Systems Lab

Agent skill

clean-job-titles

Standardize verbose LinkedIn job titles into clean, usable formats

activeSelf-containedInstructions only416 words

Filed under LinkedIn and social.

From jurjen-gtm-engineer/gtmskills · 55 skill entries · 0 · pushed 2026-10-04

What it does when it runs

Standardize verbose LinkedIn job titles into clean, usable formats

Automated analysis of the skill and the 0 files bundled beside it. A skill’s own description is written to be selected by an agent, so it describes the job and not the dependencies.

Keys and connectors you must supply
None found.
Hosts it reaches
No third-party host appears in the skill or its bundled files.
Tool permissions it declares
No allowed-tools in the frontmatter. It only issues instructions, so there is nothing to bound.
Actions present in the files
None. Instructions only.

Ask about clean-job-titles

Opens your assistant with this page's verified links already in the prompt.

Is this safe to install?ClaudeChatGPT
Adapt it to my stackClaudeChatGPT
What else do I need for it to workClaudeChatGPT
Rather ask a human? Talk to Cheetah
git clone --depth 1 --filter=blob:none --sparse https://github.com/jurjen-gtm-engineer/gtmskills.git /tmp/gtmskills
git -C /tmp/gtmskills sparse-checkout set "skills/clean-job-titles"
mkdir -p ~/.claude/skills/clean-job-titles
cp -R "/tmp/gtmskills/skills/clean-job-titles/." ~/.claude/skills/clean-job-titles/

Picked up without a restart. A project skill of the same name is shadowed by your personal one. For one repository only, swap ~/.claude/skills for .claude/skills. Claude Code docs ↗

The folder is the same in every client that implements the format — 46 of them — so if yours is not above, only the destination changes.

Reproduced in full from jurjen-gtm-engineer/gtmskills/blob/77dc0b3112dbf6cf906dfc3d526b6f7031bf964c/skills/clean-job-titles/SKILL.md, which is licensed MIT (repository). 416 words, 7 headings.

Clean Job Titles

You are standardizing verbose or complex job titles into clean, usable formats for personalization and segmentation.

Input

User provides:

  • A raw job title (often from LinkedIn)
  • Optionally: multiple titles to clean in batch

Process

  1. Analyze Title Components

    Prompt Pattern:

    For this job title:
    "[RAW TITLE]"
    
    Simplify to standard form by:
    1. Removing geographic qualifiers (", North America", ", EMEA")
    2. Removing company-specific branding ("Chief Evangelist & ...")
    3. Keeping the core role and seniority level
    4. Standardizing common variations
    
  2. Output Format

    Original: [Verbose title]
    Cleaned: [Standardized title]
    
    Components:
    - Seniority: [C-level / VP / Director / Manager / IC]
    - Function: [Sales / Marketing / Engineering / etc.]
    - Specialty: [If applicable]
    
    Segmentation Tags:
    - seniority:[level]
    - function:[area]
    - decision_maker:[yes/no]
    

Examples

Input: "Chief Evangelist & Head of Global Thought Leadership, North America"

Output:

Original: Chief Evangelist & Head of Global Thought Leadership, North America
Cleaned: Chief Evangelist

Components:
- Seniority: C-level equivalent
- Function: Marketing / Thought Leadership
- Specialty: Evangelism, Content

Segmentation Tags:
- seniority:executive
- function:marketing
- decision_maker:yes

Input: "Senior Director of Revenue Operations and Business Systems, Enterprise"

Output:

Original: Senior Director of Revenue Operations and Business Systems, Enterprise
Cleaned: Senior Director of Revenue Operations

Components:
- Seniority: Director
- Function: Revenue Operations
- Specialty: Business Systems

Segmentation Tags:
- seniority:director
- function:revops
- decision_maker:yes

Input: "VP, Head of Sales - SMB & Mid-Market (West Region)"

Output:

Original: VP, Head of Sales - SMB & Mid-Market (West Region)
Cleaned: VP of Sales

Components:
- Seniority: VP
- Function: Sales
- Specialty: SMB/Mid-Market

Segmentation Tags:
- seniority:vp
- function:sales
- decision_maker:yes

Batch Processing

For multiple titles:

| Original | Cleaned | Seniority | Function |
|----------|---------|-----------|----------|
| [Title 1] | [Clean 1] | [Level] | [Area] |
| [Title 2] | [Clean 2] | [Level] | [Area] |

Use Cases

  1. Email Personalization: Use cleaned title in "Hi [Name], as a [Clean Title]..."
  2. Segmentation: Group by seniority or function for campaigns
  3. Routing: Route leads to appropriate sales rep by title
  4. Scoring: Add points based on seniority level

Related Skills

  • /role-focus - Understand what the role focuses on
  • /ideal-customer-profiles - Match cleaned titles to ICP

Examples are illustrative. Company names, prices and numbers in them are placeholders or may be out of date, so check the live source before you rely on any detail.

Other skills for the same job

Different authors, same problem. Matched on the words in the skill name, across every library in the catalogue except this one.

Need help setting it up?

This page tells you what clean-job-titles does and what it needs. Cheetah builds the agent setup it runs inside: data, CRM, sequencing and the guardrails.

Book a call →

The directory stays free. There is nothing gated behind this.