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Agent skill

Juicebox Performance Tuning

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Optimize Juicebox performance. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check passedBackend & APIs

Install Juicebox Performance Tuning

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill juicebox-performance-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace juicebox-performance-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/juicebox-performance-tuning .claude/skills/juicebox-performance-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
juicebox-performance-tuning
GitHub stars
2.8k
Token cost
~1.3k tokens
SKILL.md length
363 words
Files
1
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Optimize Juicebox performance. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Works in 4 steps: Benchmark cache, batching, and… → Collect aggregate latency, error, and… → Run one bounded canary, halt on scope,… → …
  • Backend & APIs work in your project
  • SKILL.md covers Overview, Caching Strategy, Batch Operations and Connection Pooling, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Juicebox Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Juicebox performance. Trigger: "juicebox performance", "optimize juicebox".

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Designed for Claude Code

It sits in Backend & APIs. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Backend & APIs work in your project

Example prompts

  • “juicebox performance”
  • “optimize juicebox”
  • “/juicebox-performance-tuning”

Requirements

  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Benchmark cache, batching, and pagination changes against synthetic fixtures only; reject live contact export and unapproved destinations.
  2. Collect aggregate latency, error, and quota measurements; verify suppression, data minimization, and contacts_exported=0 before comparison.
  3. Run one bounded canary, halt on scope, policy, quota, or retention drift, and restore the prior tuning configuration if it fails.
  4. Keep only the redacted benchmark receipt and delete test artifacts after the approved window.

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Juicebox Performance Tuning loads about 1.3k tokens when it runs. Until then it costs about 28 tokens; SKILL.md has 363 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~28
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 363 words, ~1,310 tokens.

Download SKILL.mdSave it as .claude/skills/juicebox-performance-tuning/SKILL.md (or your agent's skills folder).
name
juicebox-performance-tuning
description
Optimize Juicebox performance. Trigger: "juicebox performance", "optimize juicebox".
allowed-tools
Read, Write, Edit, Grep
compatibility
Designed for Claude Code
version
1.16.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, recruiting, juicebox

Juicebox Performance Tuning

Overview

Juicebox's AI analysis API handles dataset uploads, analysis queue wait times, and result pagination. Large dataset uploads (100K+ rows) can block the analysis pipeline, while queue contention during peak hours increases wait times. Result sets from broad queries return thousands of profiles requiring efficient pagination. Caching search results, batching enrichment calls, and managing upload chunking reduces end-to-end analysis time by 40-60% and keeps interactive searches responsive.

Caching Strategy

typescript
const cache = new Map<string, { data: any; expiry: number }>();
const TTL = { search: 300_000, profile: 600_000, analysis: 900_000 };

async function cached(key: string, ttlKey: keyof typeof TTL, fn: () => Promise<any>) {
  const entry = cache.get(key);
  if (entry && entry.expiry > Date.now()) return entry.data;
  const data = await fn();
  cache.set(key, { data, expiry: Date.now() + TTL[ttlKey] });
  return data;
}
// Analysis results are expensive — cache 15 min. Searches expire at 5 min.

Batch Operations

typescript
async function enrichBatch(client: any, profileIds: string[], batchSize = 50) {
  const results = [];
  for (let i = 0; i < profileIds.length; i += batchSize) {
    const batch = profileIds.slice(i, i + batchSize);
    const res = await client.enrichBatch({ profile_ids: batch, fields: ['skills_map', 'contact'] });
    results.push(...res.profiles);
    if (i + batchSize < profileIds.length) await new Promise(r => setTimeout(r, 300));
  }
  return results;
}

Connection Pooling

typescript
import { Agent } from 'https';
const agent = new Agent({ keepAlive: true, maxSockets: 8, maxFreeSockets: 4, timeout: 60_000 });
// Longer timeout for dataset uploads and analysis queue responses

Rate Limit Management

typescript
async function withRateLimit(fn: () => Promise<any>): Promise<any> {
  try { return await fn(); }
  catch (err: any) {
    if (err.status === 429) {
      const backoff = parseInt(err.headers?.['retry-after'] || '10') * 1000;
      await new Promise(r => setTimeout(r, backoff));
      return fn();
    }
    throw err;
  }
}

Monitoring

typescript
const metrics = { searches: 0, enrichments: 0, cacheHits: 0, queueWaitMs: 0, errors: 0 };
function track(op: 'search' | 'enrich', startMs: number, cached: boolean) {
  metrics[op === 'search' ? 'searches' : 'enrichments']++;
  metrics.queueWaitMs += Date.now() - startMs;
  if (cached) metrics.cacheHits++;
}

Performance Checklist

  • Use specific filters (location, skills, title) to narrow search scope
  • Cache search results with 5-min TTL to avoid redundant queries
  • Batch profile enrichment in groups of 50 with 300ms delays
  • Chunk large dataset uploads into 10K-row segments
  • Cache analysis results for 15 min (expensive to recompute)
  • Set 60s timeout for upload and analysis endpoints
  • Monitor queue wait times and schedule uploads during off-peak
  • Paginate results with limit=20 and cursor for interactive UIs

Error Handling

IssueCauseFix
Analysis queue timeoutPeak hour contentionSchedule large analyses off-peak, increase client timeout
429 on bulk enrichmentToo many concurrent enrichment callsBatch to 50 profiles with 300ms interval
Upload failure on large datasetPayload exceeds limit or connection dropChunk into 10K-row segments, retry failed chunks
Slow broad searchUnfiltered query returning thousands of resultsAdd location/skills/title filters, set limit=20
Show full SKILL.md (143 more words)Show less

Prerequisites

  • An approved performance baseline, synthetic sandbox fixture, bounded test budget, source/destination allowlists, suppression controls, and a rollback owner.

Instructions

  1. Benchmark cache, batching, and pagination changes against synthetic fixtures only; reject live contact export and unapproved destinations.
  2. Collect aggregate latency, error, and quota measurements; verify suppression, data minimization, and contacts_exported=0 before comparison.
  3. Run one bounded canary, halt on scope, policy, quota, or retention drift, and restore the prior tuning configuration if it fails.
  4. Keep only the redacted benchmark receipt and delete test artifacts after the approved window.

Output

Produce a performance receipt with environment, baseline and aggregate measurements, tuning settings, suppression/no-export assertions, canary outcome, owner approval, retention/deletion proof, and rollback reference. Exclude queries, records, and credentials.

Examples

env=staging; fixture=synthetic; p95_delta=-22%; quota=within-budget; suppression=pass; contacts_exported=0; rollback=available supports an approval decision.

Resources

  • Juicebox API Docs
  • Juicebox Performance Guide

Next Steps

See juicebox-reference-architecture.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/.curated/juicebox-performance-tuning of jeremylongshore/tons-of-skills-marketplace.

Open the folder on GitHubat commit cfae287

Compare with similar skills

Juicebox Performance Tuning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Juicebox Performance Tuning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Juicebox Performance Tuning this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.3kAutomated safety check: PassMIT
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Nestjs Best Practicesrolling-scopes/rsschool-app10k6 repos~1.2kAutomated safety check: PassMIT
Sub2API AdminWei-Shaw/sub2api44k1 repos~717Automated safety check: PassLGPL-3.0
Firecrawl Build Onboardingfirecrawl/firecrawl190k1 repos~1.4kAutomated safety check: NotesISC
Obsidian BasesAtmosphere/atmosphere3.8k22 repos~3.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Juicebox Performance Tuning

What does Juicebox Performance Tuning do?

Optimize Juicebox performance. An agent skill from jeremylongshore/tons-of-skills-marketplace. Juicebox Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Juicebox performance.

When should I use Juicebox Performance Tuning?

Juicebox Performance Tuning fits situations like: backend & APIs work in your project.

How do I install Juicebox Performance Tuning in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill juicebox-performance-tuning -a claude-code`. Or copy the skill folder (skills/.curated/juicebox-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/juicebox-performance-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Juicebox Performance Tuning in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill juicebox-performance-tuning -a codex`. Or copy the skill folder (skills/.curated/juicebox-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/juicebox-performance-tuning in your project. Codex loads it when a task matches its description.

Can I use Juicebox Performance Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill juicebox-performance-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/juicebox-performance-tuning, .gemini/skills/juicebox-performance-tuning, .github/skills/juicebox-performance-tuning and .opencode/skills/juicebox-performance-tuning in your project.

What does Juicebox Performance Tuning need to run?

SKILL.md names no scripts, command-line tools or credentials: Juicebox Performance Tuning is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep. Compatibility (from SKILL.md): Designed for Claude Code.

Does Juicebox Performance Tuning access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Juicebox Performance Tuning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Juicebox Performance Tuning use?

Juicebox Performance Tuning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Juicebox Performance Tuning use?

About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Juicebox Performance Tuning?

Skills that share tags, products or a category with Juicebox Performance Tuning: Configuring Horizon (coollabsio/coolify, 63k stars), Nestjs Best Practices (rolling-scopes/rsschool-app, 10k stars), Sub2API Admin (Wei-Shaw/sub2api, 44k stars) and Firecrawl Build Onboarding (firecrawl/firecrawl, 190k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Juicebox Performance Tuning?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.