fireflies-performance-tuning
Optimizes Fireflies.ai API performance through field selection, result caching, and efficient batching.
Install
mkdir -p .claude/skills/fireflies-performance-tuning && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7429" && unzip -o skill.zip -d .claude/skills/fireflies-performance-tuning && rm skill.zipInstalls to .claude/skills/fireflies-performance-tuning
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Optimize Fireflies.ai GraphQL query performance with field selection,Key capabilities
- →Optimize GraphQL queries with field selection
- →Implement LRU caching for immutable transcripts
- →Configure Redis for multi-instance caching
- →Batch transcript operations with rate limit awareness
- →Warm caches using webhook events
How it works
Performance is improved by requesting only necessary fields, caching immutable transcript data in memory or Redis, and using a request queue to respect API rate limits.
Inputs & outputs
When to use fireflies-performance-tuning
- →Optimizing heavy GraphQL query responses
- →Implementing transcript caching to reduce API hits
- →Batching transcript operations within rate limits
- →Reducing API latency for integration tools
About this skill
Fireflies.ai Performance Tuning
Overview
Optimize Fireflies.ai GraphQL API performance. The biggest wins: request only needed fields (transcripts with sentences can be very large), cache immutable transcripts, and batch operations within rate limits.
Prerequisites
FIREFLIES_API_KEYconfigured- Understanding of your access pattern (list vs detail, frequency)
- Optional: Redis or LRU cache library
Instructions
Step 1: Field Selection -- The Biggest Win
Transcript responses with sentences can be enormous. Always request the minimum fields needed.
// BAD: Fetching everything when you only need titles
const HEAVY = `{ transcripts(limit: 50) {
id title date duration sentences { text speaker_name start_time end_time }
summary { overview action_items keywords outline bullet_gist }
analytics { speakers { name duration word_count } }
} }`;
// GOOD: Light query for listing
const LIGHT = `{ transcripts(limit: 50) {
id title date duration organizer_email
} }`;
// GOOD: Full query only when drilling into a specific transcript
const DETAIL = `query($id: String!) { transcript(id: $id) {
id title
sentences { speaker_name text start_time end_time }
summary { overview action_items keywords }
} }`;
Step 2: Cache Transcripts (They Are Immutable)
Once a transcript is processed, its content never changes. Cache aggressively.
import { LRUCache } from "lru-cache";
const transcriptCache = new LRUCache<string, any>({
max: 500,
ttl: 1000 * 60 * 60, // 1 hour -- transcripts are immutable
});
async function getCachedTranscript(id: string) {
const cached = transcriptCache.get(id);
if (cached) return cached;
const data = await firefliesQuery(`
query($id: String!) {
transcript(id: $id) {
id title date duration
speakers { name }
sentences { speaker_name text start_time end_time }
summary { overview action_items keywords }
}
}
`, { id });
transcriptCache.set(id, data.transcript);
return data.transcript;
}
Step 3: Redis Cache for Multi-Instance Deployments
import Redis from "ioredis";
const redis = new Redis(process.env.REDIS_URL!);
const CACHE_TTL = 3600; // 1 hour in seconds
async function getTranscriptCached(id: string) {
const cacheKey = `fireflies:transcript:${id}`;
// Check cache
const cached = await redis.get(cacheKey);
if (cached) return JSON.parse(cached);
// Fetch from API
const data = await firefliesQuery(`
query($id: String!) {
transcript(id: $id) {
id title date duration
sentences { speaker_name text start_time end_time }
summary { overview action_items keywords }
}
}
`, { id });
// Cache the result
await redis.set(cacheKey, JSON.stringify(data.transcript), "EX", CACHE_TTL);
return data.transcript;
}
Step 4: Batch Processing with Rate Limit Awareness
import PQueue from "p-queue";
// Business plan: 60 req/min. Safe rate: 1 req/sec with headroom.
const queue = new PQueue({
concurrency: 1,
interval: 1100,
intervalCap: 1,
});
async function batchFetchTranscripts(ids: string[]) {
console.log(`Fetching ${ids.length} transcripts (rate-limited)...`);
const results = await Promise.all(
ids.map(id => queue.add(() => getCachedTranscript(id)))
);
const cacheHits = ids.filter(id => transcriptCache.has(id)).length;
console.log(`Done. Cache hits: ${cacheHits}/${ids.length}`);
return results;
}
Step 5: Warm Cache on Webhook Events
// When a transcript completes, pre-cache it immediately
async function onWebhookEvent(event: { meetingId: string; eventType: string }) {
if (event.eventType === "Transcription completed") {
// Pre-warm the cache so future reads are instant
await getCachedTranscript(event.meetingId);
console.log(`Pre-cached transcript: ${event.meetingId}`);
}
}
Step 6: Pagination for Large Result Sets
async function getAllTranscripts(batchSize = 50) {
const allTranscripts: any[] = [];
let hasMore = true;
let offset = 0;
while (hasMore) {
const data = await firefliesQuery(`
query($limit: Int, $skip: Int) {
transcripts(limit: $limit, skip: $skip) {
id title date duration
}
}
`, { limit: batchSize, skip: offset });
allTranscripts.push(...data.transcripts);
if (data.transcripts.length < batchSize) {
hasMore = false;
} else {
offset += batchSize;
// Rate limit: wait between pages
await new Promise(r => setTimeout(r, 1100));
}
}
return allTranscripts;
}
Performance Benchmarks
| Optimization | Before | After | Improvement |
|---|---|---|---|
| Field selection (list) | ~2s (with sentences) | ~200ms (metadata only) | 10x |
| LRU cache (detail view) | ~500ms (API call) | <1ms (cache hit) | 500x |
| Batch with queue | Rate limited/errors | Smooth throughput | Reliable |
| Webhook pre-cache | Cold fetch on user visit | Instant from cache | UX improvement |
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Slow list queries | Requesting sentences in list | Use light query without sentences |
| Rate limit 429 | Burst requests | Use PQueue with 1.1s interval |
| Large response OOM | Transcript with 2+ hour meeting | Stream/paginate sentences |
| Stale cache | (Not a real issue -- transcripts are immutable) | N/A |
Output
- Field-optimized GraphQL queries (light list, full detail)
- LRU and Redis caching for immutable transcripts
- Rate-limited batch processor
- Webhook-driven cache warming
Resources
Next Steps
For cost optimization, see fireflies-cost-tuning.
When not to use it
- →Applications requiring real-time transcript updates
- →Environments without persistent storage for caching
Prerequisites
Limitations
- →Transcript responses with sentences can be very large
- →Requires manual cache warming via webhooks
How it compares
Instead of fetching full transcript objects on every request, this method uses selective field queries and caching to reduce latency and API load.
Compared to similar skills
fireflies-performance-tuning side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| fireflies-performance-tuning (this skill) | 1 | 27d | Review | Intermediate |
| deepgram-performance-tuning | 3 | 27d | Review | Intermediate |
| graphql | 6 | 6mo | No flags | Advanced |
| guidewire-sdk-patterns | 2 | 27d | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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