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Get Started Free →Optimize Fathom API performance with caching and batch processing. Trigger with phrases like "fathom performance", "fathom caching", "optimize fathom".
.claude/skills/jeremylongshore-fathom-performance-tuning/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -27% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -18% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 20% | 0% |
Fathom's meeting intelligence API serves transcript downloads, bulk meeting sync, and action item aggregation. Transcript payloads are large (50-500KB each), making bulk sync of historical meetings a major latency bottleneck. The 60 req/min rate limit requires careful batching. Caching immutable transcripts aggressively while keeping action item data fresh reduces download latency by 70% and prevents rate limit errors during bulk operations.
typescriptconst cache = new Map<string, { data: any; expiry: number }>(); const TTL = { transcript: 3_600_000, actionItems: 120_000, meetings: 300_000 }; async function cached(key: string, ttlKey: keyof typeof TTL, fn: () => Promise<any>) { const entry = cache.get(key); if (entry && entry.expiry > Date.now()) return entry.data; const data = await fn(); cache.set(key, { data, expiry: Date.now() + TTL[ttlKey] }); return data; } // Transcripts are immutable — cache 1hr. Action items change — cache 2min.
typescriptasync function syncMeetingsBatch(client: any, ids: string[], batchSize = 50) { const results = []; for (let i = 0; i < ids.length; i += batchSize) { const batch = ids.slice(i, i + batchSize); const res = await Promise.all(batch.map(id => client.getTranscript(id))); results.push(...res); if (i + batchSize < ids.length) await new Promise(r => setTimeout(r, 61_000)); // 60 req/min } return results; }
typescriptimport { Agent } from 'https'; const agent = new Agent({ keepAlive: true, maxSockets: 6, maxFreeSockets: 3, timeout: 45_000 }); // Transcript downloads are large — longer timeout, fewer concurrent sockets
typescriptasync function withFathomRateLimit(fn: () => Promise<any>): Promise<any> { try { return await fn(); } catch (err: any) { if (err.status === 429) { const retryAfter = parseInt(err.headers?.['retry-after'] || '60') * 1000; await new Promise(r => setTimeout(r, retryAfter)); return fn(); } throw err; } }
typescriptconst metrics = { downloads: 0, cacheHits: 0, rateLimits: 0, avgLatencyMs: 0 }; function trackDownload(startMs: number, cached: boolean, rateLimited: boolean) { metrics.downloads++; metrics.avgLatencyMs = (metrics.avgLatencyMs * (metrics.downloads - 1) + (Date.now() - startMs)) / metrics.downloads; if (cached) metrics.cacheHits++; if (rateLimited) metrics.rateLimits++; }
| Issue | Cause | Fix | |-------|-------|-----| | 429 Rate Limited | Exceeded 60 req/min | Parse Retry-After, batch with 61s delay between groups | | Transcript timeout | Large payload on slow connection | Increase timeout to 45s, enable keep-alive | | Stale action items | Cache TTL too aggressive | Reduce action item TTL to 2 min | | Missing transcript | Meeting still processing | Check meeting status before download, retry after 30s | | Partial sync failure | Network interruption mid-batch | Track progress, resume from last successful ID |
See fathom-reference-architecture.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 27,639 | 18,706 | -32% | 1 | 1 | 0% | 4,722 | 5,088 | +8% | 0 | 0 | — |
case-02 | fail→pass | 29,164 | 30,129 | +3% | 1 | 1 | 0% | 4,786 | 5,935 | +24% | 0 | 0 | — |
case-03 | fail→fail | 28,811 | 32,352 | +12% | 1 | 1 | 0% | 4,765 | 6,334 | +33% | 0 | 0 | — |
case-04 | pass→pass | 21,584 | 18,085 | -16% | 1 | 1 | 0% | 3,504 | 4,861 | +39% | 0 | 0 | — |
case-05 | pass→pass | 18,400 | 14,592 | -21% | 1 | 1 | 0% | 3,156 | 4,100 | +30% | 0 | 0 | — |
case-06 | pass→pass | 11,549 | 12,187 | +6% | 1 | 1 | 0% | 2,511 | 3,374 | +34% | 0 | 0 | — |
case-07 | fail→pass | 11,316 | 2,966 | -74% | 1 | 1 | 0% | 2,091 | 1,524 | -27% | 0 | 0 | — |
case-08 | fail→pass | 12,806 | 3,389 | -74% | 1 | 1 | 0% | 1,897 | 1,559 | -18% | 0 | 0 | — |
case-09 | fail→pass | 9,142 | 2,603 | -72% | 1 | 1 | 0% | 1,305 | 1,563 | +20% | 0 | 0 | — |
case-10 | fail→pass | 15,072 | 5,532 | -63% | 1 | 1 | 0% | 2,362 | 1,784 | -24% | 0 | 0 | — |
case-11 | fail→pass | 14,040 | 2,567 | -82% | 1 | 1 | 0% | 2,033 | 1,535 | -24% | 0 | 0 | — |
case-12 | fail→pass | 16,173 | 3,606 | -78% | 1 | 1 | 0% | 1,684 | 1,672 | -1% | 0 | 0 | — |
case-13 | pass→pass | 16,071 | 5,303 | -67% | 1 | 1 | 0% | 2,041 | 1,901 | -7% | 0 | 0 | — |
case-14 | pass→pass | 8,216 | 3,265 | -60% | 1 | 1 | 0% | 1,230 | 1,552 | +26% | 0 | 0 | — |
case-15 | pass→pass | 7,518 | 3,840 | -49% | 1 | 1 | 0% | 1,189 | 1,408 | +18% | 0 | 0 | — |
case-16 | pass→pass | 10,495 | 7,488 | -29% | 1 | 1 | 0% | 1,436 | 2,079 | +45% | 0 | 0 | — |
case-17 | fail→pass | 15,201 | 11,005 | -28% | 1 | 1 | 0% | 1,984 | 2,526 | +27% | 0 | 0 | — |
case-18 | pass→pass | 9,958 | 9,483 | -5% | 1 | 1 | 0% | 1,926 | 2,479 | +29% | 0 | 0 | — |
case-19 | pass→pass | 27,447 | 14,684 | -47% | 1 | 1 | 0% | 2,533 | 3,652 | +44% | 0 | 0 | — |
case-20 | pass→pass | 16,506 | 7,651 | -54% | 1 | 1 | 0% | 2,462 | 2,374 | -4% | 0 | 0 | — |
case-21 | pass→pass | 5,666 | 2,448 | -57% | 1 | 1 | 0% | 988 | 1,448 | +47% | 0 | 0 | — |
case-22 | fail→pass | 14,836 | 10,332 | -30% | 1 | 1 | 0% | 2,550 | 2,837 | +11% | 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. The headline lift of +45 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.