x0tta6bl4-mesh-orchestrator

name: x0tta6bl4-mesh-orchestrator description: > Self-healing mesh orchestration with PQC, Zero-Trust, DAO governance and MAPE-K autonomic loop. Use when user says "deploy mesh", "self-heal topology", "DAO vote config", "PQC rotate keys", "mesh orchestrate", "crisis recovery", "edge setup", "run MAPE-K cycle", "simulate failure", or "mesh status". Coordinates eBPF telemetry, GraphSAGE anomaly detection, quadratic voting, post-quantum key rotation, and blue-green deployments. license: Apache-2.0 metadata: author: x0tta6bl4 DAO version: 2026.02.04-H1 mcp-server: x0tta6bl4-mcp category: mesh-devops tags: [mesh, mape-k, pqc, dao, self-healing, ebpf, orchestration]

x0tta6bl4 Mesh Orchestrator

End-to-end orchestration of the x0tta6bl4 self-healing mesh network. Implements the full MAPE-K autonomic cycle with DAO-governed configuration changes and post-quantum cryptographic key rotation.

Instructions (MAPE-K Orchestration Cycle)

Step 1: Monitor (eBPF Telemetry)

Collect real-time metrics from the mesh network via eBPF probes.

python3 skills/x0tta6bl4-mesh-orchestrator/scripts/validate-mesh.py --metrics

This script collects:

• Network: p99 latency, packet loss, throughput (target: latency < 40ms, loss < 1%)
• Performance: CPU, memory, context switches, syscalls
• Security: failed auth attempts, suspicious file access, privilege escalation
• Mesh: peer count, route count, MTTR

Key classes:

from src.network.ebpf.ebpf_metrics_collector import EBPFMetricsCollector
from src.network.ebpf.telemetry_module import EBPFTelemetryCollector, TelemetryConfig

collector = EBPFMetricsCollector()
metrics = collector.collect_all_metrics()
# metrics = {'performance': PerformanceMetrics, 'network': NetworkMetrics, 'security': SecurityMetrics}

Thresholds for anomaly detection:

Step 2: Analyze (GraphSAGE + Causal)

If metrics exceed warning thresholds, run anomaly detection:

from src.ml.graphsage_anomaly_detector import GraphSAGEAnomalyDetector

detector = GraphSAGEAnomalyDetector(anomaly_threshold=0.6)
prediction = detector.predict(
    node_id="node-1",
    node_features={
        "rssi": metrics['network'].latency_ms,
        "snr": 15.0,
        "loss_rate": metrics['network'].packet_loss_percent / 100,
        "latency": metrics['network'].latency_ms,
        "cpu": metrics['performance'].cpu_percent / 100,
        "memory": metrics['performance'].memory_percent / 100,
    },
    neighbors=[],  # list of (peer_id, {feature_dict}) tuples from mesh topology
)
# prediction.is_anomaly, prediction.anomaly_score, prediction.confidence

For root cause analysis, use the integrated MAPE-K analyzer:

from src.self_healing.mape_k_integrated import IntegratedMAPEKCycle

mape_k = IntegratedMAPEKCycle(enable_observe_mode=True)
result = mape_k.run_cycle(metrics=collected_metrics)
# result['analyzer_results']['root_cause']

Step 3: Plan (DAO Governance)

If anomaly confirmed with high confidence (> 0.8), propose configuration change via DAO:

python3 skills/x0tta6bl4-mesh-orchestrator/scripts/dao-vote.py \
  --action "topology_update" \
  --title "Auto-heal: reroute around node-3" \
  --duration 300

Key governance functions:

from src.dao.governance import GovernanceEngine, VoteType

governance = GovernanceEngine(node_id="orchestrator")
proposal = governance.create_proposal(
    title="Topology update: reroute around failed node",
    description="GraphSAGE detected anomaly score 0.92 on node-3",
    duration_seconds=300,  # 5 min emergency vote
    quorum=0.33,
    threshold=0.5
)
# Quadratic voting: voting_power = sqrt(tokens)
governance.cast_vote(proposal.id, voter_id, VoteType.YES, tokens=100)
governance.check_proposals()  # Tally when expired

Decision matrix:

Step 4: Execute (Self-Heal + PQC Rotate)

After DAO approval (or auto-execute for critical):

4a. Self-healing execution:

from src.self_healing.mape_k_integrated import IntegratedMAPEKCycle

mape_k = IntegratedMAPEKCycle()
result = mape_k.run_cycle(metrics)
# result['executor_results']['success'] == True
# result['executor_results']['recovery_time'] < 180  # MTTR < 3 min

4b. PQC key rotation (if security event):

from src.security.post_quantum import PQMeshSecurityLibOQS

pq = PQMeshSecurityLibOQS(node_id="node-1", kem_algorithm="ML-KEM-768", sig_algorithm="ML-DSA-65")
new_keys = pq.get_public_keys()
# Distribute new_keys to all peers via mesh routing

4c. Blue-green deployment (if config change):

# Apply new K8s config
kubectl apply -f skills/x0tta6bl4-mesh-orchestrator/assets/mesh-deployment.yaml

# Verify health
curl -s http://localhost:8080/health | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['status'])"

Step 5: Verify + Knowledge Update

After execution, verify recovery:

python3 skills/x0tta6bl4-mesh-orchestrator/scripts/validate-mesh.py --verify

Checks:

• All critical metrics back within normal range
• MTTR < 3 minutes (measured)
• No cascading failures predicted
• PQC keys rotated if security event
• DAO proposal executed if governance-gated

Run digital twin simulation to validate stability:

from src.simulation.digital_twin import MeshDigitalTwin

twin = MeshDigitalTwin("post-heal-sim")
twin.create_test_topology(num_nodes=10)
result = twin.simulate_node_failure("node-3", duration_seconds=60)
assert result.mttr_seconds < 180
assert result.connectivity_maintained > 0.95

Examples

Example 1: Self-heal after node outage

User: "Self-heal mesh after outage on node-3"

Actions:
1. Run validate-mesh.py --metrics → detect node-3 unreachable
2. GraphSAGE anomaly_score=0.95 → auto-execute (critical)
3. MAPE-K executor: reroute traffic via node-2, node-4
4. Rotate PQC keys for affected channels
5. validate-mesh.py --verify → MTTR=2.1min, all green

Example 2: DAO-governed config update

User: "DAO vote to update routing algorithm"

Actions:
1. Create proposal via governance.create_proposal()
2. Distribute to all voting nodes
3. Collect quadratic votes for 1 hour
4. If passed: apply new routing config via MAPE-K executor
5. Validate with digital twin simulation

Example 3: PQC key rotation

User: "PQC rotate keys for all nodes"

Actions:
1. Generate new ML-KEM-768 + ML-DSA-65 keypairs per node
2. Distribute public keys via authenticated mesh channels
3. Re-establish secure channels with new keys
4. Verify with beacon signing test
5. Log rotation event to audit trail

Example 4: Crisis edge deployment

User: "Deploy edge node in disconnected environment"

Actions:
1. Generate offline PQC keypair
2. Pre-load mesh routing table with known peers
3. Deploy via assets/mesh-deployment.yaml with offline=true
4. On reconnect: sync state via CRDT, re-establish PQC channels
5. Run MAPE-K cycle to integrate into mesh

Troubleshooting

Error: "PQC key invalid"

Cause: ML-KEM-768 keypair expired or corrupted Solution: Force key rotation:

from src.security.post_quantum import PQMeshSecurityLibOQS
pq = PQMeshSecurityLibOQS("node-id")
new_keys = pq.get_public_keys()  # Generates fresh keypair

Error: "DAO quorum not reached"

Cause: Not enough voting nodes participated Solution: Lower quorum threshold for emergency or extend voting duration:

proposal = governance.create_proposal(title="...", quorum=0.25, duration_seconds=7200)

Error: "MAPE-K cycle timeout"

Cause: Analysis phase stuck (GraphSAGE model too slow) Solution: Fall back to rule-based detection:

import src.ml.graphsage_anomaly_detector as mod
mod._TORCH_AVAILABLE = False  # Force rule-based fallback

Error: "Mesh connectivity < 95%"

Cause: Cascading failure after node removal Solution: Run digital twin prediction before executing:

twin = MeshDigitalTwin("pre-check")
cascade = twin.predict_cascade_failure("failing-node", threshold=0.5)
if len(cascade) > 2:
    print("WARNING: cascade risk, manual review required")

References

Consult these files for detailed API documentation:

• references/api-guide.md - Full API patterns for all subsystems
• references/pqc-algorithms.md - Post-quantum algorithm details
• assets/mesh-deployment.yaml - K8s deployment template