Agent skill · data analytics · jeremylongshore
palantir-performance-tuning
Tune Foundry transforms, OSDK queries, and Compute Modules from measured bottlenecks and correctness constraints. Use when latency, queue time, memory, throughput, or freshness misses objectives. Trigger with "Palantir performance" or "Foundry build slow".
Why this skill is useful
Adds executable scripts for optimizing Palantir Foundry API performance through caching, batching, and pagination that aren't in public docs.
What it needs
Requires cachetools installed locally. Requires palantir account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Palantir Workload Performance Tuning Overview Identify whether delay comes from build scheduling, compute, data shape, transaction history, query breadth, network concurrency, or interactive replicas. Change one layer at a time and retain output parity and access controls. Prerequisites Name the workload owner, service/data objective, representative window, exact build/query/module, and correctness tolerance. Capture queue time, duration, CPU, memory, input/output volume, retries, pagination, selected properties, and replica behavior. Read references/official-docs.md and confirm required engine features before changing compute. Prepare a sandbox branch or non-production application and a repeatable benchmark. Current Contract Foundry build metrics show requested and observed CPU/memory and can distinguish scheduling from execution constraints. Single-node and Spark engines support different features; Polars is recommended for many production single-node transforms but is not universally interchangeable. Incremental histories can become progressively slow and may require a snapshot build. OSDK query performance depends on filters, selected properties, pagination, links, aggregations, and subscription shape. Instructions 1. Define one user-visible or data objective and reproduce the miss on an exact workload version. 2. Locate the dominant bottleneck using Foundry metrics, build reports, query traces or timings, and module replica evidence. 3. Choose one bounded experiment: engine/resource request, partition/join shape, incremental snapshot, query filter/property selection, pagination, concurrency, or replica configuration. 4. Run the benchmark and compare correctness, access behavior, latency distribution, throughput, queue time, resource use, and failure rate. 5. Promote only a statistically and operationally meaningful improvement, then observe a representative production window. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/palantir-performance-tuning