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Render Monitor

RenderOfficialCreated by Render

  • Skill
  • Tool integration
  • Cloud and DevOps
  • MIT
  • No issues found

From the author

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.
— the description by Render

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The render-monitor skill provides a structured approach to observing the health and performance of services hosted on Render. It enables users to perform real-time checks on service status, resource utilization, HTTP latency, and database health. By leveraging MCP tools or CLI commands, the skill facilitates log analysis, error tracking, and deployment verification, ensuring that users can maintain stable and performant applications.

What it does

  • Checks the health status of active Render services.
  • Retrieves performance metrics including CPU, memory, and HTTP latency.
  • Monitors and filters logs to identify errors or specific events.
  • Verifies the status of recent deployments.
  • Provides database monitoring for PostgreSQL instances and key-value stores.
  • Offers fallback CLI commands for environments where MCP tools are unavailable.

When to use it

  • When investigating slow application performance or high latency.
  • To verify if a recent deployment is healthy and live.
  • When troubleshooting errors by inspecting service logs.
  • To monitor resource usage trends for capacity planning.
  • When checking the status of database connections or query performance.

Advantages

  • Provides a unified interface for both MCP and CLI-based monitoring.
  • Includes clear health thresholds for CPU, memory, and latency metrics.
  • Supports granular log filtering by level, text, and time range.
  • Enables direct database inspection through query execution.
  • Integrates with standard Render documentation workflows for up-to-date information.

Usage examples

“Is my web service healthy right now?”
The agent will list the service status, check the latest deployment, and review recent error logs to provide a health summary.
“Why is my API responding slowly?”
The agent will retrieve p95 HTTP latency metrics and check CPU/memory usage to identify potential bottlenecks.
“Show me the last 20 error logs for my service.”
The agent will execute a log retrieval command filtered by the 'error' level for the specified service ID.
“Are there any slow queries in my database?”
The agent will query the database's pg_stat_statements to identify and list the queries with the highest mean execution time.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/render-oss/render-monitor/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip render-monitor.zip -d .agents/skills

Documentation by Render

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"]
)
MetricHealthyWarningCritical
CPU<70%70-85%>85%
Memory<80%80-90%>90%
HTTP Latency
get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["http_latency"],
  httpLatencyQuantile: 0.95
)
p95 LatencyStatus
<200msExcellent
200-500msGood
500ms-1sConcerning
>1sProblem
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


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)
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:

# 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
IndicatorHealthyWarningCritical
Error Rate<0.1%0.1-1%>1%
p95 Latency<500ms500ms-2s>2s
CPU Usage<70%70-90%>90%
Memory Usage<80%80-95%>95%

References

  • 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 -->

Files

references/deployments.md

<!-- 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 version is live. Before triggering a deploy, confirm the workspace, service, intended source commit or image, and current live deploy. Capture the new deploy ID, monitor it to a terminal state, and do not report success until its status is live and the deployed workload has been verified.

Before configuring or troubleshooting deployments and health checks, fetch the current Render references:

Using the documentation-retrieval workflow in the root SKILL.md, retrieve and read both references.

Read the relevant sections of both documents. Confirm current service-type support, command timeouts, health-check thresholds, rollout behavior, and configuration syntax rather than relying on remembered values.

Automatic deployment behavior depends on the service's source and settings. A service linked to a branch through a connected Git provider can deploy on each commit, wait for supported CI checks, or require manual triggers. Services that pull a prebuilt image and services linked by a public Git repository URL require manual deploys; moving a mutable image tag does not itself trigger one. When manually deploying a historical commit, verify whether that trigger method changes automatic-deploy settings. If automatic deploys remain enabled, a later branch update can replace the selected commit.

The normal lifecycle is build command, optional pre-deploy command, then start command and readiness checks. If a command fails or times out, the deploy fails and later steps do not run. A pre-deploy command runs after the build on a separate instance before the new version is deployed. Neither the build nor pre-deploy instance can access an attached persistent disk, and pre-deploy filesystem changes do not carry into the running service. Use pre-deploy for release tasks such as database migrations, not for producing runtime artifacts. Pre-deploy commands are supported for paid web services, private services, and background workers.

Render normally deploys services without downtime by starting the new version before retiring the current one. Web and private services continue routing traffic to the current instances until the new instances are ready. Multi-instance services roll out one instance at a time; if a replacement does not become healthy, Render cancels the rollout and retains or restores the previous version. Attaching a persistent disk disables zero-downtime deploys, so plan explicitly for an interruption.

A workflow deployment has a different completion model. It builds and caches the image used by future task runs and includes a task-registration step, but it does not execute those tasks. After a workflow deploy succeeds, verify that the expected task definitions are registered. Trigger and monitor a task run separately only when execution is intended. For workflow-specific deployment behavior, fetch the current workflow overview:

Using the documentation-retrieval workflow in the root SKILL.md, retrieve and read the Markdown reference or its HTML version.

Health checks apply to web services and private services. Both use TCP socket checks by default. Only web services support a configured HTTP health-check path. Render sends an HTTP GET; any 2xx or 3xx response received within five seconds succeeds, while other status codes, timeouts, and connection failures fail. Private services support TCP checks only.

For a new deploy, all new instances must pass their checks at the same time within the current readiness window, documented as 15 minutes, before Render routes traffic to them. For a running service, consecutive failures currently remove an instance from routing after 15 seconds and restart it after 60 seconds. Treat these thresholds as platform behavior, not user-tunable health-check interval settings, and verify them in the fetched reference before relying on exact timing.

Design an HTTP health endpoint to represent readiness for production traffic. Keep it fast and side-effect free. Check critical dependencies only when losing that dependency should make the entire instance unavailable; otherwise a transient dependency failure can unnecessarily remove healthy application capacity. Verify the configured path, response status, response time, listening port, and 0.0.0.0 binding when checks fail.

After traffic moves, Render gives the previous instance a drain period, then sends SIGTERM. The application should stop accepting new work, finish or safely abandon in-flight work, close connections, and exit within its shutdown delay. Ensure the actual application process receives the signal: container entrypoints and shell wrappers should use exec or explicitly forward signals. Render sends SIGKILL if the process remains running after the delay. The documented default shutdown delay is 30 seconds and the usual maximum is 300 seconds, but support varies by service configuration; confirm the current reference and Blueprint or API validation before setting maxShutdownDelaySeconds.

A service restart is a special deploy of the same commit and user-defined environment-variable values as the current running instance. It does not incorporate configuration changes saved after that deploy. Use a standard deploy when pending configuration must take effect.

Only one deploy runs at a time for a service. A workspace's overlapping-deploy policy determines whether a new trigger waits or cancels the in-progress deploy. Do not assume the policy or wait for every intermediate revision: inspect the workspace setting and identify the deploy that actually became live.

Treat rollback as a new deploy based on a retained previous build artifact, not as restoration of the entire service to an earlier point in time. It does not reverse database migrations, messages, external writes, or other side effects from the release, so confirm that the previous application version remains compatible with current external state. Disks and their data are not rolled back, some settings continue using current configuration, mutable image tags can resolve to a different image, and eligible artifacts are retained only for a limited period. Before rolling back, fetch the current rollback reference:

Using the documentation-retrieval workflow in the root SKILL.md, retrieve and read the Markdown reference or its HTML version.

Read which configuration comes from the target deploy and which remains current. After the rollback is created, inspect the service's resulting automatic-deploy setting instead of assuming whether it remains enabled, and deliberately restore the intended setting after the underlying issue is resolved.

Deploy hooks are secret credentials that authorize deploys. Never print, commit, or expose their full URLs. Before using one, fetch the current deploy-hook reference:

Using the documentation-retrieval workflow in the root SKILL.md, retrieve and read the Markdown reference or its HTML version.

Distinguish an immediately started deploy from a queued one, and continue monitoring the resulting deploy rather than treating the HTTP response as completion.

After a deploy becomes live, verify the behavior appropriate to the service type: make a request to a web or private service, confirm a worker is processing work, confirm a cron job or workflow invocation completes, and inspect recent runtime logs for new errors. If a deploy fails, diagnose the first failed lifecycle stage before retrying.

<!-- /shared:deployments -->

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

The author says: Requires Render MCP tools or CLI

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Render
Publisher
Render (Official)
Source
render-oss/skills · skills/render-monitor · commit b985a63
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download render-monitor.zip