Agent skill · software engineering · jeremylongshore
bamboohr-performance-tuning
Improve BambooHR sync throughput with dataset v2 projection, pagination, deterministic ordering, bounded concurrency, and measured caching. Use when an HR pipeline is slow or creates N+1 traffic. Trigger with "BambooHR performance", "BambooHR slow sync", or "optimize BambooHR pipeline".
Why this skill is useful
Adds executable scripts for optimizing BambooHR API performance through caching, batch reports, and incremental sync that aren't in public docs.
What it needs
Requires redis installed locally. Requires bamboohr account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
BambooHR Performance Tuning Overview Optimize from a measured request graph while preserving completeness and privacy. The first objective is usually eliminating per-employee fan-out, not raising concurrency against an unknown tenant limit. Prerequisites The target repository or integration path and the requested operator outcome. The tenant, identity, and data scope only when approved live work is in scope. The current evidence register plus customer-specific permissions and agreements. Current Contract Dataset v2 returns selected fields with links and meta pagination and permits page sizes up to 1000 in the reviewed OpenAPI. Its request uses filter, orderBy, page, and pageSize. Dataset v1 data retrieval and custom-report paths carry deprecation notices and should not anchor new optimizations. Authentication Benchmark with the same auth mode and effective field permissions as production, but only in an approved test tenant or sanitized workload. Different identities can produce different shapes, so never compare results without recording the credential alias and permission set. Instructions 1. Establish baseline p50/p95 latency, request count by operation, page count, records and bytes received, retries, error rate, CPU/memory, destination time, and end-to-end freshness. 2. Draw the request graph. Replace N+1 employee reads with dataset v2 projections where the required fields and semantics are supported. 3. Minimize fields, add deterministic orderBy, and tune pageSize below the documented maximum based on payload, memory, latency, and 413 behavior. 4. Stream or page through data and commit checkpoints only after destination durability. Do not load the complete workforce into memory by default. 5. Cache stable metadata such as dataset/field definitions with a bounded TTL. Do not cache tokens or unrestricted employee payloads in a shared cache. 6. Add per-tenant concurrency control and reuse safe HTTP connections. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/bamboohr-performance-tuning