firecrawl-advanced-troubleshooting
Apply FireCrawl advanced debugging techniques for hard-to-diagnose issues.Use when standard troubleshooting fails, investigating complex race conditions,or preparing evidence bundles for FireCrawl support escalation.Trigger with phrases like "firecrawl hard bug", "firecrawl mystery error","firecrawl impossible to debug", "difficult firecrawl issue", "firecrawl deep debug".
When & Why to Use This Skill
The FireCrawl Advanced Troubleshooting skill is a specialized toolkit designed for developers and SREs to resolve complex, hard-to-diagnose issues within FireCrawl environments. It automates deep-dive debugging processes, including multi-layer network isolation, race condition detection, and the generation of comprehensive evidence bundles using tools like kubectl and tcpdump to accelerate root cause analysis and support escalation.
Use Cases
- Investigating intermittent 'mystery errors' and complex race conditions that standard troubleshooting fails to capture.
- Generating automated evidence bundles containing logs, metrics, and network captures for FireCrawl support escalation.
- Performing systematic layer-by-layer testing (DNS, TLS, Auth, API) to isolate connectivity issues in distributed environments.
- Detecting memory leaks and resource exhaustion in long-running crawling processes through automated heap and timing analysis.
- Creating minimal reproduction cases to verify bug fixes or validate configuration changes in production-like settings.
| name | firecrawl-advanced-troubleshooting |
|---|---|
| description | | |
| allowed-tools | Read, Grep, Bash(kubectl:*), Bash(curl:*), Bash(tcpdump:*) |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
FireCrawl Advanced Troubleshooting
Overview
Deep debugging techniques for complex FireCrawl issues that resist standard troubleshooting.
Prerequisites
- Access to production logs and metrics
- kubectl access to clusters
- Network capture tools available
- Understanding of distributed tracing
Evidence Collection Framework
Comprehensive Debug Bundle
#!/bin/bash
# advanced-firecrawl-debug.sh
BUNDLE="firecrawl-advanced-debug-$(date +%Y%m%d-%H%M%S)"
mkdir -p "$BUNDLE"/{logs,metrics,network,config,traces}
# 1. Extended logs (1 hour window)
kubectl logs -l app=firecrawl-integration --since=1h > "$BUNDLE/logs/pods.log"
journalctl -u firecrawl-service --since "1 hour ago" > "$BUNDLE/logs/system.log"
# 2. Metrics dump
curl -s localhost:9090/api/v1/query?query=firecrawl_requests_total > "$BUNDLE/metrics/requests.json"
curl -s localhost:9090/api/v1/query?query=firecrawl_errors_total > "$BUNDLE/metrics/errors.json"
# 3. Network capture (30 seconds)
timeout 30 tcpdump -i any port 443 -w "$BUNDLE/network/capture.pcap" &
# 4. Distributed traces
curl -s localhost:16686/api/traces?service=firecrawl > "$BUNDLE/traces/jaeger.json"
# 5. Configuration state
kubectl get cm firecrawl-config -o yaml > "$BUNDLE/config/configmap.yaml"
kubectl get secret firecrawl-secrets -o yaml > "$BUNDLE/config/secrets-redacted.yaml"
tar -czf "$BUNDLE.tar.gz" "$BUNDLE"
echo "Advanced debug bundle: $BUNDLE.tar.gz"
Systematic Isolation
Layer-by-Layer Testing
// Test each layer independently
async function diagnoseFireCrawlIssue(): Promise<DiagnosisReport> {
const results: DiagnosisResult[] = [];
// Layer 1: Network connectivity
results.push(await testNetworkConnectivity());
// Layer 2: DNS resolution
results.push(await testDNSResolution('api.firecrawl.com'));
// Layer 3: TLS handshake
results.push(await testTLSHandshake('api.firecrawl.com'));
// Layer 4: Authentication
results.push(await testAuthentication());
// Layer 5: API response
results.push(await testAPIResponse());
// Layer 6: Response parsing
results.push(await testResponseParsing());
return { results, firstFailure: results.find(r => !r.success) };
}
Minimal Reproduction
// Strip down to absolute minimum
async function minimalRepro(): Promise<void> {
// 1. Fresh client, no customization
const client = new FireCrawlClient({
apiKey: process.env.FIRECRAWL_API_KEY!,
});
// 2. Simplest possible call
try {
const result = await client.ping();
console.log('Ping successful:', result);
} catch (error) {
console.error('Ping failed:', {
message: error.message,
code: error.code,
stack: error.stack,
});
}
}
Timing Analysis
class TimingAnalyzer {
private timings: Map<string, number[]> = new Map();
async measure<T>(label: string, fn: () => Promise<T>): Promise<T> {
const start = performance.now();
try {
return await fn();
} finally {
const duration = performance.now() - start;
const existing = this.timings.get(label) || [];
existing.push(duration);
this.timings.set(label, existing);
}
}
report(): TimingReport {
const report: TimingReport = {};
for (const [label, times] of this.timings) {
report[label] = {
count: times.length,
min: Math.min(...times),
max: Math.max(...times),
avg: times.reduce((a, b) => a + b, 0) / times.length,
p95: this.percentile(times, 95),
};
}
return report;
}
}
Memory and Resource Analysis
// Detect memory leaks in FireCrawl client usage
const heapUsed: number[] = [];
setInterval(() => {
const usage = process.memoryUsage();
heapUsed.push(usage.heapUsed);
// Alert on sustained growth
if (heapUsed.length > 60) { // 1 hour at 1/min
const trend = heapUsed[59] - heapUsed[0];
if (trend > 100 * 1024 * 1024) { // 100MB growth
console.warn('Potential memory leak in firecrawl integration');
}
}
}, 60000);
Race Condition Detection
// Detect concurrent access issues
class FireCrawlConcurrencyChecker {
private inProgress: Set<string> = new Set();
async execute<T>(key: string, fn: () => Promise<T>): Promise<T> {
if (this.inProgress.has(key)) {
console.warn(`Concurrent access detected for ${key}`);
}
this.inProgress.add(key);
try {
return await fn();
} finally {
this.inProgress.delete(key);
}
}
}
Support Escalation Template
## FireCrawl Support Escalation
**Severity:** P[1-4]
**Request ID:** [from error response]
**Timestamp:** [ISO 8601]
### Issue Summary
[One paragraph description]
### Steps to Reproduce
1. [Step 1]
2. [Step 2]
### Expected vs Actual
- Expected: [behavior]
- Actual: [behavior]
### Evidence Attached
- [ ] Debug bundle (firecrawl-advanced-debug-*.tar.gz)
- [ ] Minimal reproduction code
- [ ] Timing analysis
- [ ] Network capture (if relevant)
### Workarounds Attempted
1. [Workaround 1] - Result: [outcome]
2. [Workaround 2] - Result: [outcome]
Instructions
Step 1: Collect Evidence Bundle
Run the comprehensive debug script to gather all relevant data.
Step 2: Systematic Isolation
Test each layer independently to identify the failure point.
Step 3: Create Minimal Reproduction
Strip down to the simplest failing case.
Step 4: Escalate with Evidence
Use the support template with all collected evidence.
Output
- Comprehensive debug bundle collected
- Failure layer identified
- Minimal reproduction created
- Support escalation submitted
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Can't reproduce | Race condition | Add timing analysis |
| Intermittent failure | Timing-dependent | Increase sample size |
| No useful logs | Missing instrumentation | Add debug logging |
| Memory growth | Resource leak | Use heap profiling |
Examples
Quick Layer Test
# Test each layer in sequence
curl -v https://api.firecrawl.com/health 2>&1 | grep -E "(Connected|TLS|HTTP)"
Resources
Next Steps
For load testing, see firecrawl-load-scale.