agentleFS
Sign inStart free
Nomanriaz786Nomanriaz786/claude-consulting-team/skills/customer-segmenter/SKILL.md

customer-segmenter

Skill1 starsChanged 22 days agoMITView on GitHub

Builds actionable customer segments when a business needs to choose whom to serve and how.

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 before proposition, positioning or channel decisions when current customer categories are too broad or descriptive.

    line 9
  2. Distinctive:

    Test segments for distinctiveness, measurability, reachability and stability.

    line 22

≈ “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

Customer Segmenter

When to use it

Use before proposition, positioning or channel decisions when current customer categories are too broad or descriptive.

What Claude needs

  • Customer data and research
  • Business context from client-context.md
  • Relevant customer jobs and buying process
  • Geographic, channel and service constraints

Method

  1. Define the segmentation purpose and decisions it must support.
  2. Select variables based on needs, behaviour, context and buying dynamics.
  3. Group customers using observable differences rather than convenient labels.
  4. Profile each segment by job, trigger, barrier, alternative and economics.
  5. Test segments for distinctiveness, measurability, reachability and stability.
  6. Name segments plainly and identify the evidence needed to refine them.

Output format

Provide a segment definition table, concise profiles, distinguishing variables, validation gaps and strategic implications.

Quality checks

  • Segments predict meaningful differences in behaviour or need
  • Each customer can be assigned using stated rules
  • Segment labels avoid stereotypes and unsupported assumptions

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.

Check your setup, free