list-quality-scorecard
Skill8 starsChanged 46 days agoMITView on GitHub
Check a lead list before it goes into a sequence. Gives a deliverability readout (firm) and a targeting readout (advisory). Surfaces issues, never blocks. Use before enrolling any list.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- automatewithuday/GTM-Skillstheir own version, first seen Nov 2, 2026
- growthenginenowoslawski/coldoutboundskillstheir own version, first seen Oct 5, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- growthenginenowoslawski/coldoutboundskills★ 759same name
- automatewithuday/GTM-Skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository39 other files it gives its agents.
- account-based-outboundSkill
- apollo-account-auditSkill
- apollo-deliverabilitySkill
- apollo-go-liveSkill
- apollo-icp-builderSkill
- apollo-list-builderSkill
- apollo-multichannelSkill
- apollo-operatorSkill
- apollo-sequence-builderSkill
- apollo-signalsSkill
- business-briefSkill
- campaign-reportingSkill
- experiment-designSkill
- list-quality-scorecardSkill
- operator-contextSkill
- positive-reply-scoringSkill
- re-engagementSkill
- sending-infrastructureSkill
- sequence-reviewerSkill
- weekly-rhythmSkill
- account-based-outboundSkill
- apollo-account-auditSkill
- apollo-deliverabilitySkill
- apollo-go-liveSkill
- apollo-icp-builderSkill
- apollo-list-builderSkill
- apollo-multichannelSkill
- apollo-operatorSkill
- apollo-sequence-builderSkill
- apollo-signalsSkill
- business-briefSkill
- campaign-reportingSkill
- experiment-designSkill
- operator-contextSkill
- positive-reply-scoringSkill
- re-engagementSkill
- sending-infrastructureSkill
- sequence-reviewerSkill
- weekly-rhythmSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Check a lead list before it goes into a sequence, and tell the operator what is worth knowing.
- Very specific: line 21
After apollo-list-builder has searched, enriched, and deduped a list (run it on enriched.csv or scored.csv).
- Distinctive: line 22
Before enrolling anyone in a sequence, as a habit, not a checkpoint.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
List Quality Scorecard (List)
Check a lead list before it goes into a sequence, and tell the operator what is worth knowing. This is advice, not a gate. The operator always decides whether to send.
Apollo lists almost always come back technically clean, so the two things this actually surfaces are: deliverability risks (things that will hurt your sending) and targeting notes (how much of the list matches the ICP you set). Keep those two separate, because they mean different things.
Surface, do not block
Never refuse to proceed. Show what you found, recommend, and then do what the operator asks. Some people deliberately run outside their ICP, or want to test an adjacent segment, and that is a legitimate choice. The scorecard informs that choice, it does not veto it.
- Deliverability risks hurt the operator, so be direct about them: unverified emails bounce, catch-alls are low-intent, duplicates double-send. Strongly recommend fixing these, because they cost domain reputation. Still their call.
- Targeting is a strategic choice, so present it as information: "N% of this list is outside the ICP you set." Not a problem unless they decide it is.
When to use
- After
apollo-list-builderhas searched, enriched, and deduped a list (run it onenriched.csvorscored.csv). - Before enrolling anyone in a sequence, as a habit, not a checkpoint.
- Any time you inherit a list from elsewhere.
What the user sees
Two readouts and a recommendation, in plain language:
Deliverability: clean. 2,591 leads, all emails verified, no duplicates, no catch-alls, clean names. Good to send. Targeting: heads up. About a third (868) are outside the ICP you set: 310 management consulting, 62 real estate, 42 insurance, 32 PR (which your ICP excludes), and others. If that is intentional, send away. If not, tighten the industry filter and re-pull. Recommendation: deliverability is clean, so this is ready to send. The only question is targeting, and that is your call.
Or, when there is a real deliverability problem:
Deliverability: risk. 18% of emails are unverified and 9% are catch-all addresses. These will bounce and drag your domain. I would fix this before sending. Want me to drop the unverified and catch-all rows? Targeting: on-ICP. 94% match. No concerns there.
Lead with the two readouts, name the specifics, recommend, then ask what they want to do.
Background scoring (for the agent)
Score each dimension 0 to 100. Deliverability dimensions decide the deliverability readout; targeting dimensions decide the targeting note. Do not blend them into one number the user has to interpret.
| # | Dimension | Group | Scored on |
| 1 | Email verification coverage | Deliverability | % of emails verified. Unverified equals bounce risk. |
| 2 | Duplicate email rate | Deliverability | 100 at 0% dupes, scaling down. |
| 3 | Catch-all / generic inbox density | Deliverability | 100 minus the % on info@/contact@/sales@ inboxes. |
| 4 | Per-domain concentration | Deliverability | 100 if avg <= 3 per company, lower as it climbs. |
| 5 | Bad-title rate | Deliverability/quality | 100 minus the % of intern/assistant/student/retired titles. |
| 6 | Name quality | Deliverability/quality | % with a real first and last name. |
| 7 | ICP fit | Targeting | % matching the profile's industries, headcount, geo. |
| 8 | Title relevance | Targeting | % of titles matching the ICP titles. |
Deliverability readout: clean if verification is 100% and the other deliverability dimensions are healthy. Otherwise "risk," with the specific offenders named and a recommended fix. This is the one to be firm about.
Targeting readout: just report the ICP-fit and title-relevance percentages, and name the drift (which off-ICP industries, how many). Never phrase it as a failure.
ICP fit is the dimension that lies if you let it. Match strictly against the profile's industries AND its exclusions, not a loose keyword OR. A loose match rates almost anything as a fit. In a real run on a 2,591-row list, a loose keyword match scored it 99% fit, while a strict check found 33% industry drift, including contacts from an industry the ICP explicitly excluded. When a row is ambiguous, judge it against the ICP individually. Slow down on this one, then report it as a note, not a verdict.
Why it matters
The deliverability half protects the operator's domains, which is not free to recover once burned, so it is worth being direct about. The targeting half is just intelligence: it tells the operator what they are actually about to email, so the decision to send (or not) is informed rather than blind.
Common mistakes
- Blocking. The scorecard advises, it never refuses. The operator sends if they want to.
- Treating ICP drift as a defect. It is a choice. Surface it, do not scold it.
- Blending deliverability and targeting into one grade. They are different questions with different owners.
- Showing the user the weight table. They want the two readouts and a recommendation, not the math.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

