clay-performance-tuning
Performance tuning for Clay tables to increase speed and hit rates while reducing credit waste.
Install
mkdir -p .claude/skills/clay-performance-tuning && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8154" && unzip -o skill.zip -d .claude/skills/clay-performance-tuning && rm skill.zipInstalls to .claude/skills/clay-performance-tuning
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Optimize Clay table enrichment throughput, reduce processing time, andKey capabilities
- →Order enrichment columns to prioritize fast lookups
- →Apply conditional run rules to save credits
- →Pre-process and normalize input data
- →Limit waterfall depth for faster processing
- →Schedule large imports for off-peak hours
How it works
The skill optimizes performance by reordering enrichment columns, implementing conditional logic to skip unnecessary API calls, and pre-validating data to reduce wasted credits.
Inputs & outputs
When to use clay-performance-tuning
- →Improve enrichment hit rates
- →Order enrichment columns for faster processing
- →Reduce wasted credit spend per row
- →Troubleshoot slow enrichment workflows
About this skill
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 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).
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
| 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 |
Output
- Optimized table with conditional enrichment rules
- Pre-processed input data (30-50% credit savings typical)
- Column order optimized for speed
- Waterfall depth reduced to 2-3 providers
Resources
Next Steps
For cost optimization, see clay-cost-tuning.
When not to use it
- →Scenarios requiring maximum possible hit rate regardless of cost
Prerequisites
Limitations
- →Waterfall depth reduction may result in 5-10% coverage loss
How it compares
It shifts from a default sequential enrichment flow to a performance-tuned model that minimizes latency and credit waste through conditional execution.
Compared to similar skills
clay-performance-tuning side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| clay-performance-tuning (this skill) | 0 | 27d | Review | Intermediate |
| korean-public-data-api | 5 | 9mo | No flags | Intermediate |
| deepgram-performance-tuning | 3 | 27d | Review | Intermediate |
| graphql | 6 | 6mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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