exa-advanced-troubleshooting

Apply advanced debugging techniques for hard-to-diagnose Exa issues. Use when standard troubleshooting fails, investigating latency spikes, or preparing evidence bundles for Exa support escalation. Trigger with phrases like "exa hard bug", "exa mystery error", "exa deep debug", "difficult exa issue", "exa latency spike".

25 stars

Best use case

exa-advanced-troubleshooting is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Apply advanced debugging techniques for hard-to-diagnose Exa issues. Use when standard troubleshooting fails, investigating latency spikes, or preparing evidence bundles for Exa support escalation. Trigger with phrases like "exa hard bug", "exa mystery error", "exa deep debug", "difficult exa issue", "exa latency spike".

Teams using exa-advanced-troubleshooting should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/exa-advanced-troubleshooting/SKILL.md --create-dirs "https://raw.githubusercontent.com/ComeOnOliver/skillshub/main/skills/jeremylongshore/claude-code-plugins-plus-skills/exa-advanced-troubleshooting/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/exa-advanced-troubleshooting/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How exa-advanced-troubleshooting Compares

Feature / Agentexa-advanced-troubleshootingStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Apply advanced debugging techniques for hard-to-diagnose Exa issues. Use when standard troubleshooting fails, investigating latency spikes, or preparing evidence bundles for Exa support escalation. Trigger with phrases like "exa hard bug", "exa mystery error", "exa deep debug", "difficult exa issue", "exa latency spike".

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Exa Advanced Troubleshooting

## Overview
Deep debugging for complex Exa issues: latency spikes, intermittent failures, result quality degradation, and content retrieval failures. All Exa error responses include a `requestId` — always capture it.

## Instructions

### Step 1: Layer-by-Layer Diagnostics
```typescript
import Exa from "exa-js";

interface DiagnosticResult {
  layer: string;
  success: boolean;
  latencyMs: number;
  details: string;
}

async function diagnoseExa(): Promise<DiagnosticResult[]> {
  const results: DiagnosticResult[] = [];
  const exa = new Exa(process.env.EXA_API_KEY);

  // Layer 1: DNS + Network
  let start = performance.now();
  try {
    const resp = await fetch("https://api.exa.ai", { method: "HEAD" });
    results.push({
      layer: "network",
      success: true,
      latencyMs: performance.now() - start,
      details: `HTTP ${resp.status}`,
    });
  } catch (err: any) {
    results.push({
      layer: "network",
      success: false,
      latencyMs: performance.now() - start,
      details: err.message,
    });
    return results; // No point continuing if network fails
  }

  // Layer 2: Authentication
  start = performance.now();
  try {
    await exa.search("auth test", { numResults: 1 });
    results.push({
      layer: "auth",
      success: true,
      latencyMs: performance.now() - start,
      details: "API key valid",
    });
  } catch (err: any) {
    results.push({
      layer: "auth",
      success: false,
      latencyMs: performance.now() - start,
      details: `${err.status}: ${err.message}`,
    });
    if (err.status === 401 || err.status === 402) return results;
  }

  // Layer 3: Neural search
  start = performance.now();
  try {
    const r = await exa.search("test neural search quality", {
      type: "neural",
      numResults: 3,
    });
    results.push({
      layer: "neural-search",
      success: true,
      latencyMs: performance.now() - start,
      details: `${r.results.length} results, top score: ${r.results[0]?.score.toFixed(3)}`,
    });
  } catch (err: any) {
    results.push({
      layer: "neural-search",
      success: false,
      latencyMs: performance.now() - start,
      details: `${err.status}: ${err.message}`,
    });
  }

  // Layer 4: Content retrieval
  start = performance.now();
  try {
    const r = await exa.searchAndContents("content retrieval test", {
      numResults: 1,
      text: { maxCharacters: 500 },
      highlights: { maxCharacters: 200 },
    });
    const hasText = !!r.results[0]?.text;
    const hasHighlights = !!r.results[0]?.highlights?.length;
    results.push({
      layer: "content-retrieval",
      success: hasText,
      latencyMs: performance.now() - start,
      details: `text: ${hasText}, highlights: ${hasHighlights}`,
    });
  } catch (err: any) {
    results.push({
      layer: "content-retrieval",
      success: false,
      latencyMs: performance.now() - start,
      details: `${err.status}: ${err.message}`,
    });
  }

  // Layer 5: findSimilar
  start = performance.now();
  try {
    const r = await exa.findSimilar("https://nodejs.org", { numResults: 2 });
    results.push({
      layer: "find-similar",
      success: r.results.length > 0,
      latencyMs: performance.now() - start,
      details: `${r.results.length} similar pages found`,
    });
  } catch (err: any) {
    results.push({
      layer: "find-similar",
      success: false,
      latencyMs: performance.now() - start,
      details: `${err.status}: ${err.message}`,
    });
  }

  return results;
}

// Print diagnostic report
const results = await diagnoseExa();
console.log("=== Exa Diagnostic Report ===");
for (const r of results) {
  const icon = r.success ? "PASS" : "FAIL";
  console.log(`[${icon}] ${r.layer}: ${r.latencyMs.toFixed(0)}ms — ${r.details}`);
}
```

### Step 2: Latency Profiling
```typescript
async function profileLatency(query: string, iterations = 5) {
  const exa = new Exa(process.env.EXA_API_KEY);
  const timings: { type: string; ms: number }[] = [];

  for (const type of ["instant", "fast", "auto", "neural"] as const) {
    for (let i = 0; i < iterations; i++) {
      const start = performance.now();
      try {
        await exa.search(query, { type, numResults: 3 });
        timings.push({ type, ms: performance.now() - start });
      } catch {
        timings.push({ type, ms: -1 }); // -1 indicates failure
      }
    }
  }

  // Summarize
  const grouped = new Map<string, number[]>();
  for (const t of timings) {
    if (!grouped.has(t.type)) grouped.set(t.type, []);
    if (t.ms > 0) grouped.get(t.type)!.push(t.ms);
  }

  console.log(`\nLatency profile for: "${query}"`);
  for (const [type, times] of grouped) {
    const sorted = times.sort((a, b) => a - b);
    const p50 = sorted[Math.floor(sorted.length * 0.5)];
    const p95 = sorted[Math.floor(sorted.length * 0.95)];
    console.log(`  ${type}: p50=${p50?.toFixed(0)}ms, p95=${p95?.toFixed(0)}ms`);
  }
}
```

### Step 3: Content Retrieval Debugging
```typescript
// When getContents or searchAndContents returns empty text
async function debugContentRetrieval(url: string) {
  const exa = new Exa(process.env.EXA_API_KEY);
  const configs = [
    { name: "default", opts: { text: true } },
    { name: "livecrawl-preferred", opts: { text: true, livecrawl: "preferred" as const, livecrawlTimeout: 15000 } },
    { name: "livecrawl-always", opts: { text: true, livecrawl: "always" as const, livecrawlTimeout: 15000 } },
    { name: "highlights-only", opts: { highlights: { maxCharacters: 500 } } },
    { name: "summary-only", opts: { summary: true } },
  ];

  console.log(`\nContent retrieval debug for: ${url}`);
  for (const { name, opts } of configs) {
    try {
      const result = await exa.getContents([url], opts as any);
      const r = result.results[0];
      console.log(`  ${name}: text=${r?.text?.length || 0} chars, highlights=${r?.highlights?.length || 0}`);
    } catch (err: any) {
      console.log(`  ${name}: ERROR ${err.status} — ${err.message}`);
    }
  }
}
```

### Step 4: Support Escalation Template
```markdown
## Exa Support Escalation

**Severity:** P[1-4]
**RequestId:** [from error response]
**Timestamp:** [ISO 8601 from error]
**SDK:** exa-js [version from npm list]

### Issue Summary
[One paragraph description]

### Steps to Reproduce
1. Initialize Exa client
2. Call [method] with [parameters]
3. Observe [error/unexpected behavior]

### Expected vs Actual
- Expected: [behavior]
- Actual: [behavior]

### Diagnostic Results
[Output from diagnoseExa() function]

### Evidence
- Latency profile attached
- Content retrieval debug output
- Error response with requestId
```

## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| Intermittent 5xx | Exa transient failure | Retry with backoff, capture requestId |
| Neural search slow | Complex/long query | Switch to `fast`, shorten query |
| Empty text for valid URL | Site blocks crawling | Try `livecrawl: "always"`, use highlights |
| Score drops across queries | Query drift | Compare with baseline queries |
| findSimilar returns nothing | Seed URL not indexed | Try a more popular seed URL |

## Resources
- [Exa Error Codes](https://docs.exa.ai/reference/error-codes)
- [Exa Support](mailto:hello@exa.ai)
- [Exa Rate Limits](https://docs.exa.ai/reference/rate-limits)

## Next Steps
For load testing, see `exa-load-scale`.

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