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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://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
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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.
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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.
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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/metrics-debugging.md
Metrics-Based Debugging
Use Render metrics to diagnose performance issues, resource constraints, and application health problems.
When to Use Metrics
Use metrics debugging when you see:
Out of memory errors (OOM, exit code 137)
Slow response times
Service crashes or restarts
Health check timeouts
Autoscaling issues
CPU Metrics
Get CPU Usage
get_metrics(
resourceId: "<service-id>",
metricTypes: ["cpu_usage", "cpu_limit"],
startTime: "<1-hour-ago-ISO8601>"
)Interpreting CPU
| CPU Usage | Status | Action |
|---|---|---|
| <70% | Healthy | No action needed |
| 70-85% | Warning | Monitor trends |
| >85% sustained | Critical | Optimize or upgrade plan |
High CPU causes:
Inefficient algorithms
Missing database indexes (N+1 queries)
Synchronous blocking operations
Large JSON parsing/serialization
CPU Fixes:
Profile code to find hot paths
Add caching for repeated computations
Optimize database queries
Use async/non-blocking operations
Upgrade to higher plan
Memory Metrics
Get Memory Usage
get_metrics(
resourceId: "<service-id>",
metricTypes: ["memory_usage", "memory_limit"],
startTime: "<1-hour-ago-ISO8601>"
)Interpreting Memory
| Memory % of Limit | Status | Action |
|---|---|---|
| <70% | Healthy | No action needed |
| 70-85% | Warning | Monitor for leaks |
| 85-95% | Danger | Investigate immediately |
| >95% | Critical | OOM imminent |
Use the resource's reported memory limit instead of inferring capacity from a legacy plan name. When the current plan specification matters, fetch it using compute-plans.md.
High memory causes:
Memory leaks (objects not garbage collected)
Large in-memory caches
Processing large files/datasets in memory
Too many concurrent connections
Memory Fixes:
Node.js:
// Increase heap size (if on paid plan)
// Set NODE_OPTIONS=--max-old-space-size=2048
// Process data in streams
const stream = fs.createReadStream('large-file.json');
stream.pipe(parser).on('data', processChunk);Python:
# Process data in chunks
for chunk in pd.read_csv('large_file.csv', chunksize=1000):
process(chunk)
# Use generators instead of lists
def process_items():
for item in large_dataset:
yield transform(item)HTTP Performance Metrics
Get Latency
get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_latency"],
httpLatencyQuantile: 0.95,
startTime: "<1-hour-ago-ISO8601>"
)Common quantiles:
0.5(p50) - Median, typical experience0.95(p95) - Most users' worst experience0.99(p99) - Tail latency, outliers
Interpreting Latency
| p95 Latency | Status | Likely Cause |
|---|---|---|
| <200ms | Excellent | - |
| 200-500ms | Good | Complex queries |
| 500ms-1s | Concerning | DB or external API |
| 1-2s | Poor | Multiple slow operations |
| >2s | Critical | Major bottleneck |
Get Request Count
get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_request_count"],
startTime: "<1-hour-ago-ISO8601>"
)Aggregate by status code to find error rates:
get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_request_count"],
aggregateHttpRequestCountsBy: "statusCode"
)Calculate error rate:
error_rate = 5xx_count / total_count * 100Target: <0.1% error rate
Filter by Endpoint
get_metrics(
resourceId: "<service-id>",
metricTypes: ["http_latency"],
httpPath: "/api/users",
httpHost: "api.example.com"
)Instance Metrics
Get Instance Count
get_metrics(
resourceId: "<service-id>",
metricTypes: ["instance_count"],
startTime: "<1-hour-ago-ISO8601>"
)Interpreting Instance Changes
| Pattern | Meaning |
|---|---|
| Increasing | Autoscaling up (traffic increase) |
| Decreasing | Autoscaling down (traffic decrease) |
| Stuck at 1 | Autoscaling may not be enabled |
| Rapid fluctuation | Possible instability |
Check Autoscaling Targets
get_metrics(
resourceId: "<service-id>",
metricTypes: ["cpu_target", "memory_target"]
)Bandwidth Metrics
Get Bandwidth Usage
get_metrics(
resourceId: "<service-id>",
metricTypes: ["bandwidth_usage"],
startTime: "<1-hour-ago-ISO8601>"
)High bandwidth causes:
Large API responses
Uncompressed responses
File downloads
Verbose logging to external services
Bandwidth fixes:
Enable gzip/brotli compression
Paginate large responses
Use CDN for static assets
Optimize image sizes
Correlation Analysis
Correlate Metrics with Deploys
# Get deploy times
list_deploys(serviceId: "<service-id>", limit: 10)
# Check metrics around deploy time
get_metrics(
resourceId: "<service-id>",
metricTypes: ["cpu_usage", "memory_usage", "http_latency"],
startTime: "<deploy-time-minus-1-hour>",
endTime: "<deploy-time-plus-1-hour>"
)Correlate with Errors
# Get error timestamps
list_logs(
resource: ["<service-id>"],
level: ["error"],
limit: 50
)
# Check metrics at error time
get_metrics(
resourceId: "<service-id>",
metricTypes: ["memory_usage", "cpu_usage"],
startTime: "<error-time-minus-10-min>",
endTime: "<error-time-plus-10-min>"
)Quick Diagnostics
OOM Diagnosis
# 1. Check if memory was at limit before crash
get_metrics(
resourceId: "<service-id>",
metricTypes: ["memory_usage", "memory_limit"],
startTime: "<crash-time-minus-30-min>"
)
# 2. Look for memory growth pattern
# Steady growth = memory leak
# Sudden spike = large request or data loadSlow Response Diagnosis
# 1. Check if CPU-bound
get_metrics(resourceId: "<id>", metricTypes: ["cpu_usage"])
# 2. Check latency percentiles
get_metrics(resourceId: "<id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.5)
get_metrics(resourceId: "<id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.99)
# 3. If p99 >> p50, you have outlier requests causing issues
# 4. Check slow endpoints
get_metrics(resourceId: "<id>", metricTypes: ["http_latency"], httpPath: "/api/slow-endpoint")Crash Loop Diagnosis
# 1. Check instance count for restarts
get_metrics(resourceId: "<id>", metricTypes: ["instance_count"])
# 2. Check memory at crash times
get_metrics(resourceId: "<id>", metricTypes: ["memory_usage"])
# 3. Check logs for crash reason
list_logs(resource: ["<id>"], level: ["error"], limit: 100)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
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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
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