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:
- Bad list — the copy doesn't matter when you're emailing the wrong people
- Unverified emails — bounces burn domain reputation
- 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 emailfirst_name, last_namejob_title OR titlecompany_name OR companycompany_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):
| Average | Grade | Action |
|---|
| 90-100 | A+ / A | Ship it |
| 80-89 | B | Minor fixes, then ship |
| 70-79 | C | Fix top 3 issues first |
| 60-69 | D | Serious cleanup required |
| <60 | F | Don't send. Rebuild the list. |
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.