Agent skill · data analytics · jeremylongshore
intercom-performance-tuning
Optimize Intercom API performance with caching, search optimization, and pagination. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Intercom integrations. Trigger with phrases like "intercom performance", "optimize intercom", "intercom latency", "intercom caching", "intercom slow", "intercom pagination".
Why this skill is useful
Adds executable caching and optimization scripts for Intercom API that significantly enhance performance and throughput.
What it needs
Requires intercom-client, redis installed locally. Requires intercom account access. About 4k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Intercom Performance Tuning Overview Optimize Intercom API performance through response caching, efficient search queries, cursor-based pagination, connection pooling, and request batching. Prerequisites intercom-client SDK installed Understanding of Intercom data model Redis or in-memory cache available (optional) Authentication All requests authenticate with an Intercom access token passed as a bearer token. Store it as INTERCOMACCESSTOKEN in the environment and let the SDK read it — never hardcode it: For raw fetch calls, send Authorization: Bearer ${token}. Intercom API Latency Baselines Operation Typical P50 Typical P95 Notes ----------- ------------- ------------- ------- GET /me (health check) 50ms 150ms Lightest endpoint GET /contacts/:id 80ms 200ms Single lookup POST /contacts/search 120ms 400ms Depends on query complexity GET /conversations/:id 100ms 300ms Heavier with parts (up to 500) POST /contacts (create) 150ms 400ms Write operation GET /contacts (list) 100ms 350ms Paginated, 50 per page POST /messages 200ms 500ms Triggers delivery pipeline Instructions Apply these six techniques in order of impact. Each has a complete, copy-pasteable implementation in references/implementation.md; the summaries and the caching skeleton below are enough to follow the workflow at a high level. 1. Response caching — wrap contact/conversation reads in an LRUCache (read-through), and invalidate on update or via webhook so cached data never goes stale. This is the single biggest win for read-heavy integrations. 2. Efficient search queries — push predicates into the AND-combined query and request only the perpage you need (max 150), rather than fetching broadly and filtering client-side. 3. Optimized pagination — stream large result sets with an async generator over cursor pagination (startingAfter) to keep memory flat, and process in fixed-size batches. 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/intercom-performance-tuning