Agent skill · software engineering · jeremylongshore
flyio-performance-tuning
Analyze and tune Fly.io placement, VM resources, concurrency, autostart behavior, networking, and data locality from measured service objectives. Use when latency or saturation is unacceptable. Trigger with: "speed up Fly app", "tune Fly concurrency", "reduce Fly cold starts".
Why this skill is useful
Provides specific Fly.io performance tuning commands and configurations that the AI wouldn't reliably generate on its own.
What it needs
Requires fly installed locally. Requires fly.io account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Fly.io Performance and Placement Tuning Overview Treat performance as an end-to-end path through Anycast routing, Fly Proxy, Machine placement, process concurrency, VM resources, autostart, and data locality. Optimize from percentiles and saturation evidence rather than assuming more regions or larger VMs always help. Prerequisites Latency, throughput, error, availability, and cold-start objectives Per-region request, CPU, memory, concurrency, restart, and dependency latency evidence Current image, VM sizes, Machine counts, service concurrency, autostop, and database placement Instructions Step 1: Establish a representative baseline Measure regional latency percentiles, errors, throughput, concurrency, CPU, memory, restarts, health transitions, cold starts, and dependency timing. Step 2: Locate the bottleneck Separate network distance, queueing, application work, CPU throttling, memory pressure, startup, connection setup, database latency, and capacity placement. Step 3: Tune concurrency with capacity Align service soft and hard limits with measured per-Machine capacity. Preserve headroom so Fly Proxy can route around unhealthy or saturated Machines. Step 4: Tune lifecycle behavior Compare always-running, stopped, and suspended behavior. Autostart can reduce idle cost but adds startup delay; set minimum running capacity from the service objective. Step 5: Align placement with state Add or remove regions based on user and dependency latency, available capacity, residency, and data topology. Keep region-bound volumes and database writes explicit. Step 6: Change one variable and verify Canary a single resource, count, concurrency, lifecycle, or placement adjustment; compare the same observation window and roll back on regression. Authentication Use read-only access for metrics, health, state, and placement analysis. Resource or scaling changes require a deploy-capable scoped identity and explicit approval. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/flyio-performance-tuning