agentleFS
spiralcrew-ouspiralcrew-ou/profilespider-agent-skills/b2b-lead-qualification/SKILL.md

b2b-lead-qualification

Skill9 starsChanged 5 months agoMITView on GitHub

Qualifies and scores B2B prospects against a supplied ideal customer profile. Use when the user provides a lead list, company records, prospect data, or qualification criteria and wants prioritization, fit analysis, or disqualification reasons.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are 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.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use only information present in the supplied dataset or explicitly provided by the user.

    line 51
  2. Distinctive:

    Do not invent missing company, contact, revenue, employee, technology, or location information.

    line 52

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

B2B Lead Qualification

Purpose

Score and classify B2B prospects against a defined ideal customer profile, with reasoning and a recommended next action.

When to use this skill

  • Qualifying exported prospect lists before outreach
  • Prioritizing accounts when you have more leads than capacity
  • Separating high-fit and low-fit companies
  • Documenting why a prospect matches an ICP

When not to use this skill

  • You have no defined ICP or qualification criteria yet
  • You need verified contact details rather than fit scoring
  • The dataset has no company or firmographic fields to score against

Required inputs

  • An ICP definition
  • A prospect or company dataset
  • Qualification criteria

Optional inputs

  • Exclusion criteria
  • Weighted scoring rules
  • Target locations
  • Company size range
  • Required technologies or industries

Rules

  1. Use only information present in the supplied dataset or explicitly provided by the user.
  2. Do not invent missing company, contact, revenue, employee, technology, or location information.
  3. Clearly distinguish known facts from assumptions.
  4. Flag missing information that materially affects the score.
  5. Apply the same scoring framework consistently to every prospect.

Process

  1. Parse the ideal customer profile.
  2. Extract positive qualification criteria.
  3. Extract disqualifying criteria.
  4. Evaluate each prospect against both sets.
  5. Assign a score from 0 to 100.
  6. Explain the score using available evidence.
  7. Recommend the next action.

Output format

Return one record per prospect with the following fields:

  • fit_score
  • fit_level
  • qualification_reason
  • supporting_evidence
  • missing_information
  • recommended_next_action

Validation

  • Confirm every score is supported by evidence from the input.
  • Confirm missing fields are listed rather than guessed.
  • Confirm the same criteria were applied to every record.

Limitations

  • Scores reflect fit, not intent or buying readiness.
  • A high score is not a guarantee of a sale; verify before investing heavily.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.