Inputs
Provide these when prompted. The skill asks for anything missing before it runs.
Required
- A lead list
Optional
- Required fields for your use
- Your ICP for fit distribution
- A quality threshold
Agent Skill
Lead List Analysis
Assess the quality, composition, and usefulness of a lead list with next actions.
Version 1.0 Updated June 2026 SKILL.md MIT 6 min read
Overview
What this skill does
Before you spend outreach effort on a list, you need to know if it is any good — how complete it is, what it contains, and what is wrong with it.
This skill profiles the list: an overview, field completeness, how records distribute across segments, quality issues, missing data, and the recommended next actions to make the list outreach-ready.
When to use it
Best used for
- Sanity-checking a list before outreach
- Measuring field completeness and gaps
- Understanding list composition
- Deciding what to fix or enrich next
Know the limits
When not to use this skill
- You want per-record scoring (use Lead Qualification)
- The file has no consistent structure
- You need market-level patterns (use Market Research)
Outputs
A list-quality report with a consistent, inspectable schema.
list_overviewSize and high-level summary.
field_completenessFill rate per field.
segment_distributionHow records spread across segments.
quality_issuesProblems detected in the data.
missing_dataImportant fields that are sparse.
recommended_next_actionsWhat to do to improve the list.
Example
Example
A quick read on a list.
Input
List: 500 leads. Fields: name, title, company, email, source.
Goal: outreach-ready?Output
list_overview: 500 leads, mostly mid-market
field_completeness: email 62%; title 88%; company 99%
segment_distribution: 40% high-fit; 35% medium; 25% low
quality_issues: 18 malformed emails; 30 duplicates
missing_data: emails for 190 records
recommended_next_actions: Dedupe; enrich missing emails; fix formatsThe list is usable but not yet outreach-ready: dedupe, enrich the 38% missing emails, and fix formats before launching.
Setup
How to use the skill
General steps first, then notes for specific clients where verified.
- 1Download the file using the button below, or copy the Markdown.
- 2Place it in a directory named after the skill (e.g. skill-name/).
- 3Make sure the filename stays exactly SKILL.md.
- 4Add any references or assets included with the package.
- 5Load the skill into a compatible agent and provide the required inputs.
Claude Code
- 1Create a folder for the skill and save SKILL.md inside it.
- 2Place the folder where your project's skills are discovered.
- 3Reference the skill when you want it applied to your data.
Other compatible clients
- 1Confirm the client supports the open Agent Skills format.
- 2Load the SKILL.md file as instructed by that client.
- 3If skills are not auto-loaded, paste the Markdown as instructions.
Source
Full SKILL.md source
Read the rendered skill or copy the complete Markdown. The download is generated from this exact source.
Version 1.0 SKILL.md ~2 KB MIT
Lead List Analysis
Purpose
Assess the quality, composition, and usefulness of a lead list with next actions.
When to use this skill
- Sanity-checking a list before outreach
- Measuring field completeness and gaps
- Understanding list composition
- Deciding what to fix or enrich next
When not to use this skill
- You want per-record scoring (use Lead Qualification)
- The file has no consistent structure
- You need market-level patterns (use Market Research)
Required inputs
- A lead list
Optional inputs
- Required fields for your use
- Your ICP for fit distribution
- A quality threshold
Rules
- Report real completeness rates, not estimates.
- Surface concrete quality issues with counts.
- Tie next actions to the issues found.
- Do not invent missing values.
- Keep the analysis structure consistent.
Process
- Profile the list size and fields.
- Compute field completeness.
- Summarize segment distribution.
- Detect quality issues and missing data.
- Recommend next actions.
Output format
Return a list-quality report with the following fields:
- list_overview
- field_completeness
- segment_distribution
- quality_issues
- missing_data
- recommendednextactions
Validation
- Confirm completeness rates match the data.
- Confirm issue counts are accurate.
- Confirm actions address the issues.
Limitations
- Analysis reflects the supplied list only.
- It measures completeness, not factual correctness.
Before you rely on it
Safety and limitations
- Analysis reflects the supplied list only.
- It measures completeness, not factual correctness.
- Review the output before acting on it.
- Do not upload confidential datasets to an external model without authorization.
- Outputs depend on the model and the source data and are not guaranteed to be accurate.
History
Changelog
- v1.0June 2026
- Initial release.
Questions
Agent Skill FAQ
What does the report tell me?
List quality, composition, and coverage gaps — duplicates, missing fields, segment balance — with prioritized next actions to improve the list before you use it.
Does it fix the issues it finds?
No. It diagnoses and recommends. Use Prospect List Cleanup and Duplicate Record Review to act on the findings.
Do I need ProfileSpider to use this skill?
No. The skill works on any compatible data. ProfileSpider is one convenient way to produce that structured input.
Does running this skill send data to ProfileSpider?
No. Downloading or copying the file does not send any data to ProfileSpider. What happens afterward depends on the AI service you load it into.
Are Agent Skills the same as prompts?
No. A skill is a structured, reusable package — task, inputs, rules, process, and output format — so the workflow runs consistently and can be shared, versioned, and edited.