[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FACF5E96HZAH2BP0TP7VTM":3,"skill-same:01M6FACF5E96HZAH2BP0TP7VTM":74},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6FACF5E96HZAH2BP0TP7VTM","exa-observability",null,"Set up monitoring, metrics, and alerting for Exa search integrations.\nUse when implementing monitoring for Exa operations, building dashboards,\nor configuring alerting for search quality and latency.\nTrigger with phrases like \"exa monitoring\", \"exa metrics\",\n\"exa observability\", \"monitor exa\", \"exa alerts\", \"exa dashboard\".","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fexa-observability\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"exa observability","observability","exa","dashboards","io","alerts","up","edit","write","read","integrations","openclaw","exa search","SET","monitoring","alerting","search","metrics","code","dashboard","2026-11-10 10:12:30",false,"---\nname: exa-observability\ndescription: |\n  Set up monitoring, metrics, and alerting for Exa search integrations.\n  Use when implementing monitoring for Exa operations, building dashboards,\n  or configuring alerting for search quality and latency.\n  Trigger with phrases like \"exa monitoring\", \"exa metrics\",\n  \"exa observability\", \"monitor exa\", \"exa alerts\", \"exa dashboard\".\nallowed-tools: Read, Write, Edit\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\ncompatible-with: claude-code, codex, openclaw\ntags: [saas, exa, monitoring, observability]\n\n---\n# Exa Observability\n\n## Overview\nMonitor 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.\n\n## Prerequisites\n- Exa API integration in production\n- Metrics backend (Prometheus, Datadog, or OpenTelemetry)\n- Alerting system (PagerDuty, Slack, or equivalent)\n\n## Instructions\n\n### Step 1: Instrument the Exa Client\n```typescript\nimport Exa from \"exa-js\";\n\nconst exa = new Exa(process.env.EXA_API_KEY);\n\n\u002F\u002F Generic metrics emitter (replace with your metrics library)\nfunction emitMetric(name: string, value: number, tags: Record\u003Cstring, string>) {\n  \u002F\u002F Prometheus: histogram\u002Fcounter.observe(value, tags)\n  \u002F\u002F Datadog: dogstatsd.histogram(name, value, tags)\n  \u002F\u002F OpenTelemetry: meter.createHistogram(name).record(value, tags)\n  console.log(`[metric] ${name}=${value}`, tags);\n}\n\nasync function trackedSearch(query: string, options: any = {}) {\n  const start = performance.now();\n  const type = options.type || \"auto\";\n  const hasContents = options.text || options.highlights || options.summary;\n\n  try {\n    const method = hasContents ? \"searchAndContents\" : \"search\";\n    const results = hasContents\n      ? await exa.searchAndContents(query, options)\n      : await exa.search(query, options);\n\n    const duration = performance.now() - start;\n\n    emitMetric(\"exa.search.duration_ms\", duration, { type, method });\n    emitMetric(\"exa.search.result_count\", results.results.length, { type });\n    emitMetric(\"exa.search.success\", 1, { type });\n\n    return results;\n  } catch (err: any) {\n    const duration = performance.now() - start;\n    const status = String(err.status || \"unknown\");\n\n    emitMetric(\"exa.search.duration_ms\", duration, { type, status });\n    emitMetric(\"exa.search.error\", 1, { type, status });\n\n    throw err;\n  }\n}\n```\n\n### Step 2: Track Result Quality\n```typescript\n\u002F\u002F Measure whether search results are actually used downstream\nfunction trackResultUsage(\n  searchId: string,\n  resultIndex: number,\n  action: \"clicked\" | \"used_in_context\" | \"discarded\"\n) {\n  emitMetric(\"exa.result.usage\", 1, {\n    action,\n    position: String(resultIndex),\n  });\n  \u002F\u002F Results at position 0-2 should have high usage\n  \u002F\u002F If top results are discarded, query needs tuning\n}\n\n\u002F\u002F Track content extraction value\nfunction trackContentValue(result: any) {\n  if (result.text) {\n    emitMetric(\"exa.content.text_length\", result.text.length, {});\n  }\n  if (result.highlights) {\n    emitMetric(\"exa.content.highlight_count\", result.highlights.length, {});\n  }\n}\n```\n\n### Step 3: Cache Monitoring\n```typescript\nclass MonitoredCache {\n  private hits = 0;\n  private misses = 0;\n  private cache: Map\u003Cstring, { data: any; expiry: number }> = new Map();\n\n  async search(exa: Exa, query: string, opts: any) {\n    const key = `${query}:${opts.type}:${opts.numResults}`;\n    const cached = this.cache.get(key);\n\n    if (cached && cached.expiry > Date.now()) {\n      this.hits++;\n      emitMetric(\"exa.cache.hit\", 1, {});\n      return cached.data;\n    }\n\n    this.misses++;\n    emitMetric(\"exa.cache.miss\", 1, {});\n\n    const results = await exa.searchAndContents(query, opts);\n    this.cache.set(key, { data: results, expiry: Date.now() + 3600 * 1000 });\n    return results;\n  }\n\n  getStats() {\n    const total = this.hits + this.misses;\n    return {\n      hits: this.hits,\n      misses: this.misses,\n      hitRate: total > 0 ? `${((this.hits \u002F total) * 100).toFixed(1)}%` : \"N\u002FA\",\n    };\n  }\n}\n```\n\n### Step 4: Prometheus Alert Rules\n```yaml\ngroups:\n  - name: exa_alerts\n    rules:\n      - alert: ExaHighLatency\n        expr: histogram_quantile(0.95, rate(exa_search_duration_ms_bucket[5m])) > 3000\n        for: 5m\n        annotations:\n          summary: \"Exa search P95 latency exceeds 3 seconds\"\n\n      - alert: ExaHighErrorRate\n        expr: rate(exa_search_error[5m]) \u002F rate(exa_search_success[5m]) > 0.05\n        for: 5m\n        annotations:\n          summary: \"Exa API error rate exceeds 5%\"\n\n      - alert: ExaEmptyResults\n        expr: rate(exa_search_result_count{result_count=\"0\"}[15m]) > 0.2\n        for: 10m\n        annotations:\n          summary: \"Over 20% of Exa searches returning empty results\"\n\n      - alert: ExaCacheHitRateLow\n        expr: rate(exa_cache_hit[5m]) \u002F (rate(exa_cache_hit[5m]) + rate(exa_cache_miss[5m])) \u003C 0.3\n        for: 15m\n        annotations:\n          summary: \"Exa cache hit rate below 30% — check query patterns\"\n```\n\n### Step 5: Health Check Endpoint\n```typescript\napp.get(\"\u002Fhealth\u002Fexa\", async (_req, res) => {\n  const start = performance.now();\n  try {\n    const result = await exa.search(\"health check\", { numResults: 1 });\n    const latencyMs = Math.round(performance.now() - start);\n    res.json({\n      status: \"healthy\",\n      latencyMs,\n      resultCount: result.results.length,\n    });\n  } catch (err: any) {\n    res.status(503).json({\n      status: \"unhealthy\",\n      error: err.message,\n      latencyMs: Math.round(performance.now() - start),\n    });\n  }\n});\n```\n\n## Dashboard Panels\n\n| Panel | Metric | Purpose |\n|-------|--------|---------|\n| Search Volume | `rate(exa.search.success)` | Traffic trends |\n| Latency P50\u002FP95 | `histogram_quantile(exa.search.duration_ms)` | Performance SLO |\n| Error Rate | `exa.search.error \u002F exa.search.success` | Reliability |\n| Result Quality | `exa.result.usage{action=\"discarded\"}` | Query tuning signal |\n| Cache Hit Rate | `exa.cache.hit \u002F (hit + miss)` | Cost efficiency |\n| Daily Cost | `sum(exa.search.success)` | Budget tracking |\n\n## Error Handling\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| `429 Too Many Requests` | Rate limit exceeded | Implement backoff + request queue |\n| Zero results returned | Query too narrow | Broaden query, remove domain filter |\n| Latency spike to 5s+ | Deep\u002Fneural on complex query | Switch to `fast` or `auto` type |\n| Budget exhausted | Uncapped search volume | Add application-level budget tracking |\n\n## Resources\n- [Exa API Documentation](https:\u002F\u002Fdocs.exa.ai)\n- [Exa Rate Limits](https:\u002F\u002Fdocs.exa.ai\u002Freference\u002Frate-limits)\n- [Prometheus Alerting Rules](https:\u002F\u002Fprometheus.io\u002Fdocs\u002Fprometheus\u002Flatest\u002Fconfiguration\u002Falerting_rules\u002F)\n\n## Next Steps\nFor incident response, see `exa-incident-runbook`. For cost optimization, see `exa-cost-tuning`.\n\n","# Exa Observability\n\n## Overview\nMonitor 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.\n\n## Prerequisites\n- Exa API integration in production\n- Metrics backend (Prometheus, Datadog, or OpenTelemetry)\n- Alerting system (PagerDuty, Slack, or equivalent)\n\n## Instructions\n\n### Step 1: Instrument the Exa Client\n```typescript\nimport Exa from \"exa-js\";\n\nconst exa = new Exa(process.env.EXA_API_KEY);\n\n\u002F\u002F Generic metrics emitter (replace with your metrics library)\nfunction emitMetric(name: string, value: number, tags: Record\u003Cstring, string>) {\n  \u002F\u002F Prometheus: histogram\u002Fcounter.observe(value, tags)\n  \u002F\u002F Datadog: dogstatsd.histogram(name, value, tags)\n  \u002F\u002F OpenTelemetry: meter.createHistogram(name).record(value, tags)\n  console.log(`[metric] ${name}=${value}`, tags);\n}\n\nasync function trackedSearch(query: string, options: any = {}) {\n  const start = performance.now();\n  const type = options.type || \"auto\";\n  const hasContents = options.text || options.highlights || options.summary;\n\n  try {\n    const method = hasContents ? \"searchAndContents\" : \"search\";\n    const results = hasContents\n      ? await exa.searchAndContents(query, options)\n      : await exa.search(query, options);\n\n    const duration = performance.now() - start;\n\n    emitMetric(\"exa.search.duration_ms\", duration, { type, method });\n    emitMetric(\"exa.search.result_count\", results.results.length, { type });\n    emitMetric(\"exa.search.success\", 1, { type });\n\n    return results;\n  } catch (err: any) {\n    const duration = performance.now() - start;\n    const status = String(err.status || \"unknown\");\n\n    emitMetric(\"exa.search.duration_ms\", duration, { type, status });\n    emitMetric(\"exa.search.error\", 1, { type, status });\n\n    throw err;\n  }\n}\n```\n\n### Step 2: Track Result Quality\n```typescript\n\u002F\u002F Measure whether search results are actually used downstream\nfunction trackResultUsage(\n  searchId: string,\n  resultIndex: number,\n  action: \"clicked\" | \"used_in_context\" | \"discarded\"\n) {\n  emitMetric(\"exa.result.usage\", 1, {\n    action,\n    position: String(resultIndex),\n  });\n  \u002F\u002F Results at position 0-2 should have high usage\n  \u002F\u002F If top results are discarded, query needs tuning\n}\n\n\u002F\u002F Track content extraction value\nfunction trackContentValue(result: any) {\n  if (result.text) {\n    emitMetric(\"exa.content.text_length\", result.text.length, {});\n  }\n  if (result.highlights) {\n    emitMetric(\"exa.content.highlight_count\", result.highlights.length, {});\n  }\n}\n```\n\n### Step 3: Cache Monitoring\n```typescript\nclass MonitoredCache {\n  private hits = 0;\n  private misses = 0;\n  private cache: Map\u003Cstring, { data: any; expiry: number }> = new Map();\n\n  async search(exa: Exa, query: string, opts: any) {\n    const key = `${query}:${opts.type}:${opts.numResults}`;\n    const cached = this.cache.get(key);\n\n    if (cached && cached.expiry > Date.now()) {\n      this.hits++;\n      emitMetric(\"exa.cache.hit\", 1, {});\n      return cached.data;\n    }\n\n    this.misses++;\n    emitMetric(\"exa.cache.miss\", 1, {});\n\n    const results = await exa.searchAndContents(query, opts);\n    this.cache.set(key, { data: results, expiry: Date.now() + 3600 * 1000 });\n    return results;\n  }\n\n  getStats() {\n    const total = this.hits + this.misses;\n    return {\n      hits: this.hits,\n      misses: this.misses,\n      hitRate: total > 0 ? `${((this.hits \u002F total) * 100).toFixed(1)}%` : \"N\u002FA\",\n    };\n  }\n}\n```\n\n### Step 4: Prometheus Alert Rules\n```yaml\ngroups:\n  - name: exa_alerts\n    rules:\n      - alert: ExaHighLatency\n        expr: histogram_quantile(0.95, rate(exa_search_duration_ms_bucket[5m])) > 3000\n        for: 5m\n        annotations:\n          summary: \"Exa search P95 latency exceeds 3 seconds\"\n\n      - alert: ExaHighErrorRate\n        expr: rate(exa_search_error[5m]) \u002F rate(exa_search_success[5m]) > 0.05\n        for: 5m\n        annotations:\n          summary: \"Exa API error rate exceeds 5%\"\n\n      - alert: ExaEmptyResults\n        expr: rate(exa_search_result_count{result_count=\"0\"}[15m]) > 0.2\n        for: 10m\n        annotations:\n          summary: \"Over 20% of Exa searches returning empty results\"\n\n      - alert: ExaCacheHitRateLow\n        expr: rate(exa_cache_hit[5m]) \u002F (rate(exa_cache_hit[5m]) + rate(exa_cache_miss[5m])) \u003C 0.3\n        for: 15m\n        annotations:\n          summary: \"Exa cache hit rate below 30% — check query patterns\"\n```\n\n### Step 5: Health Check Endpoint\n```typescript\napp.get(\"\u002Fhealth\u002Fexa\", async (_req, res) => {\n  const start = performance.now();\n  try {\n    const result = await exa.search(\"health check\", { numResults: 1 });\n    const latencyMs = Math.round(performance.now() - start);\n    res.json({\n      status: \"healthy\",\n      latencyMs,\n      resultCount: result.results.length,\n    });\n  } catch (err: any) {\n    res.status(503).json({\n      status: \"unhealthy\",\n      error: err.message,\n      latencyMs: Math.round(performance.now() - start),\n    });\n  }\n});\n```\n\n## Dashboard Panels\n\n| Panel | Metric | Purpose |\n|-------|--------|---------|\n| Search Volume | `rate(exa.search.success)` | Traffic trends |\n| Latency P50\u002FP95 | `histogram_quantile(exa.search.duration_ms)` | Performance SLO |\n| Error Rate | `exa.search.error \u002F exa.search.success` | Reliability |\n| Result Quality | `exa.result.usage{action=\"discarded\"}` | Query tuning signal |\n| Cache Hit Rate | `exa.cache.hit \u002F (hit + miss)` | Cost efficiency |\n| Daily Cost | `sum(exa.search.success)` | Budget tracking |\n\n## Error Handling\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| `429 Too Many Requests` | Rate limit exceeded | Implement backoff + request queue |\n| Zero results returned | Query too narrow | Broaden query, remove domain filter |\n| Latency spike to 5s+ | Deep\u002Fneural on complex query | Switch to `fast` or `auto` type |\n| Budget exhausted | Uncapped search volume | Add application-level budget tracking |\n\n## Resources\n- [Exa API Documentation](https:\u002F\u002Fdocs.exa.ai)\n- [Exa Rate Limits](https:\u002F\u002Fdocs.exa.ai\u002Freference\u002Frate-limits)\n- [Prometheus Alerting Rules](https:\u002F\u002Fprometheus.io\u002Fdocs\u002Fprometheus\u002Flatest\u002Fconfiguration\u002Falerting_rules\u002F)\n\n## Next Steps\nFor incident response, see `exa-incident-runbook`. For cost optimization, see `exa-cost-tuning`.\n\n",{"name":5,"tags":44,"author":46,"license":15,"version":47,"description":48,"allowed-tools":49,"compatible-with":50},[45,21,33,20],"saas","Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Set up monitoring, metrics, and alerting for Exa search integrations.\nUse when implementing monitoring for Exa operations, building dashboards,\nor configuring alerting for search quality and latency.\nTrigger with phrases like \"exa monitoring\", \"exa metrics\",\n\"exa observability\", \"monitor exa\", \"exa alerts\", \"exa dashboard\".\n","Read, Write, Edit","claude-code, codex, openclaw","main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fexa-observability",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 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