List Quality Scorecard

A CSV of 5,000 leads is not the same as a good list of 5,000 leads. This skill grades your list across 8 dimensions BEFORE you send, catching preventable waste.

Why this exists

The three campaign failure modes cold email runners hit most:

1. Bad list — the copy doesn't matter when you're emailing the wrong people
2. Unverified emails — bounces burn domain reputation
3. ICP drift — you think you're targeting VPs, but the list is mostly Managers

Each of these is catchable before you send, in 5 minutes, for free.

Inputs

A CSV with at minimum these columns:

• email — the primary email
• first_name, last_name
• job_title OR title
• company_name OR company
• company_domain (optional, derived from email if missing)
• company_industry (optional, used for ICP fit scoring)
• company_headcount (optional, used for ICP fit scoring)

Output

A markdown scorecard with:

• Letter grade (A+ to F)
• 8 dimension scores (each 0-100)
• Top 5 issues to fix
• Pre-send checklist

Usage

npx tsx scripts/score-list.ts --list=leads.csv --icp-file=client-profile.yaml --out=scorecard.md

Optional --icp-file lets the scorecard compare your list against your declared ICP filters (from /icp-onboarding).

The 8 dimensions

1. Email verification coverage (critical)

• What: % of emails validated via MillionVerifier or equivalent
• Rule: 100% of a cold list must be verified before sending. Unverified emails = bounces = dead domains.
• Score: 100 if all verified, 0 if <50% verified

2. Duplicate email rate

• What: % of duplicate emails in the list
• Rule: <1% acceptable, >5% is a problem
• Score: 100 at 0%, drops linearly

3. Duplicate domain rate

• What: max # of leads from any single domain
• Rule: 1-2 leads per domain ideal. 5+ suggests you're over-indexing on one company.
• Score: 100 if avg <2 per domain, 60 if avg 2-5, 30 if >5

4. Title relevance

• What: % of titles matching your ICP's job title list
• Rule: Exact-match + synonym list. If 40% of your "VP Sales" list is actually "Sales Manager", you have drift.
• Score: 100 if ≥80% match, 50 if 40-80%, 0 if <40%

5. Bad-title detection

• What: % of titles matching known-bad patterns
• Bad patterns: intern, assistant, coordinator, student, part-time, retired, non-English titles when targeting US
• Rule: <2% is normal, >10% means your Prospeo filter is too loose
• Score: 100 if <2%, drops sharply after

6. Catch-all domain density

• What: % of emails on catch-all domains (e.g., info@, contact@, hello@)
• Rule: <5% acceptable for B2B outbound
• Score: 100 if <5%, 50 at 5-15%, 0 if >15%

7. ICP fit

• What: % of leads matching your client-profile.yaml filters on industry + headcount
• Requires: --icp-file passed
• Rule: 80%+ match, 100 if exact

8. Name quality

• What: % with both first_name AND last_name populated AND looking human
• Checks: Not all-caps, not fake names ("Admin", "Info"), not email-as-name
• Rule: 95%+ acceptable
• Score: 100 if 95%+, drops linearly

Letter grade mapping

Weighted average across 8 dimensions (verification and ICP fit weighted 2x):

Example output

=== List Quality Scorecard ===

File: leads.csv (2,147 rows)
Grade: B (84/100)

Dimensions:
1. Email verification:    100/100  (100% verified, good)
2. Duplicate emails:       95/100  (1.1% duplicates — trim before send)
3. Duplicate domains:      78/100  (avg 2.4 per domain — some over-concentration)
4. Title relevance:        82/100  (85% titles match "VP Sales" / "Head of Sales")
5. Bad-title detection:    92/100  (3% Coordinators slipped in — filter)
6. Catch-all density:      80/100  (8% catch-all — consider dropping)
7. ICP fit:                88/100  (88% match declared industry filter)
8. Name quality:           97/100  (good)

Top 5 issues to fix:
1. 23 emails are duplicates (1.1%) — deduplicate before upload
2. 64 leads are on catch-all addresses (3.0%) — drop or deprioritize
3. 64 Coordinators in the list — filter by seniority ≥ Manager
4. 147 leads cluster on 12 domains (>5 each) — cap at 3 per domain
5. 258 leads outside declared industry filter (12%) — filter by company_industry

Pre-send checklist:
[ ] Deduplicate by email
[ ] Drop catch-all if >5% (reduces bounce rate)
[ ] Filter out bad titles
[ ] Cap per-domain concentration
[ ] Re-run verifier if list shrunk >10%

When to use

• AFTER list-building skills (/prospeo-full-export, /blitz-list-builder, /google-maps-list-builder, /disco-like)
• AFTER email finding (/list-builder Phase 5)
• BEFORE Smartlead upload

When NOT to use

• On a list of <100. Sample too small for reliable stats.
• On a fully static list (same every send). Check once, reuse.

Scripts

• scripts/score-list.ts — the main scorecard

What to do next

If grade ≥ B: /campaign-copywriting to write the emails. Then /smartlead-campaign-upload-public to launch as DRAFT.

If grade < C: fix the top 3 issues (from scorecard output), re-run this skill until grade ≥ B. Don't upload a C-grade list — bounces and low reply rates will damage domain reputation.

Or wait: if large fixes are needed (missing email verification, 30%+ bad titles), address those BEFORE spending more on email-finding or enrichment.

Related skills

• /icp-prompt-builder — more surgical ICP fit scoring (AI per-company)
• /icp-onboarding — produces the client-profile.yaml this skill checks against
• /list-builder Phase 5 + MillionVerifier (/cold-email-starter-kit) — run BEFORE this skill for verification coverage

The 1% rule alignment

A list that scores below C-grade is very likely to produce reply rates below 1%, which violates the 1% rule (see /email-deliverability-audit). Catching list issues here saves you the deliverability hangover later.