Hex Performance Tuning
SkillDev tools'Optimize Hex API performance with caching, batching, and connection
Use Hex Performance Tuning in Claude, ChatGPT or Ahel Desktop
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Then ask your AI: use the Hex Performance Tuning skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by jeremylongshore/tons-of-skills-marketplace in skills/.curated/hex-performance-tuning/SKILL.md and read by Ahel’s review.
Latency Benchmarks
| Operation | Typical Duration |
|---|---|
| ListProjects | 200-500ms |
| RunProject (trigger) | 500ms-2s |
| Project execution | 10s-30min (depends on queries) |
| GetRunStatus (poll) | 100-300ms |
Instructions
Cache Project Lists
import { LRUCache } from 'lru-cache';
const projectCache = new LRUCache<string, any>({ max: 50, ttl: 300000 }); // 5 min
async function getCachedProjects(client: HexClient) {
const cached = projectCache.get('projects');
if (cached) return cached;
const projects = await client.listProjects();
projectCache.set('projects', projects);
return projects;
}
Parallel Independent Runs
// Run independent projects in parallel (respecting rate limits)
async function parallelRuns(client: HexClient, configs: Array<{ id: string; params: any }>) {
return Promise.allSettled(
configs.map(c => runWithRetry(client, c.id, c.params))
);
}
Optimize Poll Interval
// Adaptive polling: start fast, slow down
async function adaptivePoll(client: HexClient, projectId: string, runId: string) {
let interval = 2000; // Start at 2s
while (true) {
const status = await client.getRunStatus(projectId, runId);
if (['COMPLETED', 'ERRORED', 'KILLED'].includes(status.status)) return status;
await new Promise(r => setTimeout(r, interval));
interval = Math.min(interval * 1.5, 30000); // Max 30s
}
}
Overview
Tune run latency and throughput using safe sandbox projects and aggregate metrics. A gain is invalid if it expands data scope, compromises output correctness, exceeds quota, or makes rollback impossible.
Prerequisites
- Baseline latency/run metrics, safe fixture revision, approved error budget, and a rollback revision for cache, concurrency, parameters, and retry policy.
Output
Return a tuning receipt with baseline/canary percentile bands, cache/concurrency/parameter revisions, quota/error outcomes, aggregate assertion, owner approval, and rollback reference. Use aggregates only.
Error Handling
Roll back for quota saturation, increased errors, changed output assertion, access drift, or duplicate runs. Do not raise concurrency or cache duration to hide a failing dependency.
Examples
env=sandbox; p95=420ms->310ms; concurrency=2; cache=r4; quota=within-budget; assertions=pass; rollback=perf-r3 documents a safe canary.
Resources
Signals
- GitHub stars
- 3k
- Forks
- 415
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
hex-performance-tuning- Source
- github.com/jeremylongshore/tons-of-skills-marketplace