Fix skill cross-links and add missing sre-dashboards skill

devops/ai/ai-pipeline-orchestration/SKILL.md

@@ -256,7 +256,7 @@ nightly_refresh = ScheduleDefinition(
 
 ## Related Skills
 
-- [rag-infrastructure](../../infrastructure/local-ai/rag-infrastructure/) - RAG system setup
+- [rag-infrastructure](../../../infrastructure/local-ai/rag-infrastructure/) - RAG system setup
-- [llm-fine-tuning](../../infrastructure/local-ai/llm-fine-tuning/) - Training jobs
+- [llm-fine-tuning](../../../infrastructure/local-ai/llm-fine-tuning/) - Training jobs
 - [agent-observability](../agent-observability/) - Pipeline monitoring
-- [kubernetes-ops](../orchestration/kubernetes-ops/) - Running pipeline pods on K8s
+- [kubernetes-ops](../../orchestration/kubernetes-ops/) - Running pipeline pods on K8s

devops/ai/llm-caching/SKILL.md

@@ -304,6 +304,6 @@ tcp-keepalive 60
 ## Related Skills
 
 - [llm-cost-optimization](../llm-cost-optimization/) - Full cost strategy
-- [llm-gateway](../../infrastructure/networking/llm-gateway/) - Gateway-level caching
+- [llm-gateway](../../../infrastructure/networking/llm-gateway/) - Gateway-level caching
-- [vector-database-ops](../../infrastructure/databases/vector-database-ops/) - Qdrant setup
+- [vector-database-ops](../../../infrastructure/databases/vector-database-ops/) - Qdrant setup
 - [agent-observability](../agent-observability/) - Cache metrics dashboards

devops/ai/llm-cost-optimization/SKILL.md

@@ -280,7 +280,7 @@ def track_call(model, team, task_type, response):
 
 ## Related Skills
 
-- [llm-gateway](../../infrastructure/networking/llm-gateway/) - Centralized cost control
+- [llm-gateway](../../../infrastructure/networking/llm-gateway/) - Centralized cost control
 - [llm-caching](../llm-caching/) - Semantic caching patterns
-- [vllm-server](../../infrastructure/local-ai/vllm-server/) - Self-hosted inference
+- [vllm-server](../../../infrastructure/local-ai/vllm-server/) - Self-hosted inference
 - [agent-observability](../agent-observability/) - Token and cost telemetry

devops/observability/sre-dashboards/SKILL.md

@@ -0,0 +1,118 @@
+---
+name: sre-dashboards
+description: Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.
+license: MIT
+metadata:
+  author: devops-skills
+  version: "1.0"
+---
+
+# SRE Dashboards
+
+Build dashboards that help teams detect, triage, and prevent reliability incidents.
+
+## When to Use This Skill
+
+Use this skill when:
+- Defining service-level dashboards for production systems
+- Tracking SLO health and error-budget burn
+- Creating incident command-center views
+- Standardizing dashboard patterns across teams
+
+## Prerequisites
+
+- Metrics pipeline (Prometheus, OpenTelemetry, or vendor equivalent)
+- Logs/traces linked to services and environments
+- Agreed service taxonomy (team, service, tier, environment)
+
+## Dashboard Architecture
+
+Structure dashboards in layers:
+
+1. **Executive Reliability View**: SLO attainment, incident counts, MTTR trends.
+2. **Service Health View**: RED/USE metrics, dependency health, release markers.
+3. **Deep-Dive View**: Per-endpoint latency, resource saturation, error categories.
+
+Keep each view answer-oriented:
+- *Are customers impacted?*
+- *What changed?*
+- *Where is the bottleneck?*
+
+## Core SRE Panels
+
+### Golden Signals
+
+- **Latency**: p50/p95/p99 request duration by endpoint
+- **Traffic**: request throughput and queue depth
+- **Errors**: 5xx rate, failed jobs, timeout ratio
+- **Saturation**: CPU, memory, disk I/O, thread/connection pool exhaustion
+
+### SLO Panels
+
+- Current SLI value (rolling windows: 5m, 1h, 24h, 30d)
+- Error-budget remaining (%)
+- Burn-rate panels (fast and slow windows)
+- Multi-window burn alert status
+
+### Change Correlation
+
+- Deployment markers and config-change annotations
+- Feature flag state overlays
+- Upstream/downstream dependency error rates
+
+## Example PromQL Snippets
+
+```promql
+# API error rate (%)
+100 * sum(rate(http_requests_total{status=~"5.."}[5m]))
+  / sum(rate(http_requests_total[5m]))
+```
+
+```promql
+# p95 latency by route
+histogram_quantile(0.95,
+  sum by (le, route) (rate(http_request_duration_seconds_bucket[5m]))
+)
+```
+
+```promql
+# Fast burn rate (5m / 1h)
+(
+  sum(rate(http_requests_total{status=~"5.."}[5m]))
+  / sum(rate(http_requests_total[5m]))
+)
+/
+(
+  sum(rate(http_requests_total{status=~"5.."}[1h]))
+  / sum(rate(http_requests_total[1h]))
+)
+```
+
+## Operational Guidelines
+
+- Use consistent color semantics (green=healthy, yellow=degrading, red=breach)
+- Label units explicitly (ms, req/s, %, cores)
+- Default time windows to incident-friendly ranges (15m, 1h, 6h, 24h)
+- Minimize panel count per dashboard to reduce cognitive load
+- Add runbook links directly in panel descriptions
+
+## Troubleshooting
+
+### Panel appears flat or empty
+
+- Verify label cardinality and filters (`service`, `env`, `region`)
+- Confirm scrape/ingest latency is within expected range
+- Check metric rename regressions after instrumentation updates
+
+### High cardinality slows dashboards
+
+- Aggregate by stable dimensions (`service`, `route_group`) instead of raw IDs
+- Use recording rules for expensive percentile and ratio queries
+- Split deep-dive dashboards from NOC summary dashboards
+
+## Related Skills
+
+- [prometheus-grafana](../prometheus-grafana/) - Dashboard implementation and PromQL
+- [opentelemetry](../opentelemetry/) - Standardized telemetry instrumentation
+- [alerting-oncall](../alerting-oncall/) - Reliability alert routing and escalation
+- [agent-observability](../../ai/agent-observability/) - AI workload reliability telemetry

devops/orchestration/model-serving-kubernetes/SKILL.md

@@ -308,7 +308,7 @@ kubectl get inferenceservice llama-3-8b -n models -w
 
 ## Related Skills
 
-- [vllm-server](../../infrastructure/local-ai/vllm-server/) - vLLM for LLM serving
+- [vllm-server](../../../infrastructure/local-ai/vllm-server/) - vLLM for LLM serving
-- [llm-inference-scaling](../../infrastructure/local-ai/llm-inference-scaling/) - KEDA autoscaling
+- [llm-inference-scaling](../../../infrastructure/local-ai/llm-inference-scaling/) - KEDA autoscaling
-- [kubernetes-ops](./kubernetes-ops/) - Core Kubernetes operations
+- [kubernetes-ops](../kubernetes-ops/) - Core Kubernetes operations
-- [gpu-server-management](../../infrastructure/servers/gpu-server-management/) - GPU nodes
+- [gpu-server-management](../../../infrastructure/servers/gpu-server-management/) - GPU nodes

infrastructure/databases/vector-database-ops/SKILL.md

@@ -281,5 +281,5 @@ while True:
 ## Related Skills
 
 - [rag-infrastructure](../../local-ai/rag-infrastructure/) - Full RAG pipeline
-- [databases](../databases/) - General database management
+- [databases](../) - General database management
 - [postgresql](../postgresql/) - pgvector host database ops

infrastructure/local-ai/openclaw-security-hardening/SKILL.md

@@ -106,4 +106,4 @@ Minimum operational runbook:
 
 - [openclaw-local-mac-mini](../openclaw-local-mac-mini/) - Local OpenClaw hosting setup
 - [multi-tenant-llm-hosting](../multi-tenant-llm-hosting/) - Multi-tenant AI isolation patterns
-- [zero-trust](../../networking/zero-trust/) - Private access and identity-aware network controls
+- [zero-trust](../../../security/network/zero-trust/) - Private access and identity-aware network controls