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.
In this repository23 other files it gives its agents.
- account-prioritizationSkill
- candidate-qualificationSkill
- company-data-normalizationSkill
- company-researchSkill
- company-research-workflow-plannerSkill
- company-segmentationSkill
- competitor-researchSkill
- directory-research-plannerSkill
- duplicate-record-reviewSkill
- job-title-normalizationSkill
- lead-list-analysisSkill
- lead-list-segmentationSkill
- lead-research-workflow-plannerSkill
- market-researchSkill
- outreach-brief-generatorSkill
- partner-qualificationSkill
- persona-classificationSkill
- person-researchSkill
- prospect-list-cleanupSkill
- recruiting-sourcing-plannerSkill
- research-gap-analysisSkill
- supplier-qualificationSkill
- territory-segmentationSkill
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
- Distinctive: line 51
Use only information present in the supplied dataset or explicitly provided by the user.
- Distinctive: line 52
Do not invent missing company, contact, revenue, employee, technology, or location information.
≈ “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
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
- Use only information present in the supplied dataset or explicitly provided by the user.
- Do not invent missing company, contact, revenue, employee, technology, or location information.
- Clearly distinguish known facts from assumptions.
- Flag missing information that materially affects the score.
- Apply the same scoring framework consistently to every prospect.
Process
- Parse the ideal customer profile.
- Extract positive qualification criteria.
- Extract disqualifying criteria.
- Evaluate each prospect against both sets.
- Assign a score from 0 to 100.
- Explain the score using available evidence.
- 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.
Does it work?
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