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Written by AutoGineer from the skill’s own files, not by the author
The render-debug skill provides a structured approach to troubleshooting Render services. It guides users through identifying failed services, retrieving relevant logs, analyzing performance metrics, and querying database state to pinpoint root causes like configuration errors, resource exhaustion, or connection issues. The skill integrates with Render's MCP tools to automate data collection and suggests common fixes for identified error patterns.
What it does
- Lists and inspects status of Render services and deployments
- Retrieves and filters build, runtime, and HTTP error logs
- Analyzes CPU, memory, and latency metrics to identify performance bottlenecks
- Queries PostgreSQL instances to diagnose connection and query performance issues
- Maps common error patterns to actionable remediation steps
- Provides workflows for verifying fixes through log and metric validation
When to use it
- When a Render deployment fails to complete
- When a service crashes or fails to start
- When application health checks are timing out
- When users report production errors or performance degradation
- When diagnosing database connection or query latency problems
Advantages
- Standardizes the debugging process for Render environments
- Reduces manual log searching by targeting specific error types
- Provides direct access to database diagnostics via SQL queries
- Offers clear remediation paths for common configuration and resource issues
Usage examples
- “My service is failing to deploy on Render, can you help?”
- The agent will list your services, identify the failed one, retrieve the relevant build logs, and analyze them against known error patterns to suggest a fix.
- “My app is running very slowly, check the metrics.”
- The agent will fetch CPU, memory, and HTTP latency metrics for the service to determine if resource constraints or slow database queries are the cause.
- “I am getting ECONNREFUSED errors from my database.”
- The agent will check the database status, analyze active connection counts, and query pg_stat_activity to identify potential connection pool exhaustion.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/render-oss/render-debug/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip render-debug.zip -d .agents/skills
Documentation by Render
Debug Render Deployments
Analyze deployment failures using logs, metrics, and database queries. Identify root causes and apply fixes.
When to Use This Skill
Activate this skill when:
Deployment fails on Render
Service won't start or keeps crashing
User mentions errors, logs, or debugging
Health checks are timing out
Application errors in production
Performance issues (slow responses)
Database connection problems
Render Access
Before reading logs, metrics, or database state, follow references/render-access.md to establish access and verify the intended workspace. Prefer MCP when available; the CLI can provide logs and deploy status, while metrics and structured database queries require MCP.
Debugging Workflow
When the failure involves configuration or secrets, read references/environment-variables.md before inspecting or changing service variables.
When the failure involves deploy state, rollout, health checks, restart, rollback, or shutdown, read references/deployments.md before diagnosing it.
Step 1: Identify Failed Service
list_services()If MCP isn't configured, ask whether to set it up (preferred) or continue with CLI. Then proceed.
Look for services with failed status. Get details:
get_service(serviceId: "<id>")Step 2: Retrieve Logs
Build/Deploy Logs (most failures):
list_logs(resource: ["<service-id>"], type: ["build"], limit: 200)Runtime Error Logs:
list_logs(resource: ["<service-id>"], level: ["error"], limit: 100)Search for Specific Errors:
list_logs(resource: ["<service-id>"], text: ["KeyError", "ECONNREFUSED"], limit: 50)HTTP Error Logs:
list_logs(resource: ["<service-id>"], statusCode: ["500", "502", "503"], limit: 50)Step 3: Analyze Error Patterns
Match log errors against known patterns:
| Error | Log Pattern | Common Fix |
|---|---|---|
| MISSING_ENV_VAR | KeyError, not defined | Add to render.yaml or update_environment_variables |
| PORT_BINDING | EADDRINUSE | Use 0.0.0.0:$PORT |
| MISSING_DEPENDENCY | Cannot find module | Add to package.json/requirements.txt |
| DATABASE_CONNECTION | ECONNREFUSED :5432 | Check DATABASE_URL, DB status |
| HEALTH_CHECK | Health check timeout | Add /health endpoint, check port binding |
| OUT_OF_MEMORY | heap out of memory, exit 137 | Optimize memory or upgrade plan |
| BUILD_FAILURE | Command failed | Fix build command or dependencies |
Full error catalog: references/error-patterns.md
If errors repeat across deploys: Switch from incremental fixes to a broader sweep. Scan the codebase/config for all likely causes in that error class (related env vars, build config, dependencies, or type errors) and address them together before the next redeploy.
Step 4: Check Metrics (Performance Issues)
For crashes, slow responses, or resource issues:
get_metrics(
resourceId: "<service-id>",
metricTypes: ["cpu_usage", "memory_usage", "memory_limit"]
)get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_latency"],
httpLatencyQuantile: 0.95
)Detailed metrics guide: references/metrics-debugging.md
Step 5: Debug Database Issues
For database-related errors:
# Check database status
list_postgres_instances()
# Check connections
get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"])
# Query directly
query_render_postgres(
postgresId: "<postgres-id>",
sql: "SELECT state, count(*) FROM pg_stat_activity GROUP BY state"
)Detailed database guide: references/database-debugging.md
Step 6: Apply Fix
For environment variables:
update_environment_variables(
serviceId: "<service-id>",
envVars: [{"key": "MISSING_VAR", "value": "value"}]
)For code changes:
Edit the source file
Commit and push
Deploy triggers automatically (if auto-deploy enabled)
Step 7: Verify Fix
# Check deploy status
list_deploys(serviceId: "<service-id>", limit: 1)
# Check for new errors
list_logs(resource: ["<service-id>"], level: ["error"], limit: 20)
# Check metrics
get_metrics(resourceId: "<service-id>", metricTypes: ["http_request_count"])Quick Workflows
Pre-built debugging sequences for common scenarios:
| Scenario | Workflow |
|---|---|
| Deploy failed | list_deploys → list_logs(type: build) → fix → redeploy |
| App crashing | list_logs(level: error) → get_metrics(memory) → fix |
| App slow | get_metrics(http_latency) → get_metrics(cpu) → query_postgres |
| DB connection | list_postgres → get_metrics(connections) → query_postgres |
| Post-deploy check | list_deploys → list_logs(error) → get_metrics |
Detailed workflows: references/quick-workflows.md
Quick Reference
MCP Tools
# Service Discovery
list_services()
get_service(serviceId: "<id>")
list_postgres_instances()
# Logs
list_logs(resource: ["<id>"], level: ["error"], limit: 100)
list_logs(resource: ["<id>"], type: ["build"], limit: 200)
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)
# Database
query_render_postgres(postgresId: "<id>", sql: "SELECT ...")
# Deployments
list_deploys(serviceId: "<id>", limit: 5)
# Environment Variables
update_environment_variables(serviceId: "<id>", envVars: [{key, value}])CLI Commands (Fallback)
render services -o json
render logs -r <service-id> --level error -o json
render logs -r <service-id> --tail -o text
render deploys create <service-id> --waitReferences
Render access: references/render-access.md
Environment variables: references/environment-variables.md
Compute plans: references/compute-plans.md
Deployments: references/deployments.md
Error patterns: references/error-patterns.md
Metrics debugging: references/metrics-debugging.md
Database debugging: references/database-debugging.md
Quick workflows: references/quick-workflows.md
Log analysis: references/log-analysis.md
Troubleshooting: references/troubleshooting.md
Related Skills
render-deploy — Deploy new applications to Render
render-monitor — Ongoing service health monitoring
render-mcp — MCP server setup and capability discovery
<!-- shared:documentation-retrieval -->
Current documentation retrieval
Whenever this skill directs you to consult current Render documentation:
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.Confirm that retrieval succeeded and returned the expected document, then read its contents. Saving a file or printing its path is not sufficient.
If the request fails or your tool cannot read the Markdown response, open and read the linked HTML version instead.
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
- ATTRIBUTION.mdAdded by AutoGineer878 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md8.3 KiB
- references/compute-plans.md2.3 KiB
- references/database-debugging.md8.8 KiB
- references/deployments.md7.9 KiB
- references/environment-variables.md4.5 KiB
- references/error-patterns.md18.7 KiB
- references/log-analysis.md9.9 KiB
- references/metrics-debugging.md6.4 KiB
- references/quick-workflows.md6.2 KiB
- references/render-access.md4.7 KiB
- references/troubleshooting.md12.3 KiB
references/troubleshooting.md
Render Troubleshooting Guide
Systematic debugging procedures and fix strategies for common Render deployment issues.
General Debugging Process
The 5-Step Debugging Method
Identify - What's failing? (Build, deploy, runtime?)
Locate - Where's the error? (Logs, error messages)
Diagnose - Why is it failing? (Root cause)
Fix - Apply appropriate solution
Verify - Test the fix works
Debug Priority Checklist
When facing deployment issues, check in this order:
[ ] Environment variables (Most common)
[ ] Port binding (Second most common)
[ ] Dependencies installed correctly
[ ] Database connections configured
[ ] Build command successful
[ ] Start command correct
[ ] Health check endpoint exists
[ ] Resource limits (memory, CPU)
Issue Category 1: Environment Variables
Problem: Missing Environment Variable
Symptoms:
ReferenceError: DATABASE_URL is not definedKeyError: 'API_KEY'Service crashes immediately after start
Diagnosis Steps:
Check logs for variable name:
render logs -r <service-id> --level error -o json | grep "not defined"List current environment variables:
# In Dashboard or render.yamlIdentify which variable is missing
Fix Procedure:
Read environment-variables.md, identify the intended source of truth, and inspect existing keys without exposing values.
Add the missing key through that configuration surface, using current Blueprint wiring for resource references and an appropriate secret mechanism for sensitive values.
Commit and push if the service is Blueprint-managed; otherwise apply the authorized service configuration change.
Trigger the applicable deploy so the new value becomes active.
Verification:
render logs -r <service-id> --tail -o text
# Should see successful startupIssue Category 2: Port Binding
Problem: Port Binding Error
Symptoms:
EADDRINUSE: address already in useHealth check timeoutService starts but doesn't receive traffic
Diagnosis Steps:
Check if app hardcodes port:
# Look for hardcoded ports in code
grep -r "listen(3000" .
grep -r "port.*=" . | grep -v "process.env"Check if binding to localhost:
grep -r "localhost" .
grep -r "127.0.0.1" .Fix Procedure by Language:
Node.js / Express:
Before (WRONG):
app.listen(3000);
// or
app.listen(3000, 'localhost');After (CORRECT):
const PORT = process.env.PORT || 3000;
app.listen(PORT, '0.0.0.0', () => {
console.log(`Server running on port ${PORT}`);
});Python / Flask:
Before (WRONG):
app.run(port=5000)After (CORRECT):
import os
if __name__ == '__main__':
port = int(os.environ.get('PORT', 5000))
app.run(host='0.0.0.0', port=port)Python / Django:
Ensure gunicorn uses $PORT:
# In render.yaml
startCommand: gunicorn config.wsgi:application --bind 0.0.0.0:$PORTGo:
Before (WRONG):
http.ListenAndServe(":3000", handler)After (CORRECT):
port := os.Getenv("PORT")
if port == "" {
port = "3000"
}
http.ListenAndServe(":"+port, handler)Verification:
# Should see message like "Server running on port 10000"
render logs -r <service-id> --tail -o textIssue Category 3: Database Connection
Problem: Cannot Connect to Database
Symptoms:
ECONNREFUSED 127.0.0.1:5432could not connect to serverConnection refused
Diagnosis Steps:
Check if DATABASE_URL is set:
# Look in render.yaml
grep "DATABASE_URL" render.yamlVerify database is provisioned:
render services -o json
# Look for database serviceCheck connection code
Fix Procedure:
Step 1: Ensure DATABASE_URL in render.yaml
envVars:
- key: DATABASE_URL
fromDatabase:
name: postgres
property: connectionString
databases:
- name: postgres
databaseName: myapp_prod
plan: freeStep 2: Configure SSL if needed
Node.js / pg:
const { Pool } = require('pg');
const pool = new Pool({
connectionString: process.env.DATABASE_URL,
ssl: process.env.NODE_ENV === 'production' ? {
rejectUnauthorized: false
} : false
});Python / psycopg2:
import psycopg2
conn = psycopg2.connect(
os.environ['DATABASE_URL'],
sslmode='require'
)Step 3: Add connection pooling
Node.js:
const pool = new Pool({
connectionString: process.env.DATABASE_URL,
max: 20, # Maximum connections
idleTimeoutMillis: 30000,
connectionTimeoutMillis: 2000,
});Python:
from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool
engine = create_engine(
os.environ['DATABASE_URL'],
poolclass=QueuePool,
pool_size=10,
max_overflow=20
)Step 4: Test connection
// Add health check with DB ping
app.get('/health', async (req, res) => {
try {
await pool.query('SELECT 1');
res.status(200).json({ status: 'ok', database: 'connected' });
} catch (err) {
res.status(500).json({ status: 'error', database: 'disconnected' });
}
});Issue Category 4: Build Failures
Problem: Build Command Fails
Symptoms:
npm ERR!error: failed to compileBuild timeout
Diagnosis Steps:
Check build logs:
render logs -r <service-id> --type deploy --level error -o jsonIdentify error type:
Missing dependency?
Compilation error?
Build timeout?
Fix Procedures:
Missing Dependency:
Check package.json/requirements.txt:
{
"dependencies": {
"express": "^4.18.0" // Add missing package
}
}Build Command Wrong:
Fix in render.yaml:
# Node.js
buildCommand: npm ci && npm run build
# Python
buildCommand: pip install -r requirements.txt
# Go
buildCommand: go build -o bin/app .Build Timeout (120-minute build-command limit):
Optimize:
# Use ci instead of install (faster)
buildCommand: npm ci --prefer-offline && npm run build
# Remove unused dependencies
# Consider pre-building expensive artifacts in CI/CDIssue Category 5: Health Check Failures
Problem: Health Check Timeout
Read deployments.md for current health-check behavior, timing, and service-type support before diagnosing this failure.
Symptoms:
Health check timeoutfailed to become healthy
Diagnosis Steps:
Check if app is binding to correct port
Check whether the configured health check endpoint exists and returns an accepted response
Check startup time
Fix Procedure:
Step 1: Add health endpoint
Node.js:
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)
json.NewEncoder(w).Encode(map[string]string{"status": "ok"})
})Step 2: Configure in render.yaml
healthCheckPath: /healthStep 3: Optimize startup time
Move slow operations after server start:
// Start server first
const server = app.listen(PORT, '0.0.0.0', () => {
console.log(`Server running on port ${PORT}`);
// Then do slow operations
initializeCache();
warmupConnections();
});Issue Category 6: Memory Issues
Problem: Out of Memory
Symptoms:
JavaScript heap out of memoryKilled(exit code 137)Service crashes randomly
Diagnosis Steps:
Check logs for OOM errors
Monitor memory usage
Identify memory leaks
Fix Procedures:
Immediate Fix (Node.js):
Increase heap size (if on paid plan):
envVars:
- key: NODE_OPTIONS
value: --max-old-space-size=2048 # 2GBLong-term Fixes:
Profile memory usage:
// Add memory monitoring
setInterval(() => {
const used = process.memoryUsage();
console.log(`Memory: ${Math.round(used.heapUsed / 1024 / 1024)}MB`);
}, 60000);Optimize data processing:
// Process in chunks instead of all at once
for (let i = 0; i < data.length; i += 1000) {
const chunk = data.slice(i, i + 1000);
await processChunk(chunk);
}Fix memory leaks:
Clear intervals/timeouts
Remove event listeners
Close database connections
Clear caches periodically
Upgrade plan:
Free: 512 MB
Starter: 512 MB
Standard: 2 GB
Pro: 4 GB+
Issue Category 7: Runtime Crashes
Problem: Service Crashes After Start
Symptoms:
Service starts then exits
Process exited with code 1Uncaught exceptions
Diagnosis Steps:
Get runtime logs:
render logs -r <service-id> --level error -o jsonLook for uncaught errors:
Unhandled Promise rejections
Uncaught exceptions
Fatal errors
Fix Procedure:
Add error handlers:
Node.js:
// Catch all uncaught errors
process.on('uncaughtException', (err) => {
console.error('Uncaught Exception:', err);
process.exit(1);
});
process.on('unhandledRejection', (reason, promise) => {
console.error('Unhandled Rejection at:', promise, 'reason:', reason);
process.exit(1);
});
// Express error handler (add last)
app.use((err, req, res, next) => {
console.error(err.stack);
res.status(500).json({ error: 'Internal server error' });
});Python:
import logging
logging.basicConfig(level=logging.ERROR)
logger = logging.getLogger(__name__)
try:
# Your code
app.run()
except Exception as e:
logger.error(f"Fatal error: {e}", exc_info=True)
raiseDecision Tree: Where to Start
Deployment Failed?
├─ Build phase failed?
│ ├─ Check build logs for errors
│ ├─ Verify buildCommand is correct
│ ├─ Check dependencies installed
│ └─ Fix compilation errors
│
├─ Deploy phase failed?
│ ├─ Check start command
│ ├─ Verify port binding
│ ├─ Check environment variables
│ └─ Check health check endpoint
│
└─ Runtime failures?
├─ Check for uncaught exceptions
├─ Verify database connections
├─ Check memory usage
└─ Review application logsDebugging Checklist
Before asking for help, verify:
[ ] Required environment-variable keys exist in the intended configuration source
[ ] Secret configuration is complete without exposing values
[ ] App binds to
process.env.PORT(or equivalent)[ ] App binds to
0.0.0.0(not localhost)[ ] Build command is non-interactive
[ ] Dependencies are in package.json/requirements.txt
[ ] DATABASE_URL is set if using database
[ ] /health endpoint exists (if using health checks)
[ ] Local build works (
npm ci && npm run build)[ ] Start command is correct
Quick Fixes Reference
| Problem | Quick Fix |
|---|---|
| Missing env var | Add to render.yaml envVars |
| Port binding | Use process.env.PORT + 0.0.0.0 |
| Database connection | Add DATABASE_URL with fromDatabase |
| Missing dependency | Add to package.json/requirements.txt |
| Build timeout | Optimize build, use npm ci |
| Health check timeout | Add /health endpoint |
| OOM | Optimize memory or upgrade plan |
| Uncaught exception | Add error handlers |
Advanced Troubleshooting
Enable Debug Logging
Node.js:
envVars:
- key: DEBUG
value: "*" # Enable all debug logs
- key: NODE_ENV
value: development # More verbose logsTest Locally First
# Simulate Render environment
export PORT=10000
export DATABASE_URL="postgresql://..."
npm ci && npm run build && npm startCheck Render Status
Sometimes issues are platform-wide:
Getting Help
If stuck after trying these steps:
Gather information:
Service ID
Error messages from logs
render.yaml content
Steps to reproduce
Check documentation:
Community support:
Render Community Forum
Discord server
Next Steps
After fixing issues:
Monitor service health in Dashboard
Set up alerts for failures
Document any custom configuration
Consider implementing monitoring/observability
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
What our scanner noticed· Low risk
- Instructions that use the network
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-debug · commit b985a63
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Render.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download render-debug.zip