Agent skill · software engineering · jeremylongshore
attio-performance-tuning
Analyze and improve Attio integration latency and throughput from measured query shape, pagination, concurrency, retry, and cache evidence while preserving correctness. Use when Attio requests are slow or queues are backing up. Trigger with "Attio performance", "speed up Attio sync", or "Attio query tuning".
Why this skill is useful
Provides specific caching and batching strategies for optimizing Attio API performance that the AI wouldn't reliably generate on its own.
What it needs
Requires attio account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Attio Query and Sync Performance Overview This skill tunes observed bottlenecks without promising a universal latency target. It keeps query complexity, rate limits, freshness, and mutation safety visible. Prerequisites Latency distributions by endpoint and operation Queue depth, concurrency, retry, and 429 metrics Representative query filters, sorts, and object or list sizes A correctness baseline for synchronized fields Tool Discipline Use Read, Glob, and Grep to inspect query construction, pagination, concurrency, caches, and telemetry. Use WebFetch only for current official Attio documentation. Use Write or Edit after a measured hypothesis and rollback threshold are stated. Current Contract Attio publishes separate global limits for reads and writes and score-based limits for record and entry queries. Complex filters and sorts can increase query score; raw request concurrency is not the only load variable. Pagination may be offset-based or cursor-based depending on the endpoint. Cache object and attribute discovery only with an explicit freshness and invalidation policy. Authentication Use the workload's existing least-privilege Bearer token. Performance measurement does not justify additional scopes or access to broader CRM data. Instructions 1. Measure p50, p95, failure rate, 429 rate, queue age, and retry amplification for a representative window. 2. Segment by endpoint, method, filter/sort shape, page size, and workspace. 3. Verify that each paginator terminates according to its endpoint contract and does not refetch pages. 4. Rank hypotheses such as simpler filters, lower concurrency, bounded caching, change detection, or webhook-assisted work. 5. Change one variable, retain correctness assertions, and compare with the same workload. 6. Roll back when error rate, stale-data risk, or queue age crosses the declared threshold. Approval Boundaries Do not trade away correctness, auditability, or data freshness for latency without owner approval. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/attio-performance-tuning