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clay-performance-tuning

A model-agnostic agent-skills platform.

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项目 README

来源文件:README.md

抓取于 2026年9月2日

Tons of Skills

A model-agnostic agent-skills platform. The canonical layer is harness-free by construction; Claude Code is currently the verified-native harness. Other harnesses remain engineering candidates until their native-path integration is verified; source research alone is never presented as public support.

Release CLI Plugins Skills GitHub Stars skills.sh Sponsor: Kobiton Buy me a monster

ko-fi

Version semantics: the release badge is this marketplace's display version. npm packages, including the ccpi CLI and publishable plugins, retain their own package versions; they are intentionally not expected to equal the display version. The version-surface checker governs the display surfaces without rewriting package semver.

Install

Inside Claude Code, one command installs the whole marketplace:

/plugin marketplace add jeremylongshore/claude-code-plugins

Or use the CLI:

pnpm add -g @intentsolutionsio/ccpi
ccpi install devops-automation-pack

Browse the marketplace · Explore plugins · Download bundles

Killer Skill of the Week — no-ai-slop by Peter Yang

Strip AI slop from any draft — named-pattern edits that keep the writer's real voice

no-ai-slop does two jobs and refuses to fake a third. In Edit mode it makes the minimum effective edit — cutting throat-clearing, weak verbs, and abstract nouns while deliberately preserving the writer's cadence, bluntness, humor, and honest admissions, so a rough draft still sounds like the same person afterward. In Detect mode it names each AI-slop pattern it finds, quotes the offending line, and gives the fix in a few words — and pointedly does NOT score the draft or guess whether an AI wrote it. That restraint is the whole point: AI detectors guess; named patterns are evidence the reader can check. MIT-licensed, single focused skill, actively maintained by Peter Yang.

"AI detectors guess. Named patterns are evidence the user can check." — Peter Yang

Grade: A | Week of July 22, 2026 (W30) | View on GitHub

Previous picks: tonone, mnemos, databricks-pack, kobiton-automate, code-cleanup, web-analytics, token-optimizer, executive-assistant-skills, skill-creator, cursor-pack, crypto-portfolio-tracker. See all at tonsofskills.com.

Scale, labeled

Every number below names the cohort it counts and the command that reproduces it — an unlabeled count is how a corpus ends up with five contradictory answers to "how many skills."

CountCohortReproduce with
440catalog plugins (catalog-entry cohort)node scripts/generate-readme-toc.mjs over marketplace.extended.json
2,984marketplace-visible skills (distinct)node -e "import('./scripts/corpus-resolver.mjs').then(m=>console.log(m.resolveCorpus('marketplace-visible').length))"
347agent definitions in pluginsgit ls-files 'plugins/**' | grep '/agents/.*\.md'
19plugin categoriesls -d plugins/*/

📦 Live npm Downloads

Across 394 published packages in the claude-code-plugins namespace. Updated daily by GitHub Actions.

WindowAll packagesEstablished (>30d)
Last 24 hours641641
Last 7 days3,2723,272
Last 30 days11,79411,794

"Established" excludes packages first published within the last 30 days, so a bulk-publish event doesn't dominate the headline.

Top 10 by last 30 days:

#PackageLast 30d
1@intentsolutionsio/openrouter-pack1,026
2@intentsolutionsio/groq-pack734
3@intentsolutionsio/mistral-pack264
4@intentsolutionsio/databricks-pack216
5@intentsolutionsio/wallet-security-auditor211
6@intentsolutionsio/shopify-pack155
7@intentsolutionsio/openbb-terminal151
8@intentsolutionsio/ccpi139
9@intentsolutionsio/penetration-tester125
10@intentsolutionsio/langchain-py-pack121

Last refreshed 2026-09-02T04:55:57.759Z.

Ways in

Five real questions, five doors — each resolves to a live, generated surface, never a hand-maintained list:

Browse by category

The 19 categories below link into the live marketplace. Plugin counts are the catalog-entry cohort — regenerated from marketplace.extended.json by this generator; the catalog itself lives on tonsofskills.com, never in this file (§ 6A of the platform blueprint).

CategoryPlugins
🤖AI & Machine Learning37
🎭AI Agents & Agency9
🔌API Development26
💼Business Tools6
👥Community20
₿Crypto & Web327
💾Database26
🎨Design2
🔧DevOps & Infrastructure36
📚Examples & Templates5
🧩MCP Servers17
📦Packages5
⚡Performance25
✅Productivity29
🎁SaaS Skill Packs105
🔐Security27
✨Skill Enhancers9
🧪Testing28
📁Analytics1

What the classes mean

Four artifact classes live in this repository, distinguished on sight and never blurred — provenance is a truth requirement here, not a UX nicety:

ClassWhat it isHow the reader can tell
Canonical skillFirst-party, harness-free, the source of truthNo .source.json in its plugin directory
Generated adapterA thin, machine-produced harness projectionLives under a generated path with a "generated — do not edit" header
First-party packageAn Intent Solutions distribution (npm, cowork zip)@intentsolutionsio scope, IS-authored license
Upstream mirrorSomebody else's work, hosted mirror-by-default.source.json present — upstream author, license, and pinned commit recorded

Certification

Not yet certified. The certification program (tiers T0–T4 with retained, hash-matched evidence) is a later epic of the platform blueprint; until its report exists, no artifact on this surface claims a tier. This line is rendered from the absence of certification-report.json — honestly, not cosmetically.

Contribute

Start with the contribution guide, then the intake and review standards every submission passes through:

Governance

Provenance

External plugins are hosted mirror-by-default: the contributor's repository stays the source of truth, every mirrored source is pinned in a content lockfile, and upstream credit — author, license, resolved commit — is recorded in the mirror itself. Improvements flow by upstreaming to the author's repository, never by silently editing the mirror. The full decision record is the external-sync model.

License

MIT for the repository scaffolding and first-party tooling; each plugin carries its own license in its manifest, and mirrored plugins keep their upstream license verbatim.

数据与 AI

中风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 可能需要外部 token、网络权限或第三方服务。
  • 未检测到高风险命令。
  • 扫描发现:2 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/clay-performance-tuning" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/clay-performance-tuning" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/clay-performance-tuning" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/clay-performance-tuning" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/clay-performance-tuning" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: clay-performance-tuning
description: 'Optimize Clay table enrichment throughput, reduce processing time, and
  improve hit rates.

  Use when experiencing slow enrichment, poor email find rates,

  or needing to process large tables efficiently.

  Trigger with phrases like "clay performance", "optimize clay", "clay slow",

  "clay throughput", "clay fast enrichment", "clay batch optimization".

  '
allowed-tools: Read, Write, Edit, Bash(curl:*)
version: 1.14.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- clay
- api
- performance
compatibility: Designed for Claude Code

Clay Performance Tuning

Overview

Optimize Clay table processing speed, enrichment hit rates, and credit efficiency. Clay processes enrichment columns sequentially per row, and each enrichment column makes external API calls. Performance tuning focuses on reducing wasted enrichments, ordering columns optimally, and managing table auto-run behavior.

Prerequisites

  • Clay table with enrichment columns configured
  • Understanding of which providers are in your waterfall
  • Access to Clay table settings and column configuration

Instructions

Step 1: Order Enrichment Columns by Speed

Clay runs enrichment columns left-to-right. Place fast columns first:

Column TypeTypical SpeedPosition
Company lookup (Clearbit)~100msFirst (fastest)
Email finder (single provider)~200msSecond
Email waterfall (multi-provider)1-10sMiddle
Claygent AI research5-30sLater
HTTP API (outbound call)VariableLast
AI text generation2-5sAfter Claygent

Why order matters: Fast columns populate data that slow columns may need as input (e.g., company name feeds into Claygent research prompt).

Step 2: Add Conditional Run Rules

Prevent enrichments from running on rows that won't yield results:

# In Clay column settings > "Only run if" condition:

# Email waterfall: only run if we have enough input data
ISNOTEMPTY(domain) AND ISNOTEMPTY(first_name) AND ISNOTEMPTY(last_name)

# Claygent: only run for high-value prospects
ICP Score >= 60 AND ISNOTEMPTY(Company Name)

# CRM push: only run for enriched, qualified leads
ICP Score >= 70 AND ISNOTEMPTY(Work Email)

This prevents:

  • Waterfall enrichment on rows with missing domains (wasted credits)
  • Claygent research on low-value prospects (expensive AI credits)
  • CRM pushes for incomplete records

Step 3: Optimize Input Data Before Import

// src/clay/pre-process.ts — clean data before sending to Clay
interface RawLead {
  domain?: string;
  email?: string;
  first_name?: string;
  last_name?: string;
}

function preProcessForClay(rows: RawLead[]): {
  ready: RawLead[];
  filtered: { row: RawLead; reason: string }[];
  stats: { total: number; ready: number; filtered: number; deduped: number };
} {
  const personalDomains = new Set([
    'gmail.com', 'yahoo.com', 'hotmail.com', 'outlook.com',
    'icloud.com', 'aol.com', 'protonmail.com', 'mail.com',
  ]);

  const seen = new Set<string>();
  const ready: RawLead[] = [];
  const filtered: { row: RawLead; reason: string }[] = [];
  let deduped = 0;

  for (const row of rows) {
    // Normalize domain
    const domain = row.domain?.toLowerCase().trim().replace(/^(https?:\/\/)?(www\.)?/, '').replace(/\/.*$/, '');

    // Filter invalid
    if (!domain || !domain.includes('.')) {
      filtered.push({ row, reason: 'invalid domain' });
      continue;
    }
    if (personalDomains.has(domain)) {
      filtered.push({ row, reason: 'personal email domain' });
      continue;
    }
    if (!row.first_name?.trim() || !row.last_name?.trim()) {
      filtered.push({ row, reason: 'missing name' });
      continue;
    }

    // Deduplicate
    const key = `${domain}:${row.first_name?.toLowerCase()}:${row.last_name?.toLowerCase()}`;
    if (seen.has(key)) {
      deduped++;
      continue;
    }
    seen.add(key);

    ready.push({ ...row, domain });
  }

  return {
    ready,
    filtered,
    stats: {
      total: rows.length,
      ready: ready.length,
      filtered: filtered.length,
      deduped,
    },
  };
}

// Usage
const { ready, stats } = preProcessForClay(rawLeads);
console.log(`Pre-processing: ${stats.total} total -> ${stats.ready} ready (${stats.filtered} filtered, ${stats.deduped} deduped)`);
// Typical result: 30-50% of rows filtered, saving that many credits

Step 4: Limit Waterfall Depth

Each additional waterfall provider adds 1-5 seconds per row and burns credits if the previous providers already found data:

# Before: 5-provider waterfall (slow, expensive)
# Each provider: ~2 credits, ~2s
# Worst case: 10 credits, 10s per row
waterfall_deep:
  providers: [apollo, hunter, prospeo, dropcontact, findymail]
  max_time_per_row: "~10s"
  max_credits_per_row: 10

# After: 2-provider waterfall (fast, cheap)
# Covers 80%+ of findable emails with 2 providers
waterfall_optimized:
  providers: [apollo, hunter]
  max_time_per_row: "~4s"
  max_credits_per_row: 4
  coverage_loss: "~5-10%"

Rule of thumb: Apollo + one backup provider covers 80-85% of findable work emails. Adding more providers gives diminishing returns.

Step 5: Use Table-Level Auto-Update Controls

# Table Settings in Clay UI:
table_auto_update: ON   # Parent switch: if OFF, nothing auto-runs
column_settings:
  company_lookup:
    auto_run: ON          # Runs on every new row
  email_waterfall:
    auto_run: ON          # Runs on every new row (if condition met)
    condition: "ISNOTEMPTY(domain)"
  claygent_research:
    auto_run: OFF         # Manual trigger only (expensive)
  crm_push:
    auto_run: ON          # Auto-push qualified leads
    condition: "ICP Score >= 70"

Step 6: Schedule Large Imports for Off-Peak

Clay's enrichment providers respond faster during off-peak hours (US nighttime):

// src/clay/scheduler.ts
function shouldProcessNow(rowCount: number): { proceed: boolean; reason: string } {
  const hour = new Date().getUTCHours();
  const isOffPeak = hour >= 2 && hour <= 8; // 2am-8am UTC

  if (rowCount < 100) {
    return { proceed: true, reason: 'Small batch — process anytime' };
  }

  if (rowCount >= 1000 && !isOffPeak) {
    return {
      proceed: false,
      reason: `Large batch (${rowCount} rows). Schedule for 02:00-08:00 UTC for faster provider responses.`,
    };
  }

  return { proceed: true, reason: isOffPeak ? 'Off-peak — optimal time' : 'Medium batch — acceptable' };
}

Error Handling

IssueCauseSolution
Table stuck processingProvider rate limit hitWait for reset or reduce concurrency
Slow enrichment (>10s/row)Deep waterfall (5+ providers)Reduce to 2-3 providers
Low hit rate (<40%)Bad input dataPre-validate and filter before import
Credits burning with no resultsNo conditional run rulesAdd "Only run if" conditions to columns
Enrichment re-runs on editTable auto-update triggeredTurn off auto-update during bulk edits

Output

Produce a reviewed optimization record with table scope, baseline volume and credit use, selected conditions/waterfall, expected and observed throughput, data-quality impact, owner, and rollback threshold. Avoid representing provider or credit-saving estimates as guaranteed results; confirm them with observed workspace telemetry before expanding the rollout.

Examples

In a staging table, add a conditional run for rows with a valid business email and limit the waterfall to two providers, then compare hit rate, cost, and latency to the prior baseline. Keep the former table configuration available; restore it if qualification quality or downstream CRM coverage drops.

Resources

Next Steps

For cost optimization, see clay-cost-tuning.

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