JSON summary\" \\\n  --artifact  # Include if this handoff produces output\n```\n\n### 2. Silent Failure Detection 👻\n\n**Problem**: One agent fails silently, others keep working on bad data.\n\n**Prevention - Heartbeats**:\n```bash\n# Agents must send heartbeats while working:\npython {baseDir}/scripts/swarm_guard.py heartbeat --agent data_analyst --task-id \"task_001\"\n\n# Check if an agent is healthy:\npython {baseDir}/scripts/swarm_guard.py health-check --agent data_analyst\n\n# Output if healthy:\n# 💚 Agent 'data_analyst' is HEALTHY\n#    Last seen: 15s ago\n\n# Output if failed:\n# 💔 Agent 'data_analyst' is UNHEALTHY\n#    Reason: STALE_HEARTBEAT\n#    → Do NOT use any pending results from this agent.\n```\n\n**Prevention - Result Validation**:\n```bash\n# Before using another agent's result, validate it:\npython {baseDir}/scripts/swarm_guard.py validate-result \\\n  --task-id \"task_001\" \\\n  --agent data_analyst \\\n  --result '{\"status\": \"success\", \"output\": {\"revenue\": 125000}, \"confidence\": 0.85}'\n\n# Output:\n# ✅ RESULT VALID\n#    → APPROVED - Result can be used by other agents\n```\n\n**Required result fields**: `status`, `output`, `confidence`\n\n### Supervisor Review\n\nBefore finalizing any task, run supervisor review:\n```bash\npython {baseDir}/scripts/swarm_guard.py supervisor-review --task-id \"task_001\"\n\n# Output:\n# ✅ SUPERVISOR VERDICT: APPROVED\n#    Task: task_001\n#    Age: 1.5 minutes\n#    Handoffs: 2\n#    Artifacts: 2\n```\n\n**Verdicts**:\n- `APPROVED` - Task healthy, results usable\n- `WARNING` - Issues detected, review recommended\n- `BLOCKED` - Critical failures, do NOT use results\n\n## Troubleshooting\n\n### Permission Denied\n- Provide more specific justification (mention task, purpose, expected outcome)\n- Narrow the requested scope\n- Check agent trust level\n\n### Blackboard Read Returns Null\n- Entry may have expired (check TTL)\n- Key may be misspelled\n- Entry was never written\n\n### Session Not Found\n- Run `sessions_list` to see available sessions\n- Session may need to be started first\n\n## References\n\n- [AuthGuardian Details](references/auth-guardian.md) - Full permission system documentation\n- [Blackboard Schema](references/blackboard-schema.md) - Data structure specifications\n- [Agent Trust Levels](references/trust-levels.md) - How trust is calculated\n","readmeExcerpt":"--- name: Network-AI description: Multi-agent swarm orchestration for complex workflows. Coordinates multiple agents, decomposes tasks, manages shared state via a local blackboard file, and enforces permission walls before sensitive operations. All execution is local and sandboxed. metadata: openclaw: emoji: \"\\U0001F41D\" homepage: https://github.com/jovanSAPFIONEER/Network-AI requires: bins: - python3 --- Swarm Orche","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"┌─────────────────────────────────────────────────────────────────┐\n│                     COMPLEX USER REQUEST                        │\n└─────────────────────────────────────────────────────────────────┘\n                              │\n                              ▼\n        ┌─────────────────────┼─────────────────────┐\n        │                     │                     │\n        ▼                     ▼                     ▼\n┌───────────────┐   ┌───────────────┐   ┌───────────────┐\n│  SUB-TASK 1   │   │  SUB-TASK 2   │   │  SUB-TASK 3   │\n│ data_analyst  │   │ risk_assessor │   │strategy_advisor│\n│    (DATA)     │   │   (VERIFY)    │   │  (RECOMMEND)  │\n└───────────────┘   └───────────────┘   └───────────────┘\n        │                     │                     │\n        └─────────────────────┼─────────────────────┘\n                              ▼\n                    ┌───────────────┐\n                    │  SYNTHESIZE   │\n                    │ orchestrator  │\n                    └───────────────┘"},{"language":"text","snippet":"TASK DECOMPOSITION for: \"{user_request}\"\n\nSub-Task 1 (DATA): [data_analyst]\n  - Objective: Extract/process raw data\n  - Output: Structured JSON with metrics\n\nSub-Task 2 (VERIFY): [risk_assessor]  \n  - Objective: Validate data quality & compliance\n  - Output: Validation report with confidence score\n\nSub-Task 3 (RECOMMEND): [strategy_advisor]\n  - Objective: Generate actionable insights\n  - Output: Recommendations with rationale"},{"language":"bash","snippet":"# ALWAYS run this BEFORE sessions_send\npython {baseDir}/scripts/swarm_guard.py intercept-handoff \\\n  --task-id \"task_001\" \\\n  --from orchestrator \\\n  --to data_analyst \\\n  --message \"Analyze Q4 revenue data\""},{"language":"text","snippet":"IF result.allowed == true:\n    → Proceed with sessions_send\n    → Note tokens_spent and remaining_budget\nELSE:\n    → STOP - Do NOT call sessions_send\n    → Report blocked reason to user\n    → Consider: reduce scope or abort task"},{"language":"bash","snippet":"# Step 1: Check all sub-task results on blackboard\npython {baseDir}/scripts/blackboard.py read \"task:001:data_analyst\"\npython {baseDir}/scripts/blackboard.py read \"task:001:risk_assessor\"\npython {baseDir}/scripts/blackboard.py read \"task:001:strategy_advisor\"\n\n# Step 2: Validate each result\npython {baseDir}/scripts/swarm_guard.py validate-result \\\n  --task-id \"task_001\" \\\n  --agent data_analyst \\\n  --result '{\"status\":\"success\",\"output\":{...},\"confidence\":0.85}'\n\n# Step 3: Supervisor review (checks all issues)\npython {baseDir}/scripts/swarm_guard.py supervisor-review --task-id \"task_001\"\n\n# Step 4: Only if APPROVED, commit final state\npython {baseDir}/scripts/blackboard.py write \"task:001:final\" \\\n  '{\"status\":\"SUCCESS\",\"output\":{...}}'"},{"language":"bash","snippet":"python {baseDir}/scripts/swarm_guard.py budget-init \\\n  --task-id \"task_001\" \\\n  --budget 10000 \\\n  --description \"Q4 Financial Analysis\""}],"parameters":{},"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["typescript"],"docsSourceLabel":"GITHUB OPENCLEW","editorialOverview":"Multi-agent swarm orchestration for complex workflows. 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