复制安装命令
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A model-agnostic agent-skills platform.
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
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.
Version semantics: the release badge is this marketplace's display version. npm packages, including the
ccpiCLI 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.
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.
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."
| Count | Cohort | Reproduce with |
|---|---|---|
| 440 | catalog plugins (catalog-entry cohort) | node scripts/generate-readme-toc.mjs over marketplace.extended.json |
| 2,984 | marketplace-visible skills (distinct) | node -e "import('./scripts/corpus-resolver.mjs').then(m=>console.log(m.resolveCorpus('marketplace-visible').length))" |
| 347 | agent definitions in plugins | git ls-files 'plugins/**' | grep '/agents/.*\.md' |
| 19 | plugin categories | ls -d plugins/*/ |
Across 394 published packages in the claude-code-plugins namespace. Updated daily by GitHub Actions.
| Window | All packages | Established (>30d) |
|---|---|---|
| Last 24 hours | 641 | 641 |
| Last 7 days | 3,272 | 3,272 |
| Last 30 days | 11,794 | 11,794 |
"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:
Last refreshed 2026-09-02T04:55:57.759Z.
Five real questions, five doors — each resolves to a live, generated surface, never a hand-maintained list:
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).
| Category | Plugins | |
|---|---|---|
| 🤖 | AI & Machine Learning | 37 |
| 🎭 | AI Agents & Agency | 9 |
| 🔌 | API Development | 26 |
| 💼 | Business Tools | 6 |
| 👥 | Community | 20 |
| ₿ | Crypto & Web3 | 27 |
| 💾 | Database | 26 |
| 🎨 | Design | 2 |
| 🔧 | DevOps & Infrastructure | 36 |
| 📚 | Examples & Templates | 5 |
| 🧩 | MCP Servers | 17 |
| 📦 | Packages | 5 |
| ⚡ | Performance | 25 |
| ✅ | Productivity | 29 |
| 🎁 | SaaS Skill Packs | 105 |
| 🔐 | Security | 27 |
| ✨ | Skill Enhancers | 9 |
| 🧪 | Testing | 28 |
| 📁 | Analytics | 1 |
Four artifact classes live in this repository, distinguished on sight and never blurred — provenance is a truth requirement here, not a UX nicety:
| Class | What it is | How the reader can tell |
|---|---|---|
| Canonical skill | First-party, harness-free, the source of truth | No .source.json in its plugin directory |
| Generated adapter | A thin, machine-produced harness projection | Lives under a generated path with a "generated — do not edit" header |
| First-party package | An Intent Solutions distribution (npm, cowork zip) | @intentsolutionsio scope, IS-authored license |
| Upstream mirror | Somebody else's work, hosted mirror-by-default | .source.json present — upstream author, license, and pinned commit recorded |
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.
Start with the contribution guide, then the intake and review standards every submission passes through:
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.
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.
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 CodeOptimize 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.
Clay runs enrichment columns left-to-right. Place fast columns first:
| Column Type | Typical Speed | Position |
|---|---|---|
| Company lookup (Clearbit) | ~100ms | First (fastest) |
| Email finder (single provider) | ~200ms | Second |
| Email waterfall (multi-provider) | 1-10s | Middle |
| Claygent AI research | 5-30s | Later |
| HTTP API (outbound call) | Variable | Last |
| AI text generation | 2-5s | After Claygent |
Why order matters: Fast columns populate data that slow columns may need as input (e.g., company name feeds into Claygent research prompt).
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:
// 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, ${stats.deduped} deduped)`);
// Typical result: 30-50% of rows filtered, saving that many credits
Each additional waterfall provider adds 1-5 seconds per row and burns credits if the previous providers already found data:
# Before: 5-provider waterfall (slow, expensive)
# Each provider: ~2 credits, ~2s
# Worst case: 10 credits, 10s per row
waterfall_deep:
providers: [apollo, hunter, prospeo, dropcontact, findymail]
max_time_per_row: "~10s"
max_credits_per_row: 10
# After: 2-provider waterfall (fast, cheap)
# Covers 80%+ of findable emails with 2 providers
waterfall_optimized:
providers: [apollo, hunter]
max_time_per_row: "~4s"
max_credits_per_row: 4
coverage_loss: "~5-10%"
Rule of thumb: Apollo + one backup provider covers 80-85% of findable work emails. Adding more providers gives diminishing returns.
# Table Settings in Clay UI:
table_auto_update: ON # Parent switch: if OFF, nothing auto-runs
column_settings:
company_lookup:
auto_run: ON # Runs on every new row
email_waterfall:
auto_run: ON # Runs on every new row (if condition met)
condition: "ISNOTEMPTY(domain)"
claygent_research:
auto_run: OFF # Manual trigger only (expensive)
crm_push:
auto_run: ON # Auto-push qualified leads
condition: "ICP Score >= 70"
Clay's enrichment providers respond faster during off-peak hours (US nighttime):
// src/clay/scheduler.ts
function shouldProcessNow(rowCount: number): { proceed: boolean; reason: string } {
const hour = new Date().getUTCHours();
const isOffPeak = hour >= 2 && hour <= 8; // 2am-8am UTC
if (rowCount < 100) {
return { proceed: true, reason: 'Small batch — process anytime' };
}
if (rowCount >= 1000 && !isOffPeak) {
return {
proceed: false,
reason: `Large batch (${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' };
}
| Issue | Cause | Solution |
|---|---|---|
| Table stuck processing | Provider rate limit hit | Wait for reset or reduce concurrency |
| Slow enrichment (>10s/row) | Deep waterfall (5+ providers) | Reduce to 2-3 providers |
| Low hit rate (<40%) | Bad input data | Pre-validate and filter before import |
| Credits burning with no results | No conditional run rules | Add "Only run if" conditions to columns |
| Enrichment re-runs on edit | Table auto-update triggered | Turn off auto-update during bulk edits |
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.
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.
For cost optimization, see clay-cost-tuning.
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