Exa Observability

VerifiedSafe

Set up monitoring, metrics, and alerting for Exa search integrations.

Sby Skills Guide Bot
DevOpsIntermediate
7/25/2026
Claude CodeCodex
#exa#observability#metrics#alerting#typescript

Recommended for

Automated summary

This skill provides instrumentation patterns for monitoring Exa search API calls: latency, result counts, errors, cache hit rates, and result quality.

Strengths

  • Ships a ready-to-adapt `trackedSearch` wrapper that tags every metric by search type and method (search vs searchAndContents)
  • Also addresses result quality through position-based usage tracking and extracted-content value metrics
  • Includes an instrumented cache that exposes hit/miss counters
  • The metrics emitter is deliberately backend-agnostic (Prometheus, Datadog, OpenTelemetry) with commented examples

Limitations

  • The metrics backend and alert rules are not actually configured — they are comments you must implement yourself
  • Assumes a TypeScript Exa integration using the exa-js SDK; no examples in other languages
  • The monitored cache code is truncated in the document (incomplete getStats), so it needs finishing
When to use it

Use it when an Exa integration already runs in production and you need to add metrics, quality tracking, or alerting on top of it.

When not to use it

Avoid it if you do not yet have a working Exa integration, or if you want a fully specified dashboard and alert-threshold configuration out of the box.

Security analysis

Safe
Quality score78/100

The skill is documentation-only with allowed-tools limited to Read, Write, and Edit. All code samples are benign monitoring instrumentation for a legitimate Exa search API client; there are no destructive shell commands, no credential exfiltration (the API key is read from an env var), no base64 payloads, and no attempts to disable safety mechanisms. Network/API calls appear solely in illustrative example code for the user's own application, so listing this poses no meaningful execution risk.

No concerns found

How this analysis is made

Examples

Instrument Exa search latency
Wrap my Exa search client so it emits latency and result-count metrics tagged by search type (neural vs keyword) and by method, using my Datadog statsd client instead of the console emitter.
Track result quality and cache
Add metrics to my Exa integration that track which result positions get used downstream, plus hit/miss counters for a cached search layer.
Alert on Exa errors
Help me instrument my Exa client to track error rates by HTTP status code and daily search volume so I can set up Slack alerts for budget and failures.

name: exa-observability description: | Set up monitoring, metrics, and alerting for Exa search integrations. Use when implementing monitoring for Exa operations, building dashboards, or configuring alerting for search quality and latency. Trigger with phrases like "exa monitoring", "exa metrics", "exa observability", "monitor exa", "exa alerts", "exa dashboard". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io compatible-with: claude-code, codex, openclaw tags: [saas, exa, monitoring, observability]


Exa Observability

Overview

Monitor Exa search API performance, result quality, and cost efficiency. Key metrics: search latency by type (neural ~500-2000ms, keyword ~200-500ms), result count per query, cache hit rates, error rates by status code, and daily search volume for budget tracking.

Prerequisites

  • Exa API integration in production
  • Metrics backend (Prometheus, Datadog, or OpenTelemetry)
  • Alerting system (PagerDuty, Slack, or equivalent)

Instructions

Step 1: Instrument the Exa Client

import Exa from "exa-js";

const exa = new Exa(process.env.EXA_API_KEY);

// Generic metrics emitter (replace with your metrics library)
function emitMetric(name: string, value: number, tags: Record<string, string>) {
  // Prometheus: histogram/counter.observe(value, tags)
  // Datadog: dogstatsd.histogram(name, value, tags)
  // OpenTelemetry: meter.createHistogram(name).record(value, tags)
  console.log(`[metric] ${name}=${value}`, tags);
}

async function trackedSearch(query: string, options: any = {}) {
  const start = performance.now();
  const type = options.type || "auto";
  const hasContents = options.text || options.highlights || options.summary;

  try {
    const method = hasContents ? "searchAndContents" : "search";
    const results = hasContents
      ? await exa.searchAndContents(query, options)
      : await exa.search(query, options);

    const duration = performance.now() - start;

    emitMetric("exa.search.duration_ms", duration, { type, method });
    emitMetric("exa.search.result_count", results.results.length, { type });
    emitMetric("exa.search.success", 1, { type });

    return results;
  } catch (err: any) {
    const duration = performance.now() - start;
    const status = String(err.status || "unknown");

    emitMetric("exa.search.duration_ms", duration, { type, status });
    emitMetric("exa.search.error", 1, { type, status });

    throw err;
  }
}

Step 2: Track Result Quality

// Measure whether search results are actually used downstream
function trackResultUsage(
  searchId: string,
  resultIndex: number,
  action: "clicked" | "used_in_context" | "discarded"
) {
  emitMetric("exa.result.usage", 1, {
    action,
    position: String(resultIndex),
  });
  // Results at position 0-2 should have high usage
  // If top results are discarded, query needs tuning
}

// Track content extraction value
function trackContentValue(result: any) {
  if (result.text) {
    emitMetric("exa.content.text_length", result.text.length, {});
  }
  if (result.highlights) {
    emitMetric("exa.content.highlight_count", result.highlights.length, {});
  }
}

Step 3: Cache Monitoring

class MonitoredCache {
  private hits = 0;
  private misses = 0;
  private cache: Map<string, { data: any; expiry: number }> = new Map();

  async search(exa: Exa, query: string, opts: any) {
    const key = `${query}:${opts.type}:${opts.numResults}`;
    const cached = this.cache.get(key);

    if (cached && cached.expiry > Date.now()) {
      this.hits++;
      emitMetric("exa.cache.hit", 1, {});
      return cached.data;
    }

    this.misses++;
    emitMetric("exa.cache.miss", 1, {});

    const results = await exa.searchAndContents(query, opts);
    this.cache.set(key, { data: results, expiry: Date.now() + 3600 * 1000 });
    return results;
  }

  getStats() {
    const total = this.hits + this.misses;
    return {
      hits: this.hits,
      misses: this.misses,
      hitRate: total > 0 ? `${((this.hits / total) * 100).toFixed(1)}%` : "N/A",
    };
  }
}

Step 4: Prometheus Alert Rules

groups:
  - name: exa_alerts
    rules:
      - alert: ExaHighLatency
        expr: histogram_quantile(0.95, rate(exa_search_duration_ms_bucket[5m])) > 3000
        for: 5m
        annotations:
          summary: "Exa search P95 latency exceeds 3 seconds"

      - alert: ExaHighErrorRate
        expr: rate(exa_search_error[5m]) / rate(exa_search_success[5m]) > 0.05
        for: 5m
        annotations:
          summary: "Exa API error rate exceeds 5%"

      - alert: ExaEmptyResults
        expr: rate(exa_search_result_count{result_count="0"}[15m]) > 0.2
        for: 10m
        annotations:
          summary: "Over 20% of Exa searches returning empty results"

      - alert: ExaCacheHitRateLow
        expr: rate(exa_cache_hit[5m]) / (rate(exa_cache_hit[5m]) + rate(exa_cache_miss[5m])) < 0.3
        for: 15m
        annotations:
          summary: "Exa cache hit rate below 30% — check query patterns"

Step 5: Health Check Endpoint

app.get("/health/exa", async (_req, res) => {
  const start = performance.now();
  try {
    const result = await exa.search("health check", { numResults: 1 });
    const latencyMs = Math.round(performance.now() - start);
    res.json({
      status: "healthy",
      latencyMs,
      resultCount: result.results.length,
    });
  } catch (err: any) {
    res.status(503).json({
      status: "unhealthy",
      error: err.message,
      latencyMs: Math.round(performance.now() - start),
    });
  }
});

Dashboard Panels

| Panel | Metric | Purpose | |-------|--------|---------| | Search Volume | rate(exa.search.success) | Traffic trends | | Latency P50/P95 | histogram_quantile(exa.search.duration_ms) | Performance SLO | | Error Rate | exa.search.error / exa.search.success | Reliability | | Result Quality | exa.result.usage{action="discarded"} | Query tuning signal | | Cache Hit Rate | exa.cache.hit / (hit + miss) | Cost efficiency | | Daily Cost | sum(exa.search.success) | Budget tracking |

Error Handling

| Issue | Cause | Solution | |-------|-------|----------| | 429 Too Many Requests | Rate limit exceeded | Implement backoff + request queue | | Zero results returned | Query too narrow | Broaden query, remove domain filter | | Latency spike to 5s+ | Deep/neural on complex query | Switch to fast or auto type | | Budget exhausted | Uncapped search volume | Add application-level budget tracking |

Resources

Next Steps

For incident response, see exa-incident-runbook. For cost optimization, see exa-cost-tuning.

From the file to a first result

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1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/exa-observability-mrzxur0p/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

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2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

Tools declared by the author (check availability in your agent): Read, Write, Edit

Original excerpt: Prerequisites
  • Exa API integration in production
  • Metrics backend (Prometheus, Datadog, or OpenTelemetry)
  • Alerting system (PagerDuty, Slack, or equivalent)

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/exa-observability-mrzxur0p/SKILL.md' && test -s '.skill-downloads/exa-observability-mrzxur0p/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/exa-observability' && cp -R '.skill-downloads/exa-observability-mrzxur0p' '.claude/skills/exa-observability'

Codex
test -f '.skill-downloads/exa-observability-mrzxur0p/SKILL.md' && test -s '.skill-downloads/exa-observability-mrzxur0p/SKILL.md' && mkdir -p '.agents/skills' && test ! -e '.agents/skills/exa-observability' && cp -R '.skill-downloads/exa-observability-mrzxur0p' '.agents/skills/exa-observability'

OpenSkills

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Official OpenSkills installation
openskills install './.skill-downloads/exa-observability-mrzxur0p'

openskills read 'exa-observability-mrzxur0p'

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openskills sync

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4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/exa-observability-mrzxur0p/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

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