render-monitor
Monitor Render services in real-time. Check health, performance metrics, logs, and resource usage. Use when users want to check service status, view metrics, monitor performance, or verify deployments are healthy.
Savant verdict: Review advised
Security analysis or source-hub review identified patterns requiring attention.
Live evaluation
Not evaluated yet. Workspaces can request a live evaluation.
Security assessment
Risk score
26/100
Recommendation
CAUTION
Severity
MEDIUM
Savant decision
Review advised
The assessment recommends caution with a risk score of 20/100 or more; review its findings before use. Static security analysis completed.
2 patterns found
- Prompt Injection: <!-- shared:compute-plans --> ## Compute plans A compute plan determines the CPU and RAM available to each instance of a Render service. Use **compute plan**, not the former term **instance type**. Dhigh · references/compute-plans.md:1 · Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.
- Prompt Injection: <!-- shared:deployments --> ## Deployments and health checks A Render deploy is asynchronous. A successful trigger means only that Render accepted or queued the deploy; it does not mean the new versihigh · references/deployments.md:1 · Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.
Structure
- The source hub's security checks flagged this skillSnyk: warn (Risk: MEDIUM · 1 issue); Runlayer: warn (2/2 files flagged)
SKILL.md
--- name: render-monitor description: Monitor Render services in real-time. Check health, performance metrics, logs, and resource usage. Use when users want to check service status, view metrics, monitor performance, or verify deployments are healthy. license: MIT compatibility: Requires Render MCP tools or CLI metadata: author: Render version: "1.0.0" category: monitoring --- # Monitor Render Services Real-time monitoring of Render services including health checks, performance metrics, and logs. ## When to Use This Skill Activate this skill when users want to: - Check if services are healthy - View performance metrics - Monitor logs - Verify a deployment is working - Investigate slow performance - Check database health ## Render Access Before reading service state, logs, metrics, or database health, follow `references/render-access.md` to establish access and verify the intended workspace. Prefer MCP when available; the CLI can provide service status and logs, while metrics and database queries require MCP. Before interpreting deploy or health-check state, read `references/deployments.md`. --- ## Quick Health Check Run these 5 checks to assess service health: ``` # 1. Check service status list_services() # 2. Check latest deploy list_deploys(serviceId: "<service-id>", limit: 1) # 3. Check for errors list_logs(resource: ["<service-id>"], level: ["error"], limit: 20) # 4. Check resource usage get_metrics(resourceId: "<service-id>", metricTypes: ["cpu_usage", "memory_usage"]) # 5. Check latency get_metrics(resourceId: "<service-id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95) ``` --- ## Service Health ### Check Status ``` list_services() ``` ``` get_service(serviceId: "<id>") ``` ### Check Deployments ``` list_deploys(serviceId: "<service-id>", limit: 5) ``` Follow `references/deployments.md` to interpret asynchronous and terminal states, identify the specific deploy, and verify the workload after it becomes live. ### Check Errors ``` list_logs(resource: ["<service-id>"], level: ["error"], limit: 50) ``` ``` list_logs(resource: ["<service-id>"], statusCode: ["500", "502", "503"], limit: 50) ``` --- ## Performance Metrics ### CPU & Memory ``` get_metrics( resourceId: "<service-id>", metricTypes: ["cpu_usage", "memory_usage", "cpu_limit", "memory_limit"] ) ``` | Metric | Healthy | Warning | Critical | |--------|---------|---------|----------| | CPU | <70% | 70-85% | >85% | | Memory | <80% | 80-90% | >90% | ### HTTP Latency ``` get_metrics( resourceId: "<service-id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95 ) ``` | p95 Latency | Status | |-------------|--------| | <200ms | Excellent | | 200-500ms | Good | | 500ms-1s | Concerning | | >1s | Problem | ### Request Count ``` get_metrics( resourceId: "<service-id>", metricTypes: ["http_request_count"] ) ``` ### Filter by Endpoint ``` get_metrics( resourceId: "<service-id>", metricTypes: ["http_latency"], httpPath: "/api/users" ) ``` Detailed metrics guide: [references/metrics-guide.md](references/metrics-guide.md) --- ## Database Monitoring ### PostgreSQL Status ``` list_postgres_instances() get_postgres(postgresId: "<postgres-id>") ``` ### Connection Count ``` get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"]) ``` ### Query Database ``` query_render_postgres( postgresId: "<postgres-id>", sql: "SELECT state, count(*) FROM pg_stat_activity GROUP BY state" ) ``` ### Find Slow Queries ``` query_render_postgres( postgresId: "<postgres-id>", sql: "SELECT query, mean_exec_time FROM pg_stat_statements ORDER BY mean_exec_time DESC LIMIT 10" ) ``` ### Key-Value Store ``` list_key_value() get_key_value(keyValueId: "<kv-id>") ``` --- ## Log Monitoring ### Recent Logs ``` list_logs(resource: ["<service-id>"], limit: 100) ``` ### Error Logs ``` list_logs(resource: ["<service-id>"], level: ["error"], limit: 50) ``` ### Search Logs ``` list_logs(resource: ["<service-id>"], text: ["timeout", "error"], limit: 50) ``` ### Filter by Time ``` list_logs( resource: ["<service-id>"], startTime: "2024-01-15T10:00:00Z", endTime: "2024-01-15T11:00:00Z" ) ``` ### Stream Logs (CLI) ```bash render logs -r <service-id> --tail -o text ``` --- ## Quick Reference ### MCP Tools ``` # Services list_services() get_service(serviceId: "<id>") list_deploys(serviceId: "<id>", limit: 5) # Logs list_logs(resource: ["<id>"], level: ["error"], limit: 100) list_logs(resource: ["<id>"], text: ["search"], limit: 50) # Metrics get_metrics(resourceId: "<id>", metricTypes: ["cpu_usage", "memory_usage"]) get_metrics(resourceId: "<id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95) get_metrics(resourceId: "<id>", metricTypes: ["http_request_count"]) # Database list_postgres_instances() get_postgres(postgresId: "<id>") query_render_postgres(postgresId: "<id>", sql: "SELECT ...") get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"]) # Key-Value list_key_value() get_key_value(keyValueId: "<id>") ``` ### CLI Commands (Fallback) Use these if MCP tools are unavailable: ```bash # Service status render services -o json render services instances <service-id> # Deployments render deploys list <service-id> -o json # Logs render logs -r <service-id> --tail -o text # Stream logs render logs -r <service-id> --level error -o json # Error logs render logs -r <service-id> --type deploy -o json # Build logs # Database render psql <database-id> # Connect to PostgreSQL # SSH for live debugging render ssh <service-id> ``` ### Healthy Service Indicators | Indicator | Healthy | Warning | Critical | |-----------|---------|---------|----------| | Error Rate | <0.1% | 0.1-1% | >1% | | p95 Latency | <500ms | 500ms-2s | >2s | | CPU Usage | <70% | 70-90% | >90% | | Memory Usage | <80% | 80-95% | >95% | --- ## References - **Render access:** [references/render-access.md](references/render-access.md) - **Compute plans:** [references/compute-plans.md](references/compute-plans.md) - **Deployments:** [references/deployments.md](references/deployments.md) - **Metrics guide:** [references/metrics-guide.md](references/metrics-guide.md) ## Related Skills - **render-deploy** — Deploy new applications to Render - **render-debug** — Diagnose and fix deployment failures - **render-mcp** — MCP server setup and capability discovery <!-- shared:documentation-retrieval --> ## Current documentation retrieval Whenever this skill directs you to consult current Render documentation: 1. Retrieve the linked Markdown document directly with an available URL-fetching tool or HTTP client, such as `curl`. Do not substitute web-search summaries for the document. 2. Confirm that retrieval succeeded and returned the expected document, then read its contents. Saving a file or printing its path is not sufficient. 3. If the request fails or your tool cannot read the Markdown response, open and read the linked HTML version instead. 4. If neither version can be retrieved, disclose that the current reference is unavailable and follow any topic-specific fallback in the skill. Use bundled guidance only for stable constraints, and do not guess at changeable platform details. When a task requires multiple references, apply this workflow to each one and distinguish the documents you verified from those that remain unavailable. <!-- /shared:documentation-retrieval -->