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Prospect list cleanup

Skill spiralcrew-ou/profilespider-agent-skills/prospect-list-cleanup

Normalizes and improves the consistency of prospect-list data. Use when the user provides a messy prospect or lead export and wants standardized records, flagged issues, and a cleanup summary.From its SKILL.md

Install
npx -y skills add spiralcrew-ou/profilespider-agent-skills --skill prospect-list-cleanup

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.0 KB, 345 tokens by cl100k_base, as published. Nobody here has run it

Prospect List Cleanup

Purpose

Normalize and improve the consistency of prospect-list data before outreach or import.

When to use this skill

  • Preparing an export for CRM or outreach import
  • Standardizing names, casing, and formats
  • Flagging incomplete or broken rows
  • Producing a defensible cleanup audit

When not to use this skill

  • You need verified emails or enrichment, not cleanup
  • The file has no consistent columns to normalize
  • You want hard de-duplication (use Duplicate Record Review)

Required inputs

  • A prospect or lead list

Optional inputs

  • A target column schema
  • Formatting conventions (casing, phone format)
  • Fields that are mandatory

Rules

  1. Standardize formatting; do not invent missing values.
  2. Flag incomplete and suspect rows rather than deleting them.
  3. Record every change in normalization notes.
  4. Preserve the original value when flagging an error.
  5. Apply the same conventions to every row.

Process

  1. Profile the columns and detect formats.
  2. Normalize casing, names, companies, and formats.
  3. Flag incomplete and suspected-error rows.
  4. Record normalization notes.
  5. Produce a cleanup summary with counts.

Output format

Return the cleaned list plus a summary with the following fields:

  • standardized_records
  • incomplete_records
  • suspected_errors
  • normalization_notes
  • cleanup_summary

Validation

  • Confirm no values were invented.
  • Confirm flagged rows retain their original data.
  • Confirm the summary counts match the records.

Limitations

  • It standardizes formatting, not factual accuracy.
  • Suspected errors are heuristics and should be reviewed.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most sales audience skills give in 345 tokens

Counted across 401 of the 401 authors here whose files we hold, read 2026-08-07

  • Read product marketing context before asking questionsin 21 of 401, across 11 files
  • Acknowledge competitor strengths honestlyin 18 of 401, across 7 files
  • Start every page with a summaryin 15 of 401, across 4 files
  • Use a single, low-friction call to actionin 15 of 401, across 7 files
  • Create a single source of truth for each competitorin 14 of 401, across 3 files
  • Make each follow-up email add new valuein 11 of 401, across 5 files
  • Cut any sentence that does not drive a replyin 10 of 401, across 4 files
  • Tie personalization directly to the problemin 10 of 401, across 4 files
  • Write paragraph comparisons for each dimensionin 9 of 401, across 3 files
  • Link between related competitor pagesin 9 of 401, across 3 files
  • Keep subject lines short and lowercasein 9 of 401, across 3 files
  • Define ideal customer profile from top customersin 9 of 401, across 3 files

Said here and by no other author read

  • standardize formatting without inventing missing values
  • flag incomplete and suspect rows
  • do not delete incomplete rows
  • record every change in normalization notes
  • preserve original value when flagging an error
  • apply the same conventions to every row

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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