Agent skill · software engineering · jeremylongshore
alchemy-performance-tuning
Tune Alchemy-backed reads with measured latency, cache semantics, batching, concurrency, and freshness SLOs. Use when an integration is slow or wasteful. Trigger with "optimize Alchemy performance", "cache Alchemy data", or "reduce Alchemy latency".
Why this skill is useful
Adds executable scripts for caching, batching, and multi-chain fetching that optimize performance for Alchemy SDK usage.
What it needs
Requires npm installed locally. Requires alchemy account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Alchemy Performance and Freshness Tuning Overview Tune Alchemy-backed reads with measured latency, cache semantics, batching, concurrency, and freshness SLOs. This workflow produces a reviewable artifact and negative-path evidence before any live side effect. Prerequisites Current first-party Alchemy documentation for the selected product, chain, feature, client, authentication method, limit, and lifecycle. Named product, application, security, data/privacy, budget, release, and operations owners appropriate to the requested scope. Synthetic or approved non-production fixtures, a credential canary, explicit success criteria, and a tested rollback boundary. Current Contract Performance depends on endpoint family, chain, response size, pagination, account throughput, region, cache state, and application work. There is no universal latency or batch-size guarantee. Optimization must preserve chain context, finality, partial-error semantics, and the product's freshness contract. Authentication Telemetry may include key identifiers, wallet addresses, or request metadata; log only approved low-cardinality fields and never credential-bearing URLs or full user payloads. Instructions 1. Define endpoint-specific latency, completeness, freshness, and cost SLOs plus the user-visible degraded state. 2. Measure an approved baseline by chain, method, payload/page size, cache state, and concurrency; record percentiles rather than a single average. 3. Remove duplicate calls, bound pagination, choose current batch endpoints only where their documented semantics match, and cap concurrency below the shared account budget. 4. Cache immutable block-scoped data longer than head-sensitive data; include chain, method, normalized parameters, block/finality context, and schema version in keys. 5. Propagate partial failures and staleness metadata through caches; never cache a degraded result as complete success. 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/alchemy-performance-tuning