Render Deploy
OpenAIOfficialPublished by OpenAI
- Skill
- Tool integration
- Cloud and DevOps
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The render-deploy skill streamlines the process of hosting applications on the Render cloud platform. It provides a structured workflow to analyze your codebase, generate necessary configuration files, and execute deployments. The skill supports two primary paths: the Blueprint method for complex, multi-service, or reproducible infrastructure setups, and the Direct Creation method for quick, single-service deployments via MCP tools. It includes comprehensive guidance on prerequisites, authentication, and post-deployment verification to ensure a smooth transition from local development to production.
What it does
- Analyzes codebases to detect frameworks, build commands, and infrastructure requirements.
- Generates render.yaml Blueprint files for Infrastructure-as-Code deployments.
- Facilitates direct service creation for web apps and static sites using MCP tools.
- Provides deep-linking to the Render Dashboard for final configuration and deployment.
- Verifies deployment status, logs, and service health metrics post-launch.
- Guides users through CLI authentication and workspace management.
When to use it
- When you need to deploy a new application or service to Render.
- When you want to define your infrastructure as code using a render.yaml file.
- When you need to set up complex environments including databases, workers, or cron jobs.
- When you want a quick, automated way to deploy a single web service or static site.
- When you need to troubleshoot or verify the health of an existing Render deployment.
Advantages
- Ensures reproducible deployments through version-controlled Blueprint configurations.
- Reduces manual setup time by automating resource creation and environment variable mapping.
- Provides a clear, step-by-step path for both simple and complex application architectures.
- Integrates directly with AI agent workflows for seamless interaction and command execution.
- Includes built-in validation to catch configuration errors before deployment.
Usage examples
- “Deploy my current project to Render.”
- The agent will analyze your repository, ask if you prefer a Blueprint or direct creation, and guide you through the necessary steps to get your app live.
- “Create a render.yaml file for my Node.js app with a Postgres database.”
- The agent will generate a valid Blueprint configuration tailored to your project's needs, including the database definition and required environment variables.
- “Check the status of my deployed service.”
- The agent will use MCP tools to list your recent deployments and check if the service is live, providing logs if any errors are detected.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/openai/render-deploy/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip render-deploy.zip -d .agents/skills
Documentation by OpenAI
Deploy to Render
Render supports Git-backed services and prebuilt Docker image services.
This skill covers Git-backed flows:
Blueprint Method - Generate render.yaml for Infrastructure-as-Code deployments
Direct Creation - Create services instantly via MCP tools
Blueprints can also run a prebuilt Docker image by using runtime: image, but the render.yaml still must live in a Git repo.
If there is no Git remote, stop and ask the user to either:
Create/push a Git remote (can be minimal if only the Blueprint is needed), or
Use the Render Dashboard/API to deploy a prebuilt Docker image (MCP cannot create image-backed services).
Prerequisites
When sandboxing blocks the deployment network calls, rerun with
sandbox_permissions=require_escalated.The deployment might take a few minutes. Use appropriate timeout values.
When to Use This Skill
Activate this skill when users want to:
Deploy an application to Render
Create a render.yaml Blueprint file
Set up Render deployment for their project
Host or publish their application on Render's cloud platform
Create databases, cron jobs, or other Render resources
Happy Path (New Users)
Use this short prompt sequence before deep analysis to reduce friction:
Ask whether they want to deploy from a Git repo or a prebuilt Docker image.
Ask whether Render should provision everything the app needs (based on what seems likely from the user's description) or only the app while they bring their own infra. If dependencies are unclear, ask a short follow-up to confirm whether they need a database, workers, cron, or other services.
Then proceed with the appropriate method below.
Choose Your Source Path
Git Repo Path: Required for both Blueprint and Direct Creation. The repo must be pushed to GitHub, GitLab, or Bitbucket.
Prebuilt Docker Image Path: Supported by Render via image-backed services. This is not supported by MCP; use the Dashboard/API. Ask for:
Image URL (registry + tag)
Registry auth (if private)
Service type (web/worker) and port
If the user chooses a Docker image, guide them to the Render Dashboard image deploy flow or ask them to add a Git remote (so you can use a Blueprint with runtime: image).
Choose Your Deployment Method (Git Repo)
Both methods require a Git repository pushed to GitHub, GitLab, or Bitbucket. (If using runtime: image, the repo can be minimal and only contain render.yaml.)
| Method | Best For | Pros |
|---|---|---|
| Blueprint | Multi-service apps, IaC workflows | Version controlled, reproducible, supports complex setups |
| Direct Creation | Single services, quick deployments | Instant creation, no render.yaml file needed |
Method Selection Heuristic
Use this decision rule by default unless the user requests a specific method. Analyze the codebase first; only ask if deployment intent is unclear (e.g., DB, workers, cron).
Use Direct Creation (MCP) when ALL are true:
Single service (one web app or one static site)
No separate worker/cron services
No attached databases or Key Value
Simple env vars only (no shared env groups) If this path fits and MCP isn't configured yet, stop and guide MCP setup before proceeding.
Use Blueprint when ANY are true:
Multiple services (web + worker, API + frontend, etc.)
Databases, Redis/Key Value, or other datastores are required
Cron jobs, background workers, or private services
You want reproducible IaC or a render.yaml committed to the repo
Monorepo or multi-env setup that needs consistent configuration
If unsure, ask a quick clarifying question, but default to Blueprint for safety. For a single service, strongly prefer Direct Creation via MCP and guide MCP setup if needed.
Prerequisites Check
When starting a deployment, verify these requirements in order:
1. Confirm Source Path (Git vs Docker)
If using Git-based methods (Blueprint or Direct Creation), the repo must be pushed to GitHub/GitLab/Bitbucket. Blueprints that reference a prebuilt image still require a Git repo with render.yaml.
git remote -vIf no remote exists, stop and ask the user to create/push a remote or switch to Docker image deploy.
2. Check MCP Tools Availability (Preferred for Single-Service)
MCP tools provide the best experience. Check if available by attempting:
list_services()If MCP tools are available, you can skip CLI installation for most operations.
3. Check Render CLI Installation (for Blueprint validation)
render --versionIf not installed, offer to install:
macOS:
brew install renderLinux/macOS:
curl -fsSL https://raw.githubusercontent.com/render-oss/cli/main/bin/install.sh | sh
4. MCP Setup (if MCP isn't configured)
If list_services() fails because MCP isn't configured, ask whether they want to set up MCP (preferred) or continue with the CLI fallback. If they choose MCP, ask which AI tool they're using, then provide the matching instructions below. Always use their API key.
Cursor
Walk the user through these steps:
Get a Render API key:
https://dashboard.render.com/u/*/settings#api-keysAdd this to
~/.cursor/mcp.json(replace<YOUR_API_KEY>):
{
"mcpServers": {
"render": {
"url": "https://mcp.render.com/mcp",
"headers": {
"Authorization": "Bearer <YOUR_API_KEY>"
}
}
}
}Restart Cursor, then retry
list_services().
Claude Code
Walk the user through these steps:
Get a Render API key:
https://dashboard.render.com/u/*/settings#api-keysAdd the MCP server with Claude Code (replace
<YOUR_API_KEY>):
claude mcp add --transport http render https://mcp.render.com/mcp --header "Authorization: Bearer <YOUR_API_KEY>"Restart Claude Code, then retry
list_services().
Codex
Walk the user through these steps:
Get a Render API key:
https://dashboard.render.com/u/*/settings#api-keysSet it in their shell:
export RENDER_API_KEY="<YOUR_API_KEY>"Add the MCP server with the Codex CLI:
codex mcp add render --url https://mcp.render.com/mcp --bearer-token-env-var RENDER_API_KEYRestart Codex, then retry
list_services().
Other Tools
If the user is on another AI app, direct them to the Render MCP docs for that tool's setup steps and install method.
Workspace Selection
After MCP is configured, have the user set the active Render workspace with a prompt like:
Set my Render workspace to [WORKSPACE_NAME]5. Check Authentication (CLI fallback only)
If MCP isn't available, use the CLI instead and verify you can access your account:
# Check if user is logged in (use -o json for non-interactive mode)
render whoami -o jsonIf render whoami fails or returns empty data, the CLI is not authenticated. The CLI won't always prompt automatically, so explicitly prompt the user to authenticate:
If neither is configured, ask user which method they prefer:
API Key (CLI):
export RENDER_API_KEY="rnd_xxxxx"(Get from https://dashboard.render.com/u/*/settings#api-keys)Login:
render login(Opens browser for OAuth)
6. Check Workspace Context
Verify the active workspace:
get_selected_workspace()Or via CLI:
render workspace current -o jsonTo list available workspaces:
list_workspaces()If user needs to switch workspaces, they must do so via Dashboard or CLI (render workspace set).
Once prerequisites are met, proceed with deployment workflow.
Method 1: Blueprint Deployment (Recommended for Complex Apps)
Blueprint Workflow
Step 1: Analyze Codebase
Analyze the codebase to determine framework/runtime, build and start commands, required env vars, datastores, and port binding. Use the detailed checklists in references/codebase-analysis.md.
Step 2: Generate render.yaml
Create a render.yaml Blueprint file following the Blueprint specification.
Complete specification: references/blueprint-spec.md
Key Points:
Always use
plan: freeunless user specifies otherwiseInclude ALL environment variables the app needs
Mark secrets with
sync: false(user fills these in Dashboard)Use appropriate service type:
web,worker,cron,static, orpservUse appropriate runtime: references/runtimes.md
Basic Structure:
services:
- type: web
name: my-app
runtime: node
plan: free
buildCommand: npm ci
startCommand: npm start
envVars:
- key: DATABASE_URL
fromDatabase:
name: postgres
property: connectionString
- key: JWT_SECRET
sync: false # User fills in Dashboard
databases:
- name: postgres
databaseName: myapp_db
plan: freeService Types:
web: HTTP services, APIs, web applications (publicly accessible)worker: Background job processors (not publicly accessible)cron: Scheduled tasks that run on a cron schedulestatic: Static sites (HTML/CSS/JS served via CDN)pserv: Private services (internal only, within same account)
Service type details: references/service-types.md Runtime options: references/runtimes.md Template examples: assets/
Step 2.5: Immediate Next Steps (Always Provide)
After creating render.yaml, always give the user a short, explicit checklist and run validation immediately when the CLI is available:
Authenticate (CLI): run
render whoami -o json(if not logged in, runrender loginor setRENDER_API_KEY)Validate (recommended): run
render blueprints validateIf the CLI isn't installed, offer to install it and provide the command.
Commit + push:
git add render.yaml && git commit -m "Add Render deployment configuration" && git push origin mainOpen Dashboard: Use the Blueprint deeplink and complete Git OAuth if prompted
Fill secrets: Set env vars marked
sync: falseDeploy: Click "Apply" and monitor the deploy
Step 3: Validate Configuration
Validate the render.yaml file to catch errors before deployment. If the CLI is installed, run the commands directly; only prompt the user if the CLI is missing:
render whoami -o json # Ensure CLI is authenticated (won't always prompt)
render blueprints validateFix any validation errors before proceeding. Common issues:
Missing required fields (
name,type,runtime)Invalid runtime values
Incorrect YAML syntax
Invalid environment variable references
Configuration guide: references/configuration-guide.md
Step 4: Commit and Push
IMPORTANT: You must merge the render.yaml file into your repository before deploying.
Ensure the render.yaml file is committed and pushed to your Git remote:
git add render.yaml
git commit -m "Add Render deployment configuration"
git push origin mainIf there is no Git remote yet, stop here and guide the user to create a GitHub/GitLab/Bitbucket repo, add it as origin, and push before continuing.
Why this matters: The Dashboard deeplink will read the render.yaml from your repository. If the file isn't merged and pushed, Render won't find the configuration and deployment will fail.
Verify the file is in your remote repository before proceeding to the next step.
Step 5: Generate Deeplink
Get the Git repository URL:
git remote get-url originThis will return a URL from your Git provider. If the URL is SSH format, convert it to HTTPS:
| SSH Format | HTTPS Format |
|---|---|
[email protected]:user/repo.git | https://github.com/user/repo |
[email protected]:user/repo.git | https://gitlab.com/user/repo |
[email protected]:user/repo.git | https://bitbucket.org/user/repo |
Conversion pattern: Replace git@<host>: with https://<host>/ and remove .git suffix.
Format the Dashboard deeplink using the HTTPS repository URL:
https://dashboard.render.com/blueprint/new?repo=<REPOSITORY_URL>Example:
https://dashboard.render.com/blueprint/new?repo=https://github.com/username/repo-nameStep 6: Guide User
CRITICAL: Ensure the user has merged and pushed the render.yaml file to their repository before clicking the deeplink. If the file isn't in the repository, Render cannot read the Blueprint configuration and deployment will fail.
Provide the deeplink to the user with these instructions:
Verify render.yaml is merged - Confirm the file exists in your repository on GitHub/GitLab/Bitbucket
Click the deeplink to open Render Dashboard
Complete Git provider OAuth if prompted
Name the Blueprint (or use default from render.yaml)
Fill in secret environment variables (marked with
sync: false)Review services and databases configuration
Click "Apply" to deploy
The deployment will begin automatically. Users can monitor progress in the Render Dashboard.
Step 7: Verify Deployment
After the user deploys via Dashboard, verify everything is working.
Check deployment status via MCP:
list_deploys(serviceId: "<service-id>", limit: 1)Look for status: "live" to confirm successful deployment.
Check for runtime errors (wait 2-3 minutes after deploy):
list_logs(resource: ["<service-id>"], level: ["error"], limit: 20)Check service health metrics:
get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_request_count", "cpu_usage", "memory_usage"]
)If errors are found, proceed to the Post-deploy verification and basic triage section below.
Method 2: Direct Service Creation (Quick Single-Service Deployments)
For simple deployments without Infrastructure-as-Code, create services directly via MCP tools.
When to Use Direct Creation
Single web service or static site
Quick prototypes or demos
When you don't need a render.yaml file in your repo
Adding databases or cron jobs to existing projects
Prerequisites for Direct Creation
Repository must be pushed to a Git provider. Render clones your repository to build and deploy services.
git remote -v # Verify remote exists
git push origin main # Ensure code is pushedSupported providers: GitHub, GitLab, Bitbucket
If no remote exists, stop and ask the user to create/push a remote or switch to Docker image deploy.
Note: MCP does not support creating image-backed services. Use the Dashboard/API for prebuilt Docker image deploys.
Direct Creation Workflow
Use the concise steps below, and refer to references/direct-creation.md for full MCP command examples and follow-on configuration.
Step 1: Analyze Codebase
Use references/codebase-analysis.md to determine runtime, build/start commands, env vars, and datastores.
Step 2: Create Resources via MCP
Create the service (web or static) and any required databases or key-value stores. See references/direct-creation.md.
If MCP returns an error about missing Git credentials or repo access, stop and guide the user to connect their Git provider in the Render Dashboard, then retry.
Step 3: Configure Environment Variables
Add required env vars via MCP after creation. See references/direct-creation.md.
Remind the user that secrets can be set in the Dashboard if they prefer not to pass them via MCP.
Step 4: Verify Deployment
Check deploy status, logs, and metrics. See references/direct-creation.md.
For service discovery, configuration details, quick commands, and common issues, see references/deployment-details.md.
Post-deploy verification and basic triage (All Methods)
Keep this short and repeatable. If any check fails, fix it before redeploying.
Confirm the latest deploy is
liveand serving trafficHit the health endpoint (or root) and verify a 200 response
Scan recent error logs for a clear failure signature
Verify required env vars and port binding (
0.0.0.0:$PORT)
Detailed checklist and commands: references/post-deploy-checks.md
If the service fails to start or health checks time out, use the basic triage guide: references/troubleshooting-basics.md
Troubleshooting
Escalated Network Access
If deployment fails due to network issues (timeouts, DNS errors, connection resets), rerun the deploy with escalated permissions (use sandbox_permissions=require_escalated). The deploy requires escalated network access when sandbox networking blocks outbound requests.
Example guidance to the user:
The deploy needs escalated network access to deploy to Render. I can rerun the command with escalated permissions—want me to proceed?Optional: If you need deeper diagnostics (metrics/DB checks/error catalog), suggest installing the
render-debug skill. It is not required for the core deploy flow.
Files
- ATTRIBUTION.mdAdded by AutoGineer878 B
- LICENSE.txt10.5 KiB
- SKILL.md17.2 KiB
- agents/openai.yaml480 B
- assets/docker.yaml1.4 KiB
- assets/go-api.yaml848 B
- assets/nextjs-postgres.yaml870 B
- assets/node-express.yaml619 B
- assets/python-django.yaml2.5 KiB
- assets/render-small.svg553 B
- assets/render.png911 B
- assets/static-site.yaml1.3 KiB
- references/blueprint-spec.md15.6 KiB
- references/codebase-analysis.md2.2 KiB
- references/configuration-guide.md11.5 KiB
- references/deployment-details.md5.2 KiB
- references/direct-creation.md2.7 KiB
- references/error-patterns.md833 B
- references/post-deploy-checks.md1.0 KiB
- references/runtimes.md9.6 KiB
- references/service-types.md11.1 KiB
- references/troubleshooting-basics.md1.3 KiB
references/configuration-guide.md
Render Configuration Guide
Common configuration patterns, best practices, and troubleshooting for Render deployments.
Environment Variables
Required vs Optional Variables
Always declare ALL environment variables in render.yaml, even if values are provided by user later.
Three categories:
Configuration values (hardcoded):
envVars:
- key: NODE_ENV
value: production
- key: LOG_LEVEL
value: info
- key: API_URL
value: https://api.example.comSecrets (user provides):
envVars:
- key: JWT_SECRET
sync: false
- key: STRIPE_SECRET_KEY
sync: false
- key: API_KEY
sync: falseAuto-generated (Render provides):
envVars:
- key: SESSION_SECRET
generateValue: true
- key: ENCRYPTION_KEY
generateValue: trueDatabase Connection Patterns
PostgreSQL:
envVars:
- key: DATABASE_URL
fromDatabase:
name: postgres
property: connectionStringRedis:
envVars:
- key: REDIS_URL
fromDatabase:
name: redis
property: connectionStringMultiple databases:
envVars:
- key: PRIMARY_DB_URL
fromDatabase:
name: postgres-primary
property: connectionString
- key: ANALYTICS_DB_URL
fromDatabase:
name: postgres-analytics
property: connectionString
- key: CACHE_URL
fromDatabase:
name: redis
property: connectionStringCross-Service References
Reference other services in your account:
services:
- type: web
name: frontend
runtime: node
envVars:
- key: API_URL
fromService:
name: backend-api
type: web
property: host # or hostport, port
- type: web
name: backend-api
runtime: nodeAvailable properties:
host: Service hostnameport: Service porthostport: Combinedhost:port
Environment Variable Groups
Share common configuration across services:
envVarGroups:
- name: common-config
envVars:
- key: NODE_ENV
value: production
- key: LOG_LEVEL
value: info
- key: TZ
value: UTC
services:
- type: web
name: web-app
runtime: node
envVars:
- fromGroup: common-config
- key: PORT
value: 10000
- type: worker
name: worker
runtime: node
envVars:
- fromGroup: common-configPort Binding
The Port Binding Requirement
CRITICAL: Web services must bind to 0.0.0.0:$PORT
Why this matters:
Render sets
PORTenvironment variable (default: 10000)Services must bind to
0.0.0.0(notlocalhostor127.0.0.1)Health checks fail if port binding is incorrect
Deployment will fail or service won't receive traffic
Code Examples by Language
Node.js / Express:
const express = require('express');
const app = express();
const PORT = process.env.PORT || 3000;
app.listen(PORT, '0.0.0.0', () => {
console.log(`Server running on port ${PORT}`);
});Python / Flask:
import os
from flask import Flask
app = Flask(__name__)
if __name__ == '__main__':
port = int(os.environ.get('PORT', 5000))
app.run(host='0.0.0.0', port=port)Python / Django:
In settings.py:
# Django runs on port specified by environment
ALLOWED_HOSTS = ['*']Start command in render.yaml:
startCommand: gunicorn config.wsgi:application --bind 0.0.0.0:$PORTPython / FastAPI:
import os
import uvicorn
from fastapi import FastAPI
app = FastAPI()
if __name__ == "__main__":
port = int(os.environ.get("PORT", 8000))
uvicorn.run(app, host="0.0.0.0", port=port)Start command:
startCommand: uvicorn main:app --host 0.0.0.0 --port $PORTGo:
package main
import (
"fmt"
"net/http"
"os"
)
func main() {
port := os.Getenv("PORT")
if port == "" {
port = "3000"
}
http.HandleFunc("/", handler)
fmt.Printf("Server starting on port %s\n", port)
http.ListenAndServe(":"+port, nil)
}Ruby / Rails:
In config/puma.rb:
port ENV.fetch("PORT") { 3000 }
bind "tcp://0.0.0.0:#{ENV.fetch('PORT', 3000)}"Rust / Actix:
use actix_web::{App, HttpServer};
use std::env;
#[actix_web::main]
async fn main() -> std::io::Result<()> {
let port = env::var("PORT").unwrap_or_else(|_| "8080".to_string());
let addr = format!("0.0.0.0:{}", port);
HttpServer::new(|| App::new())
.bind(&addr)?
.run()
.await
}Build Commands
Non-Interactive Flags
Always use non-interactive flags to prevent builds from hanging waiting for input.
npm (Node.js):
buildCommand: npm ci
# NOT: npm installpip (Python):
buildCommand: pip install -r requirements.txt
# Already non-interactiveapt (System packages):
buildCommand: apt-get update && apt-get install -y libpq-dev
# Use -y flag to auto-confirmbundler (Ruby):
buildCommand: bundle install --jobs=4 --retry=3Build with Additional Steps
Node.js with build step:
buildCommand: npm ci && npm run buildPython Django with static files:
buildCommand: pip install -r requirements.txt && python manage.py collectstatic --no-inputRuby Rails with assets:
buildCommand: bundle install && bundle exec rails assets:precompileBuild Timeouts
Free tier: 15 minutes Paid tiers: Configurable
If builds timeout:
Optimize dependencies (remove unused packages)
Use build caching
Consider pre-building in CI/CD
Upgrade to paid tier for longer timeouts
Database Connections
Internal vs External URLs
Use internal URLs for better performance:
When using fromDatabase, Render automatically provides internal .render-internal.com URLs:
envVars:
- key: DATABASE_URL
fromDatabase:
name: postgres
property: connectionStringThis provides: postgresql://user:[email protected]:5432/db
Benefits:
Lower latency (same data center)
No external bandwidth charges
Automatic internal DNS
Connection Pooling
Node.js / PostgreSQL:
const { Pool } = require('pg');
const pool = new Pool({
connectionString: process.env.DATABASE_URL,
ssl: process.env.NODE_ENV === 'production' ? { rejectUnauthorized: false } : false,
max: 20, // Maximum pool size
idleTimeoutMillis: 30000,
connectionTimeoutMillis: 2000,
});Python / PostgreSQL:
import psycopg2.pool
pool = psycopg2.pool.SimpleConnectionPool(
minconn=1,
maxconn=20,
dsn=os.environ['DATABASE_URL']
)Django Settings:
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'URL': os.environ['DATABASE_URL'],
'CONN_MAX_AGE': 600, # Connection pooling
}
}Database Migrations
Run migrations during build:
Django:
buildCommand: pip install -r requirements.txt && python manage.py migrateRails:
buildCommand: bundle install && bundle exec rails db:migrateNode.js / Prisma:
buildCommand: npm ci && npx prisma migrate deployFree Tier Limitations
What's Included
Free tier provides:
1 web service
1 PostgreSQL database (1 GB storage, 97 MB RAM)
750 hours/month compute
512 MB RAM per service
0.5 CPU per service
100 GB bandwidth/month
Resource Limits
Memory (512 MB):
Monitor memory usage in logs
Optimize for memory-constrained environments
Use lightweight dependencies
CPU (0.5 cores):
Suitable for low-traffic applications
Consider upgrading for higher traffic
Spin Down (Free services):
Services spin down after 15 minutes of inactivity
First request after spin down takes ~30 seconds (cold start)
Upgrade to paid tier for always-on services
When to Upgrade
Upgrade to paid plan when:
Need more than 1 web service
Need always-on services (no spin down)
Traffic exceeds free tier limits
Need more memory/CPU
Need faster build times
Need preview environments
Health Checks
Adding Health Check Endpoints
Node.js / Express:
app.get('/health', (req, res) => {
res.status(200).json({
status: 'ok',
timestamp: new Date().toISOString()
});
});Python / Flask:
@app.route('/health')
def health():
return {'status': 'ok'}, 200Python / FastAPI:
@app.get("/health")
async def health():
return {"status": "ok"}Go:
http.HandleFunc("/health", func(w http.ResponseWriter, r *http.Request) {
w.WriteHeader(http.StatusOK)
w.Write([]byte(`{"status":"ok"}`))
})Configure in render.yaml
services:
- type: web
name: my-app
runtime: node
healthCheckPath: /healthBenefits:
Faster deployment detection
Better monitoring
Automatic restart on health check failures
Common Deployment Issues
Issue 1: Missing Environment Variables
Symptom: Service crashes with "undefined variable" errors
Solution: Add all required env vars to render.yaml:
envVars:
- key: DATABASE_URL
fromDatabase:
name: postgres
property: connectionString
- key: JWT_SECRET
sync: false # User fills in DashboardIssue 2: Port Binding Errors
Symptom: EADDRINUSE or health check timeout errors
Solution: Ensure app binds to 0.0.0.0:$PORT:
const PORT = process.env.PORT || 3000;
app.listen(PORT, '0.0.0.0');Issue 3: Build Hangs
Symptom: Build times out after 15 minutes
Solution: Use non-interactive build commands:
buildCommand: npm ci # NOT npm installIssue 4: Database Connection Fails
Symptom: ECONNREFUSED on port 5432
Solutions:
Use
fromDatabasefor automatic internal URLsEnable SSL for external connections
Check
ipAllowListsettings
Issue 5: Static Site 404s
Symptom: Client-side routes return 404
Solution: Add SPA rewrite rules:
routes:
- type: rewrite
source: /*
destination: /index.htmlIssue 6: Out of Memory (OOM)
Symptom: Service crashes with JavaScript heap out of memory
Solutions:
Optimize application memory usage
Reduce dependency size
Upgrade to higher plan with more RAM
Best Practices Checklist
Environment Variables:
[ ] All env vars declared in render.yaml
[ ] Secrets marked with
sync: false[ ] Database URLs use
fromDatabasereferences
Port Binding:
[ ] App binds to
process.env.PORT[ ] Bind to
0.0.0.0(notlocalhost)
Build Commands:
[ ] Use non-interactive flags (
npm ci,-y, etc.)[ ] Build completes under 15 minutes (free tier)
Start Commands:
[ ] Command starts HTTP server correctly
[ ] Server binds to correct port
Health Checks:
[ ]
/healthendpoint implemented[ ] Returns 200 status code
Database:
[ ] Connection pooling configured
[ ] Using internal URLs (
.render-internal.com)[ ] SSL enabled if needed
Plans:
[ ] Using
plan: freeby default[ ] Documented upgrade path for users
Git Repository:
[ ] render.yaml committed to repository
[ ] Pushed to git remote (GitHub/GitLab/Bitbucket)
[ ] Branch specified in render.yaml (if not main)
Additional Resources
Blueprint Specification: blueprint-spec.md
Service Types: service-types.md
Runtimes: runtimes.md
Official Render Docs: https://render.com/docs
Requirements and compatibility
Written for: OpenAI Codex
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, Needs network access, Needs an API key, Needs a command-line tool, Needs Node.js
Security
What our scanner noticed· Medium risk
- Instructions that use the network
- Downloads piped into a shell
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
- Published by
- OpenAI
- Publisher
- OpenAI (Official)
- Source
- openai/skills · skills/.curated/render-deploy · commit 49f948f
- Licence
- Apache License 2.0 (redistribution allowed)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by OpenAI or OpenAI.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download render-deploy.zip