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name: bamboohr-performance-tuning description: >- 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". allowed-tools: Read,Glob,Grep,Write,Edit argument-hint: " " version: 1.5.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags: [saas, hr, bamboohr, performance, datasets] model: inherit effort: high compatibility: Designed for Claude Code

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. Increase parallelism only during a measured canary and watch 429 and tail latency.
7. Compare exact record identities and reconciliation counts before and after. Roll back if freshness, completeness, permissions, or memory regresses.

Tool Discipline

Use Read, Glob, and Grep to inspect the request graph and current metrics. Use Write/Edit for approved instrumentation, query changes, and tests. This skill does not authorize a production benchmark or HR-data export.

Approval Boundaries

Require approval before querying production, changing field selection or cache retention, increasing concurrency, or replacing a source endpoint. Performance does not justify silently omitting inactive or future-dated records.

Output

Return baseline and candidate metrics, request-graph delta, selected fields and pagination, cache/concurrency policy, reconciliation proof, canary scope, rollback thresholds, and measured result.

Error Handling

• Faster but incomplete: reject the change and restore the last checkpoint.
• 413: lower page size/field count and remeasure.
• Rising 429 or tail latency: reduce concurrency and open the circuit.
• Schema drift: quarantine the page rather than skipping fields or records.

Examples

• "Make the nightly sync faster" begins with a request-count and reconciliation baseline.
• "Run 100 workers" is replaced by a per-tenant canary with explicit stop thresholds.

Resources

Read official evidence before altering data access.