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Get Started Free →Track: documents indexed per run (total + new + updated + deleted), indexing errors and retries, search API latency, zero-result query rate, stale content age distribution. Trigger: "glean observability", "observability".
.claude/skills/jeremylongshore-glean-observability/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -27% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 42% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -33% | 0% |
Glean aggregates enterprise knowledge across dozens of connectors, making indexing health and search quality the two pillars of observability. Monitor connector sync status to catch stale content before users notice, track search latency to maintain sub-second responses, and measure zero-result rates to identify coverage gaps. Degraded indexing silently erodes search relevance, so proactive alerting is essential.
| Metric | Type | Target | Alert Threshold | |--------|------|--------|-----------------| | Search latency p95 | Histogram | < 400ms | > 1s | | Zero-result query rate | Gauge | < 5% | > 10% | | Documents indexed per run | Counter | Stable +/-5% | Drop > 20% | | Connector sync errors | Counter | 0 | > 3 per hour | | Stale content ratio | Gauge | < 10% | > 25% (>30 days old) | | Indexing throughput | Gauge | > 1000 docs/min | < 500 docs/min |
typescriptasync function trackGleanSearch(query: string, client: GleanClient) { const start = Date.now(); try { const results = await client.search({ query }); const latency = Date.now() - start; metrics.histogram('glean.search.latency', latency); metrics.increment('glean.search.total'); if (results.totalCount === 0) metrics.increment('glean.search.zero_results'); return results; } catch (err) { metrics.increment('glean.search.errors', { error: err.code }); throw err; } }
typescriptasync function gleanHealth(): Promise<Record<string, string>> { const connectors = await gleanAdmin.getConnectorStatus(); const staleRatio = await gleanAdmin.getStaleContentRatio(30); const searchP95 = await metrics.query('glean.search.latency', 'p95', '5m'); return { connectors: connectors.every(c => c.status === 'ok') ? 'healthy' : 'degraded', content_freshness: staleRatio < 0.1 ? 'healthy' : 'stale', search_latency: searchP95 < 400 ? 'healthy' : 'slow', }; }
typescriptconst alerts = [ { metric: 'glean.search.latency_p95', condition: '> 1000ms', window: '10m', severity: 'warning' }, { metric: 'glean.search.zero_result_rate', condition: '> 0.10', window: '1h', severity: 'warning' }, { metric: 'glean.indexing.sync_errors', condition: '> 3', window: '1h', severity: 'critical' }, { metric: 'glean.indexing.doc_count_delta', condition: 'drop > 20%', window: '1d', severity: 'critical' }, ];
typescriptfunction logGleanEvent(event: string, data: Record<string, any>) { console.log(JSON.stringify({ service: 'glean', event, connector: data.connector, doc_count: data.docCount, query: data.query ? data.query.substring(0, 100) : undefined, latency_ms: data.latency, result_count: data.resultCount, timestamp: new Date().toISOString(), })); }
| Signal | Meaning | Action | |--------|---------|--------| | Connector sync failure | Source API down or creds expired | Check connector config, rotate tokens | | Zero-result spike | Missing content or bad query parsing | Audit indexed sources, check synonyms | | Indexing doc count drop | Source deletion or API pagination bug | Compare source counts, review API logs | | Search latency > 1s | Overloaded cluster or complex queries | Check Glean status page, review query patterns |
See glean-incident-runbook.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 15,062 | 12,098 | -20% | 1 | 1 | 0% | 3,426 | 2,667 | -22% | 0 | 0 | — |
case-02 | fail→fail | 47,921 | 12,499 | -74% | 1 | 1 | 0% | 2,122 | 4,159 | +96% | 0 | 0 | — |
case-03 | fail→pass | 18,789 | 11,077 | -41% | 1 | 1 | 0% | 3,393 | 3,134 | -8% | 0 | 0 | — |
case-04 | pass→pass | 18,502 | 10,140 | -45% | 1 | 1 | 0% | 2,721 | 2,175 | -20% | 0 | 0 | — |
case-05 | fail→pass | 11,412 | 2,721 | -76% | 1 | 1 | 0% | 2,230 | 1,636 | -27% | 0 | 0 | — |
case-06 | fail→pass | 16,017 | 2,458 | -85% | 1 | 1 | 0% | 1,138 | 1,612 | +42% | 0 | 0 | — |
case-07 | fail→pass | 11,133 | 2,073 | -81% | 1 | 1 | 0% | 2,189 | 1,463 | -33% | 0 | 0 | — |
case-08 | fail→pass | 14,541 | 6,923 | -52% | 1 | 1 | 0% | 1,875 | 1,504 | -20% | 0 | 0 | — |
case-09 | fail→pass | 7,564 | 6,484 | -14% | 1 | 1 | 0% | 1,438 | 1,361 | -5% | 0 | 0 | — |
case-10 | fail→pass | 9,591 | 1,727 | -82% | 1 | 1 | 0% | 2,015 | 1,395 | -31% | 0 | 0 | — |
case-11 | fail→pass | 6,559 | 6,870 | +5% | 1 | 1 | 0% | 1,406 | 1,503 | +7% | 0 | 0 | — |
case-12 | fail→pass | 14,887 | 7,272 | -51% | 1 | 1 | 0% | 2,541 | 1,577 | -38% | 0 | 0 | — |
case-13 | fail→pass | 17,537 | 7,359 | -58% | 1 | 1 | 0% | 1,810 | 1,489 | -18% | 0 | 0 | — |
case-14 | fail→pass | 28,867 | 7,382 | -74% | 1 | 1 | 0% | 1,666 | 1,464 | -12% | 0 | 0 | — |
case-15 | pass→pass | 24,185 | 18,452 | -24% | 1 | 1 | 0% | 2,913 | 3,103 | +7% | 0 | 0 | — |
case-16 | fail→pass | 15,887 | 1,987 | -87% | 1 | 1 | 0% | 1,662 | 1,311 | -21% | 0 | 0 | — |
case-17 | fail→pass | 10,529 | 7,184 | -32% | 1 | 1 | 0% | 1,590 | 1,408 | -11% | 0 | 0 | — |
case-18 | pass→pass | 13,529 | 3,415 | -75% | 1 | 1 | 0% | 2,177 | 1,652 | -24% | 0 | 0 | — |
case-19 | fail→pass | 40,436 | 2,502 | -94% | 1 | 1 | 0% | 6,235 | 1,519 | -76% | 0 | 0 | — |
case-20 | pass→pass | 17,387 | 20,838 | +20% | 1 | 1 | 0% | 3,137 | 4,125 | +31% | 0 | 0 | — |
case-21 | pass→pass | 14,099 | 7,983 | -43% | 1 | 1 | 0% | 1,722 | 2,523 | +47% | 0 | 0 | — |
case-22 | pass→fail | 22,103 | 27,214 | +23% | 1 | 1 | 0% | 3,681 | 5,783 | +57% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +64 percentage points is the difference between those two pass rates over the 20 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.